Articles | Volume 24, issue 7
https://doi.org/10.5194/hess-24-3431-2020
© Author(s) 2020. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/hess-24-3431-2020
© Author(s) 2020. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Research article
| 
07 Jul 2020
Research article |
| 07 Jul 2020
| 07 Jul 2020
Assimilation of vegetation optical depth retrievals from passive microwave radiometry
Sujay V. Kumar
CORRESPONDING AUTHOR
Hydrological Sciences Laboratory, NASA Goddard Space Flight Center, Greenbelt, MD, USA
Thomas R. Holmes
Hydrological Sciences Laboratory, NASA Goddard Space Flight Center, Greenbelt, MD, USA
Rajat Bindlish
Hydrological Sciences Laboratory, NASA Goddard Space Flight Center, Greenbelt, MD, USA
Richard de Jeu
Vandersat, Haarlem, the Netherlands
Christa Peters-Lidard
Hydrosphere, Biosphere, and Geophysics, Earth Sciences Division at NASA GSFC, Greenbelt, MD, USA
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To manage Earth's water resources effectively amid climate change, it's crucial to understand both surface and groundwater processes. We developed a new modeling system that combines two advanced tools, ParFlow and LIS/Noah-MP, to better simulate both land surface and groundwater interactions. By testing this integrated model in the Upper Colorado River Basin, we found it improves predictions of hydrologic processes, especially in complex terrains.
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This study estimates irrigation in the Po Valley using AquaCrop and Noah-MP models with sprinkler irrigation. Noah-MP shows higher annual rates than AquaCrop due to more water losses. After adjusting, both align with reported irrigation ranges (500–600 mm/yr). Soil moisture estimates from both models match satellite data, though both have limitations in vegetation and evapotranspiration modeling. The study emphasizes the need for observations to improve irrigation estimates.
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Estimates of 250 m of snow water equivalent in the western USA and Canada are improved by assimilating observations representative of a snow-focused satellite mission with a land surface model. Here, by including a gap-filling strategy, snow estimates could be improved in forested regions where remote sensing is challenging. This approach improved estimates of winter maximum snow water volume to within 4 %, on average, with persistent improvements to both spring snow and runoff in many regions.
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While land surface models are a common approach for estimating macroscale snow water equivalent (SWE), the SWE accuracy is often limited by uncertainties in model physics and forcing inputs. In this study, we found large underestimations of modeled SWE compared to observations. Precipitation forcings and melting physics limitations dominantly contribute to the SWE underestimations. Results provide insights into prioritizing strategies to improve the SWE simulations for hydrologic applications.
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Earth Syst. Sci. Data, 14, 3115–3135, https://doi.org/10.5194/essd-14-3115-2022, https://doi.org/10.5194/essd-14-3115-2022, 2022
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The Famine Early Warning Systems Network (FEWS NET) Land Data Assimilation System (FLDAS) global and Central Asia data streams described here generate routine estimates of snow, soil moisture, runoff, and other variables useful for tracking water availability. These data are hosted by NASA and USGS data portals for public use.
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The MENA (Middle East and North Africa) region faces significant food and water insecurity and hydrological hazards. Here we investigate the value of assimilating remote sensing data sets into an Earth system model to help build an effective drought monitoring system and support risk mitigation and management by countries in the region. We highlight incorporating satellite-informed vegetation conditions into the model as being one of the key processes for a successful application for the region.
Jawairia A. Ahmad, Barton A. Forman, and Sujay V. Kumar
Hydrol. Earth Syst. Sci., 26, 2221–2243, https://doi.org/10.5194/hess-26-2221-2022, https://doi.org/10.5194/hess-26-2221-2022, 2022
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Assimilation of remotely sensed data into a land surface model to improve the spatiotemporal estimation of soil moisture across South Asia exhibits potential. Satellite retrieval assimilation corrects biases that are generated due to an unmodeled hydrologic phenomenon, i.e., irrigation. The improvements in fine-scale, modeled soil moisture estimates by assimilating coarse-scale retrievals indicates the utility of the described methodology for data-scarce regions.
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Kristi R. Arsenault, Sujay V. Kumar, James V. Geiger, Shugong Wang, Eric Kemp, David M. Mocko, Hiroko Kato Beaudoing, Augusto Getirana, Mahdi Navari, Bailing Li, Jossy Jacob, Jerry Wegiel, and Christa D. Peters-Lidard
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The Earth’s land surface hydrology and physics can be represented in highly sophisticated models known as land surface models. The Land surface Data Toolkit (LDT) software was developed to meet these models’ input processing needs. LDT supports a variety of land surface and hydrology models and prepares the inputs (e.g., meteorological data, satellite observations to be assimilated into a model), which can be used for inter-model studies and to initialize weather and climate forecasts.
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Data assimilation deals with the blending of model forecasts and observations based on their relative errors. This paper addresses the importance of accurately representing the errors in the model forecasts for skillful data assimilation performance.
S. V. Kumar, C. D. Peters-Lidard, J. A. Santanello, R. H. Reichle, C. S. Draper, R. D. Koster, G. Nearing, and M. F. Jasinski
Hydrol. Earth Syst. Sci., 19, 4463–4478, https://doi.org/10.5194/hess-19-4463-2015, https://doi.org/10.5194/hess-19-4463-2015, 2015
Peyman Abbaszadeh, Fadji Zaouna Maina, Chen Yang, Dan Rosen, Sujay Kumar, Matthew Rodell, and Reed Maxwell
Hydrol. Earth Syst. Sci. Discuss., https://doi.org/10.5194/hess-2024-280, https://doi.org/10.5194/hess-2024-280, 2024
Preprint under review for HESS
Short summary
Short summary
To manage Earth's water resources effectively amid climate change, it's crucial to understand both surface and groundwater processes. We developed a new modeling system that combines two advanced tools, ParFlow and LIS/Noah-MP, to better simulate both land surface and groundwater interactions. By testing this integrated model in the Upper Colorado River Basin, we found it improves predictions of hydrologic processes, especially in complex terrains.
Louise Busschaert, Michel Bechtold, Sara Modanesi, Christian Massari, Dirk Raes, Sujay V. Kumar, and Gabrielle J. M. De Lannoy
EGUsphere, https://doi.org/10.2139/ssrn.4974019, https://doi.org/10.2139/ssrn.4974019, 2024
Short summary
Short summary
This study estimates irrigation in the Po Valley using AquaCrop and Noah-MP models with sprinkler irrigation. Noah-MP shows higher annual rates than AquaCrop due to more water losses. After adjusting, both align with reported irrigation ranges (500–600 mm/yr). Soil moisture estimates from both models match satellite data, though both have limitations in vegetation and evapotranspiration modeling. The study emphasizes the need for observations to improve irrigation estimates.
Pengfei Xue, Chenfu Huang, Yafang Zhong, Michael Notaro, Miraj B. Kayastha, Xing Zhou, Chuyan Zhao, Christa Peters-Lidard, Carlos Cruz, and Eric Kemp
Geosci. Model Dev. Discuss., https://doi.org/10.5194/gmd-2024-146, https://doi.org/10.5194/gmd-2024-146, 2024
Preprint under review for GMD
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This study introduces a new lake-ice-atmosphere coupled model that significantly improves winter climate simulation for the Great Lakes compared to traditional one-dimensional (1D) lake models. It better simulates both lake conditions and over-lake atmospheric conditions. More importantly, the study highlights three critical 3D lake processes—ice movement, heat transport, and turbulent mixing—as essential for accurately simulating lake-atmosphere interactions and the Great Lakes’ winter climate.
Min Huang, Gregory R. Carmichael, James H. Crawford, Kevin W. Bowman, Isabelle De Smedt, Andreas Colliander, Michael H. Cosh, Sujay V. Kumar, Alex B. Guenther, Scott J. Janz, Ryan M. Stauffer, Anne M. Thompson, Niko M. Fedkin, Robert J. Swap, John D. Bolten, and Alicia T. Joseph
EGUsphere, https://doi.org/10.5194/egusphere-2024-484, https://doi.org/10.5194/egusphere-2024-484, 2024
Short summary
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This study uses model simulations along with multiplatform, multidisciplinary observations and a range of analysis methods to estimate and understand the distributions, temporal changes, and impacts of reactive nitrogen and ozone over the most populous US region that has undergone significant environmental changes. Deposition, biogenic emissions, and extra-regional sources have been playing increasingly important roles in controlling pollutants’ budgets in this area as local emissions go down.
Justin M. Pflug, Melissa L. Wrzesien, Sujay V. Kumar, Eunsang Cho, Kristi R. Arsenault, Paul R. Houser, and Carrie M. Vuyovich
Hydrol. Earth Syst. Sci., 28, 631–648, https://doi.org/10.5194/hess-28-631-2024, https://doi.org/10.5194/hess-28-631-2024, 2024
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Estimates of 250 m of snow water equivalent in the western USA and Canada are improved by assimilating observations representative of a snow-focused satellite mission with a land surface model. Here, by including a gap-filling strategy, snow estimates could be improved in forested regions where remote sensing is challenging. This approach improved estimates of winter maximum snow water volume to within 4 %, on average, with persistent improvements to both spring snow and runoff in many regions.
Eunsang Cho, Yonghwan Kwon, Sujay V. Kumar, and Carrie M. Vuyovich
Hydrol. Earth Syst. Sci., 27, 4039–4056, https://doi.org/10.5194/hess-27-4039-2023, https://doi.org/10.5194/hess-27-4039-2023, 2023
Short summary
Short summary
An airborne gamma-ray remote-sensing technique provides reliable snow water equivalent (SWE) in a forested area where remote-sensing techniques (e.g., passive microwave) typically have large uncertainties. Here, we explore the utility of assimilating the gamma snow data into a land surface model to improve the modeled SWE estimates in the northeastern US. Results provide new insights into utilizing the gamma SWE data for enhanced land surface model simulations in forested environments.
Eunsang Cho, Carrie M. Vuyovich, Sujay V. Kumar, Melissa L. Wrzesien, and Rhae Sung Kim
The Cryosphere, 17, 3915–3931, https://doi.org/10.5194/tc-17-3915-2023, https://doi.org/10.5194/tc-17-3915-2023, 2023
Short summary
Short summary
As a future snow mission concept, active microwave sensors have the potential to measure snow water equivalent (SWE) in deep snowpack and forested environments. We used a modeling and data assimilation approach (a so-called observing system simulation experiment) to quantify the usefulness of active microwave-based SWE retrievals over western Colorado. We found that active microwave sensors with a mature retrieval algorithm can improve SWE simulations by about 20 % in the mountainous domain.
Eunsang Cho, Carrie M. Vuyovich, Sujay V. Kumar, Melissa L. Wrzesien, Rhae Sung Kim, and Jennifer M. Jacobs
Hydrol. Earth Syst. Sci., 26, 5721–5735, https://doi.org/10.5194/hess-26-5721-2022, https://doi.org/10.5194/hess-26-5721-2022, 2022
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Short summary
While land surface models are a common approach for estimating macroscale snow water equivalent (SWE), the SWE accuracy is often limited by uncertainties in model physics and forcing inputs. In this study, we found large underestimations of modeled SWE compared to observations. Precipitation forcings and melting physics limitations dominantly contribute to the SWE underestimations. Results provide insights into prioritizing strategies to improve the SWE simulations for hydrologic applications.
Leander Moesinger, Ruxandra-Maria Zotta, Robin van der Schalie, Tracy Scanlon, Richard de Jeu, and Wouter Dorigo
Biogeosciences, 19, 5107–5123, https://doi.org/10.5194/bg-19-5107-2022, https://doi.org/10.5194/bg-19-5107-2022, 2022
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The standardized vegetation optical depth index (SVODI) can be used to monitor the vegetation condition, such as whether the vegetation is unusually dry or wet. SVODI has global coverage, spans the past 3 decades and is derived from multiple spaceborne passive microwave sensors of that period. SVODI is based on a new probabilistic merging method that allows the merging of normally distributed data even if the data are not gap-free.
Robin van der Schalie, Mendy van der Vliet, Clément Albergel, Wouter Dorigo, Piotr Wolski, and Richard de Jeu
Hydrol. Earth Syst. Sci., 26, 3611–3627, https://doi.org/10.5194/hess-26-3611-2022, https://doi.org/10.5194/hess-26-3611-2022, 2022
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Climate data records of surface soil moisture, vegetation optical depth, and land surface temperature can be derived from passive microwave observations. The ability of these datasets to properly detect anomalies and extremes is very valuable in climate research and can especially help to improve our insight in complex regions where the current climate reanalysis datasets reach their limitations. Here, we present a case study over the Okavango Delta, where we focus on inter-annual variability.
Amy McNally, Jossy Jacob, Kristi Arsenault, Kimberly Slinski, Daniel P. Sarmiento, Andrew Hoell, Shahriar Pervez, James Rowland, Mike Budde, Sujay Kumar, Christa Peters-Lidard, and James P. Verdin
Earth Syst. Sci. Data, 14, 3115–3135, https://doi.org/10.5194/essd-14-3115-2022, https://doi.org/10.5194/essd-14-3115-2022, 2022
Short summary
Short summary
The Famine Early Warning Systems Network (FEWS NET) Land Data Assimilation System (FLDAS) global and Central Asia data streams described here generate routine estimates of snow, soil moisture, runoff, and other variables useful for tracking water availability. These data are hosted by NASA and USGS data portals for public use.
Min Huang, James H. Crawford, Gregory R. Carmichael, Kevin W. Bowman, Sujay V. Kumar, and Colm Sweeney
Atmos. Chem. Phys., 22, 7461–7487, https://doi.org/10.5194/acp-22-7461-2022, https://doi.org/10.5194/acp-22-7461-2022, 2022
Short summary
Short summary
This study demonstrates that ozone dry-deposition modeling can be improved by revising the model's dry-deposition parameterizations to better represent the effects of environmental conditions including the soil moisture fields. Applying satellite soil moisture data assimilation is shown to also have added value. Such advancements in coupled modeling and data assimilation can benefit the assessments of ozone impacts on human and vegetation health.
Wanshu Nie, Sujay V. Kumar, Kristi R. Arsenault, Christa D. Peters-Lidard, Iliana E. Mladenova, Karim Bergaoui, Abheera Hazra, Benjamin F. Zaitchik, Sarith P. Mahanama, Rachael McDonnell, David M. Mocko, and Mahdi Navari
Hydrol. Earth Syst. Sci., 26, 2365–2386, https://doi.org/10.5194/hess-26-2365-2022, https://doi.org/10.5194/hess-26-2365-2022, 2022
Short summary
Short summary
The MENA (Middle East and North Africa) region faces significant food and water insecurity and hydrological hazards. Here we investigate the value of assimilating remote sensing data sets into an Earth system model to help build an effective drought monitoring system and support risk mitigation and management by countries in the region. We highlight incorporating satellite-informed vegetation conditions into the model as being one of the key processes for a successful application for the region.
Jawairia A. Ahmad, Barton A. Forman, and Sujay V. Kumar
Hydrol. Earth Syst. Sci., 26, 2221–2243, https://doi.org/10.5194/hess-26-2221-2022, https://doi.org/10.5194/hess-26-2221-2022, 2022
Short summary
Short summary
Assimilation of remotely sensed data into a land surface model to improve the spatiotemporal estimation of soil moisture across South Asia exhibits potential. Satellite retrieval assimilation corrects biases that are generated due to an unmodeled hydrologic phenomenon, i.e., irrigation. The improvements in fine-scale, modeled soil moisture estimates by assimilating coarse-scale retrievals indicates the utility of the described methodology for data-scarce regions.
Min Huang, James H. Crawford, Joshua P. DiGangi, Gregory R. Carmichael, Kevin W. Bowman, Sujay V. Kumar, and Xiwu Zhan
Atmos. Chem. Phys., 21, 11013–11040, https://doi.org/10.5194/acp-21-11013-2021, https://doi.org/10.5194/acp-21-11013-2021, 2021
Short summary
Short summary
This study evaluates the impact of satellite soil moisture data assimilation on modeled weather and ozone fields at various altitudes above the southeastern US during the summer. It emphasizes the importance of soil moisture in the understanding of surface ozone pollution and upper tropospheric chemistry, as well as air pollutants’ source–receptor relationships between the US and its downwind areas.
Michiel Maertens, Gabriëlle J. M. De Lannoy, Sebastian Apers, Sujay V. Kumar, and Sarith P. P. Mahanama
Hydrol. Earth Syst. Sci., 25, 4099–4125, https://doi.org/10.5194/hess-25-4099-2021, https://doi.org/10.5194/hess-25-4099-2021, 2021
Short summary
Short summary
In this study, we simulated the water balance over the South American Dry Chaco and assessed the impact of land cover changes thereon using three different land surface models. Our simulations indicated that different models result in a different partitioning of the total water budget, but all showed an increase in soil moisture and percolation over the deforested areas. We also found that, relative to independent data, no specific land surface model is significantly better than another.
Rhae Sung Kim, Sujay Kumar, Carrie Vuyovich, Paul Houser, Jessica Lundquist, Lawrence Mudryk, Michael Durand, Ana Barros, Edward J. Kim, Barton A. Forman, Ethan D. Gutmann, Melissa L. Wrzesien, Camille Garnaud, Melody Sandells, Hans-Peter Marshall, Nicoleta Cristea, Justin M. Pflug, Jeremy Johnston, Yueqian Cao, David Mocko, and Shugong Wang
The Cryosphere, 15, 771–791, https://doi.org/10.5194/tc-15-771-2021, https://doi.org/10.5194/tc-15-771-2021, 2021
Short summary
Short summary
High SWE uncertainty is observed in mountainous and forested regions, highlighting the need for high-resolution snow observations in these regions. Substantial uncertainty in snow water storage in Tundra regions and the dominance of water storage in these regions points to the need for high-accuracy snow estimation. Finally, snow measurements during the melt season are most needed at high latitudes, whereas observations at near peak snow accumulations are most beneficial over the midlatitudes.
Rogier van der Velde, Andreas Colliander, Michiel Pezij, Harm-Jan F. Benninga, Rajat Bindlish, Steven K. Chan, Thomas J. Jackson, Dimmie M. D. Hendriks, Denie C. M. Augustijn, and Zhongbo Su
Hydrol. Earth Syst. Sci., 25, 473–495, https://doi.org/10.5194/hess-25-473-2021, https://doi.org/10.5194/hess-25-473-2021, 2021
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NASA’s SMAP satellite provides estimates of the amount of water in the soil. With measurements from a network of 20 monitoring stations, the accuracy of these estimates has been studied for a 4-year period. We found an agreement between satellite and in situ estimates in line with the mission requirements once the large mismatches associated with rapidly changing water contents, e.g. soil freezing and rainfall, are excluded.
Yifan Zhou, Benjamin F. Zaitchik, Sujay V. Kumar, Kristi R. Arsenault, Mir A. Matin, Faisal M. Qamer, Ryan A. Zamora, and Kiran Shakya
Hydrol. Earth Syst. Sci., 25, 41–61, https://doi.org/10.5194/hess-25-41-2021, https://doi.org/10.5194/hess-25-41-2021, 2021
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South and Southeast Asia face significant food insecurity and hydrological hazards. Here we introduce a South and Southeast Asia hydrological monitoring and sub-seasonal to seasonal forecasting system (SAHFS-S2S) to help local governments and decision-makers prepare for extreme hydroclimatic events. The monitoring system captures soil moisture variability well in most regions, and the forecasting system offers skillful prediction of soil moisture variability 2–3 months in advance, on average.
Xinxuan Zhang, Viviana Maggioni, Azbina Rahman, Paul Houser, Yuan Xue, Timothy Sauer, Sujay Kumar, and David Mocko
Hydrol. Earth Syst. Sci., 24, 3775–3788, https://doi.org/10.5194/hess-24-3775-2020, https://doi.org/10.5194/hess-24-3775-2020, 2020
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This study assesses the extent to which a land surface model can be optimized via the assimilation of leaf area index (LAI) observations at the global scale. The model performance is evaluated by the model-estimated LAI and five water flux/storage variables. Results show the LAI assimilation reduces errors in the model-estimated LAI. The LAI assimilation also improves the five water variables under wet conditions, but some of the model-estimated variables tend to be worse under dry conditions.
Shraddhanand Shukla, Kristi R. Arsenault, Abheera Hazra, Christa Peters-Lidard, Randal D. Koster, Frank Davenport, Tamuka Magadzire, Chris Funk, Sujay Kumar, Amy McNally, Augusto Getirana, Greg Husak, Ben Zaitchik, Jim Verdin, Faka Dieudonne Nsadisa, and Inbal Becker-Reshef
Nat. Hazards Earth Syst. Sci., 20, 1187–1201, https://doi.org/10.5194/nhess-20-1187-2020, https://doi.org/10.5194/nhess-20-1187-2020, 2020
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The region of southern Africa is prone to climate-driven food insecurity events, as demonstrated by the major drought event in 2015–2016. This study demonstrates that recently developed NASA Hydrological Forecasting and Analysis System-based root-zone soil moisture monitoring and forecasting products are well correlated with interannual regional crop yield, can identify below-normal crop yield events and provide skillful crop yield forecasts, and hence support early warning of food insecurity.
Leander Moesinger, Wouter Dorigo, Richard de Jeu, Robin van der Schalie, Tracy Scanlon, Irene Teubner, and Matthias Forkel
Earth Syst. Sci. Data, 12, 177–196, https://doi.org/10.5194/essd-12-177-2020, https://doi.org/10.5194/essd-12-177-2020, 2020
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Vegetation optical depth (VOD) is measured by satellites and is related to the density of vegetation and its water content. VOD has a wide range of uses, including drought, wildfire danger, biomass, and carbon stock monitoring. For the past 30 years there have been various VOD data sets derived from space-borne microwave sensors, but biases between them prohibit a combined use. We removed these biases and merged the data to create the global long-term VOD Climate Archive (VODCA).
Nevil Quinn, Günter Blöschl, András Bárdossy, Attilio Castellarin, Martyn Clark, Christophe Cudennec, Demetris Koutsoyiannis, Upmanu Lall, Lubomir Lichner, Juraj Parajka, Christa D. Peters-Lidard, Graham Sander, Hubert Savenije, Keith Smettem, Harry Vereecken, Alberto Viglione, Patrick Willems, Andy Wood, Ross Woods, Chong-Yu Xu, and Erwin Zehe
Proc. IAHS, 380, 3–8, https://doi.org/10.5194/piahs-380-3-2018, https://doi.org/10.5194/piahs-380-3-2018, 2018
Nevil Quinn, Günter Blöschl, András Bárdossy, Attilio Castellarin, Martyn Clark, Christophe Cudennec, Demetris Koutsoyiannis, Upmanu Lall, Lubomir Lichner, Juraj Parajka, Christa D. Peters-Lidard, Graham Sander, Hubert Savenije, Keith Smettem, Harry Vereecken, Alberto Viglione, Patrick Willems, Andy Wood, Ross Woods, Chong-Yu Xu, and Erwin Zehe
Hydrol. Earth Syst. Sci., 22, 5735–5739, https://doi.org/10.5194/hess-22-5735-2018, https://doi.org/10.5194/hess-22-5735-2018, 2018
Kristi R. Arsenault, Sujay V. Kumar, James V. Geiger, Shugong Wang, Eric Kemp, David M. Mocko, Hiroko Kato Beaudoing, Augusto Getirana, Mahdi Navari, Bailing Li, Jossy Jacob, Jerry Wegiel, and Christa D. Peters-Lidard
Geosci. Model Dev., 11, 3605–3621, https://doi.org/10.5194/gmd-11-3605-2018, https://doi.org/10.5194/gmd-11-3605-2018, 2018
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The Earth’s land surface hydrology and physics can be represented in highly sophisticated models known as land surface models. The Land surface Data Toolkit (LDT) software was developed to meet these models’ input processing needs. LDT supports a variety of land surface and hydrology models and prepares the inputs (e.g., meteorological data, satellite observations to be assimilated into a model), which can be used for inter-model studies and to initialize weather and climate forecasts.
Anouk I. Gevaert, Luigi J. Renzullo, Albert I. J. M. van Dijk, Hans J. van der Woerd, Albrecht H. Weerts, and Richard A. M. de Jeu
Hydrol. Earth Syst. Sci., 22, 4605–4619, https://doi.org/10.5194/hess-22-4605-2018, https://doi.org/10.5194/hess-22-4605-2018, 2018
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We assimilated three satellite soil moisture retrievals based on different microwave frequencies into a hydrological model. Two sets of experiments were performed, first assimilating the retrievals individually and then assimilating each set of two retrievals jointly. Overall, assimilation improved agreement between model and field-measured soil moisture. Joint assimilation resulted in model performance similar to or better than assimilating either retrieval individually.
Thomas R. H. Holmes, Christopher R. Hain, Wade T. Crow, Martha C. Anderson, and William P. Kustas
Hydrol. Earth Syst. Sci., 22, 1351–1369, https://doi.org/10.5194/hess-22-1351-2018, https://doi.org/10.5194/hess-22-1351-2018, 2018
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In an effort to apply cloud-tolerant microwave data to satellite-based monitoring of evapotranspiration (ET), this study reports on an experiment where microwave-based land surface temperature is used as the key diagnostic input to a two-source energy balance method for the estimation of ET. Comparisons of this microwave ET with the conventional thermal infrared estimates show widespread agreement in spatial and temporal patterns from seasonal to inter-annual timescales over Africa and Europe.
Christa D. Peters-Lidard, Martyn Clark, Luis Samaniego, Niko E. C. Verhoest, Tim van Emmerik, Remko Uijlenhoet, Kevin Achieng, Trenton E. Franz, and Ross Woods
Hydrol. Earth Syst. Sci., 21, 3701–3713, https://doi.org/10.5194/hess-21-3701-2017, https://doi.org/10.5194/hess-21-3701-2017, 2017
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In this synthesis of hydrologic scaling and similarity, we assert that it is time for hydrology to embrace a fourth paradigm of data-intensive science. Advances in information-based hydrologic science, coupled with an explosion of hydrologic data and advances in parameter estimation and modeling, have laid the foundation for a data-driven framework for scrutinizing hydrological hypotheses. We call upon the community to develop a focused effort towards a fourth paradigm for hydrology.
Martyn P. Clark, Marc F. P. Bierkens, Luis Samaniego, Ross A. Woods, Remko Uijlenhoet, Katrina E. Bennett, Valentijn R. N. Pauwels, Xitian Cai, Andrew W. Wood, and Christa D. Peters-Lidard
Hydrol. Earth Syst. Sci., 21, 3427–3440, https://doi.org/10.5194/hess-21-3427-2017, https://doi.org/10.5194/hess-21-3427-2017, 2017
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The diversity in hydrologic models has led to controversy surrounding the “correct” approach to hydrologic modeling. In this paper we revisit key modeling challenges on requirements to (1) define suitable model equations, (2) define adequate model parameters, and (3) cope with limitations in computing power. We outline the historical modeling challenges, summarize modeling advances that address these challenges, and define outstanding research needs.
Sujay V. Kumar, Jiarui Dong, Christa D. Peters-Lidard, David Mocko, and Breogán Gómez
Hydrol. Earth Syst. Sci., 21, 2637–2647, https://doi.org/10.5194/hess-21-2637-2017, https://doi.org/10.5194/hess-21-2637-2017, 2017
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Data assimilation deals with the blending of model forecasts and observations based on their relative errors. This paper addresses the importance of accurately representing the errors in the model forecasts for skillful data assimilation performance.
Brecht Martens, Diego G. Miralles, Hans Lievens, Robin van der Schalie, Richard A. M. de Jeu, Diego Fernández-Prieto, Hylke E. Beck, Wouter A. Dorigo, and Niko E. C. Verhoest
Geosci. Model Dev., 10, 1903–1925, https://doi.org/10.5194/gmd-10-1903-2017, https://doi.org/10.5194/gmd-10-1903-2017, 2017
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Terrestrial evaporation is a key component of the hydrological cycle and reliable data sets of this variable are of major importance. The Global Land Evaporation Amsterdam Model (GLEAM, www.GLEAM.eu) is a set of algorithms which estimates evaporation based on satellite observations. The third version of GLEAM, presented in this study, includes an improved parameterization of different model components. As a result, the accuracy of the GLEAM data sets has been improved upon previous versions.
Thomas R. H. Holmes, Christopher R. Hain, Martha C. Anderson, and Wade T. Crow
Hydrol. Earth Syst. Sci., 20, 3263–3275, https://doi.org/10.5194/hess-20-3263-2016, https://doi.org/10.5194/hess-20-3263-2016, 2016
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We test the cloud tolerance of two technologies to estimate land surface temperature (LST) from space: microwave (MW) and thermal infrared (TIR). Although TIR has slightly lower errors than MW with ground data under clear-sky conditions, it suffers increasing negative bias as cloud cover increases. In contrast, we find no direct impact of clouds on the accuracy and bias of MW-LST. MW-LST can therefore be used to improve TIR cloud screening and increase sampling in clouded regions.
S. V. Kumar, C. D. Peters-Lidard, J. A. Santanello, R. H. Reichle, C. S. Draper, R. D. Koster, G. Nearing, and M. F. Jasinski
Hydrol. Earth Syst. Sci., 19, 4463–4478, https://doi.org/10.5194/hess-19-4463-2015, https://doi.org/10.5194/hess-19-4463-2015, 2015
Z. Tao, J. A. Santanello, M. Chin, S. Zhou, Q. Tan, E. M. Kemp, and C. D. Peters-Lidard
Atmos. Chem. Phys., 13, 6207–6226, https://doi.org/10.5194/acp-13-6207-2013, https://doi.org/10.5194/acp-13-6207-2013, 2013
A. C. V. Getirana and C. Peters-Lidard
Hydrol. Earth Syst. Sci., 17, 923–933, https://doi.org/10.5194/hess-17-923-2013, https://doi.org/10.5194/hess-17-923-2013, 2013
Related subject area
Subject: Global hydrology | Techniques and Approaches: Remote Sensing and GIS
Interannual variations of terrestrial water storage in the East African Rift region
Technical note: Surface fields for global environmental modelling
Benchmarking multimodel terrestrial water storage seasonal cycle against Gravity Recovery and Climate Experiment (GRACE) observations over major global river basins
Increasing seasonal variation in the extent of rivers and lakes from 1984 to 2022
Investigating sources of variability in closing the terrestrial water balance with remote sensing
Characterising recent drought events in the context of dry-season trends using state-of-the-art reanalysis and remote-sensing soil moisture products
Dynamic rainfall erosivity estimates derived from IMERG data
A global analysis of water storage variations from remotely sensed soil moisture and daily satellite gravimetry
Soil moisture estimates at 1 km resolution making a synergistic use of Sentinel data
Global evaluation of the “dry gets drier, and wet gets wetter” paradigm from a terrestrial water storage change perspective
Global assessment of subnational drought impact based on the Geocoded Disasters dataset and land reanalysis
Scaling methods of leakage correction in GRACE mass change estimates revisited for the complex hydro-climatic setting of the Indus Basin
Remotely sensed reservoir water storage dynamics (1984–2015) and the influence of climate variability and management at a global scale
Characterizing natural variability in complex hydrological systems using passive microwave-based climate data records: a case study for the Okavango Delta
High-resolution (1 km) satellite rainfall estimation from SM2RAIN applied to Sentinel-1: Po River basin as a case study
The accuracy of temporal upscaling of instantaneous evapotranspiration to daily values with seven upscaling methods
Global component analysis of errors in three satellite-only global precipitation estimates
Estimation of hydrological drought recovery based on precipitation and Gravity Recovery and Climate Experiment (GRACE) water storage deficit
Intercomparison of freshwater fluxes over ocean and investigations into water budget closure
Widespread decline in terrestrial water storage and its link to teleconnections across Asia and eastern Europe
Long-term total water storage change from a Satellite Water Cycle reconstruction over large southern Asian basins
Global partitioning of runoff generation mechanisms using remote sensing data
Land–atmosphere interactions in the tropics – a review
Global-scale human pressure evolution imprints on sustainability of river systems
Using GRACE in a streamflow recession to determine drainable water storage in the Mississippi River basin
A new dense 18-year time series of surface water fraction estimates from MODIS for the Mediterranean region
Global joint assimilation of GRACE and SMOS for improved estimation of root-zone soil moisture and vegetation response
Using modelled discharge to develop satellite-based river gauging: a case study for the Amazon Basin
Global downscaling of remotely sensed soil moisture using neural networks
Global 5 km resolution estimates of secondary evaporation including irrigation through satellite data assimilation
Exploring the merging of the global land evaporation WACMOS-ET products based on local tower measurements
Estimating time-dependent vegetation biases in the SMAP soil moisture product
Daily GRACE gravity field solutions track major flood events in the Ganges–Brahmaputra Delta
Controls on surface soil drying rates observed by SMAP and simulated by the Noah land surface model
Quantification of surface water volume changes in the Mackenzie Delta using satellite multi-mission data
Microwave implementation of two-source energy balance approach for estimating evapotranspiration
A global approach to estimate irrigated areas – a comparison between different data and statistics
The future of Earth observation in hydrology
Validation of terrestrial water storage variations as simulated by different global numerical models with GRACE satellite observations
MSWEP: 3-hourly 0.25° global gridded precipitation (1979–2015) by merging gauge, satellite, and reanalysis data
Evaluating the hydrological consistency of evaporation products using satellite-based gravity and rainfall data
Evaluating the strength of the land–atmosphere moisture feedback in Earth system models using satellite observations
Cloud tolerance of remote-sensing technologies to measure land surface temperature
Dynamic changes in terrestrial net primary production and their effects on evapotranspiration
Assessing changes in urban flood vulnerability through mapping land use from historical information
SACRA – a method for the estimation of global high-resolution crop calendars from a satellite-sensed NDVI
A global data set of the extent of irrigated land from 1900 to 2005
Evaluation of the satellite-based Global Flood Detection System for measuring river discharge: influence of local factors
Spatial patterns in timing of the diurnal temperature cycle
Potential and limitations of multidecadal satellite soil moisture observations for selected climate model evaluation studies
Eva Boergens, Andreas Güntner, Mike Sips, Christian Schwatke, and Henryk Dobslaw
Hydrol. Earth Syst. Sci., 28, 4733–4754, https://doi.org/10.5194/hess-28-4733-2024, https://doi.org/10.5194/hess-28-4733-2024, 2024
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The satellites GRACE and GRACE-FO observe continental terrestrial water storage (TWS) changes. With over 20 years of data, we can look into long-term variations in the East Africa Rift region. We focus on analysing the interannual TWS variations compared to meteorological data and observations of the water storage compartments. We found strong influences of natural precipitation variability and human actions over Lake Victoria's water level.
Margarita Choulga, Francesca Moschini, Cinzia Mazzetti, Stefania Grimaldi, Juliana Disperati, Hylke Beck, Peter Salamon, and Christel Prudhomme
Hydrol. Earth Syst. Sci., 28, 2991–3036, https://doi.org/10.5194/hess-28-2991-2024, https://doi.org/10.5194/hess-28-2991-2024, 2024
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CEMS_SurfaceFields_2022 dataset is a new set of high-resolution maps for land type (e.g. lake, forest), soil properties and population water needs at approximately 2 and 6 km at the Equator, covering Europe and the globe (excluding Antarctica). We describe what and how new high-resolution information can be used to create the dataset. The paper suggests that the dataset can be used as input for river, weather or other models, as well as for statistical descriptions of the region of interest.
Sadia Bibi, Tingju Zhu, Ashraf Rateb, Bridget R. Scanlon, Muhammad Aqeel Kamran, Abdelrazek Elnashar, Ali Bennour, and Ci Li
Hydrol. Earth Syst. Sci., 28, 1725–1750, https://doi.org/10.5194/hess-28-1725-2024, https://doi.org/10.5194/hess-28-1725-2024, 2024
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We assessed 13 global models using GRACE satellite data over 29 river basins. Simulated seasonal water storage cycles showed discrepancies compared to GRACE. The models overestimated seasonal amplitude in boreal basins and showed underestimation in tropical, arid, and temperate zones, with phase differences of 2–3 months compared to GRACE in cold basins and of 1 month in temperate, arid, and semi-arid basins. Seasonal amplitude and phase differences provide insights for model improvement.
Björn Nyberg, Roger Sayre, and Elco Luijendijk
Hydrol. Earth Syst. Sci., 28, 1653–1663, https://doi.org/10.5194/hess-28-1653-2024, https://doi.org/10.5194/hess-28-1653-2024, 2024
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Understanding the spatial and temporal distribution of surface water is crucial for effective water resource management, maintaining ecosystem health and assessing flood risks. This study examined permanent and seasonal rivers and lakes globally over 38 years, uncovering a statistically significant expansion in seasonal extent captured in the new SARL database. The findings offer valuable resources for assessing the impact of changing river and lake extents on ecosystems and human livelihoods.
Claire I. Michailovsky, Bert Coerver, Marloes Mul, and Graham Jewitt
Hydrol. Earth Syst. Sci., 27, 4335–4354, https://doi.org/10.5194/hess-27-4335-2023, https://doi.org/10.5194/hess-27-4335-2023, 2023
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Many remote sensing products for precipitation, evapotranspiration, and water storage variations exist. However, when these are used with in situ runoff data in water balance closure studies, no single combination of products consistently outperforms others. We analyzed the water balance closure using different products in catchments worldwide and related the results to catchment characteristics. Our results can help identify the dataset combinations best suited for use in different catchments.
Martin Hirschi, Bas Crezee, Pietro Stradiotti, Wouter Dorigo, and Sonia I. Seneviratne
EGUsphere, https://doi.org/10.5194/egusphere-2023-2499, https://doi.org/10.5194/egusphere-2023-2499, 2023
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Based on surface and root-zone soil moisture, we compare the ability of selected long-term reanalysis and merged remote-sensing products to represent major agroecological drought events. While all products capture the investigated droughts, they particularly show differences in the drought magnitudes. Globally, the diverse and regionally contradicting dry-season soil moisture trends of the products is an important factor governing their drought representation and monitoring capability.
Robert A. Emberson
Hydrol. Earth Syst. Sci., 27, 3547–3563, https://doi.org/10.5194/hess-27-3547-2023, https://doi.org/10.5194/hess-27-3547-2023, 2023
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Soil can be eroded by rainfall, and this is a major threat to agricultural sustainability. Estimating the erosivity of rainfall is essential as a first step to determine how much soil might be lost. Until recently, satellite data have not been used to estimate rainfall erosivity, but the data quality is now sufficient to do so. In this study, I test several methods to calculate rainfall erosivity using satellite rainfall data and contrast this with ground-based estimates.
Daniel Blank, Annette Eicker, Laura Jensen, and Andreas Güntner
Hydrol. Earth Syst. Sci., 27, 2413–2435, https://doi.org/10.5194/hess-27-2413-2023, https://doi.org/10.5194/hess-27-2413-2023, 2023
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Soil moisture (SM), a key variable of the global water cycle, is analyzed using two types of satellite observations; microwave sensors measure the top few centimeters and satellite gravimetry (GRACE) the full vertical water column. As SM can change very fast, non-standard daily GRACE data are applied for the first time for this analysis. Jointly analyzing these data gives insight into the SM dynamics at different soil depths, and time shifts indicate the infiltration time into deeper layers.
Remi Madelon, Nemesio J. Rodríguez-Fernández, Hassan Bazzi, Nicolas Baghdadi, Clement Albergel, Wouter Dorigo, and Mehrez Zribi
Hydrol. Earth Syst. Sci., 27, 1221–1242, https://doi.org/10.5194/hess-27-1221-2023, https://doi.org/10.5194/hess-27-1221-2023, 2023
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We present an approach to estimate soil moisture (SM) at 1 km resolution using Sentinel-1 and Sentinel-3 satellites. The estimates were compared to other high-resolution (HR) datasets over Europe, northern Africa, Australia, and North America, showing good agreement. However, the discrepancies between the different HR datasets and their lower performances compared with in situ measurements and coarse-resolution datasets show the remaining challenges for large-scale HR SM mapping.
Jinghua Xiong, Shenglian Guo, Abhishek, Jie Chen, and Jiabo Yin
Hydrol. Earth Syst. Sci., 26, 6457–6476, https://doi.org/10.5194/hess-26-6457-2022, https://doi.org/10.5194/hess-26-6457-2022, 2022
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Although the "dry gets drier, and wet gets wetter (DDWW)" paradigm is prevalent in summarizing wetting and drying trends, we show that only 11.01 %–40.84 % of the global land confirms and 10.21 %–35.43 % contradicts the paradigm during 1985–2014 from a terrestrial water storage change perspective. Similar proportions that intensify with the increasing emission scenarios persist until the end of the 21st century. Findings benefit understanding of global hydrological responses to climate change.
Yuya Kageyama and Yohei Sawada
Hydrol. Earth Syst. Sci., 26, 4707–4720, https://doi.org/10.5194/hess-26-4707-2022, https://doi.org/10.5194/hess-26-4707-2022, 2022
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This study explores the link between hydrometeorological droughts and their socioeconomic impact at a subnational scale based on the newly developed disaster dataset with subnational location information. Hydrometeorological drought-prone areas were generally consistent with socioeconomic drought-prone areas in the disaster dataset. Our analysis clarifies the importance of the use of subnational disaster information.
Vasaw Tripathi, Andreas Groh, Martin Horwath, and Raaj Ramsankaran
Hydrol. Earth Syst. Sci., 26, 4515–4535, https://doi.org/10.5194/hess-26-4515-2022, https://doi.org/10.5194/hess-26-4515-2022, 2022
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GRACE/GRACE-FO provided global observations of water storage change since 2002. Scaling is a common approach to compensate for the spatial filtering inherent to the results. However, for complex hydrological basins, the compatibility of scaling with the characteristics of regional hydrology has been rarely assessed. We assess traditional scaling approaches and a new scaling approach for the Indus Basin. Our results will help users with regional focus understand implications of scaling choices.
Jiawei Hou, Albert I. J. M. van Dijk, Hylke E. Beck, Luigi J. Renzullo, and Yoshihide Wada
Hydrol. Earth Syst. Sci., 26, 3785–3803, https://doi.org/10.5194/hess-26-3785-2022, https://doi.org/10.5194/hess-26-3785-2022, 2022
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We used satellite imagery to measure monthly reservoir water volumes for 6695 reservoirs worldwide for 1984–2015. We investigated how changing precipitation, streamflow, evaporation, and human activity affected reservoir water storage. Almost half of the reservoirs showed significant increasing or decreasing trends over the past three decades. These changes are caused, first and foremost, by changes in precipitation rather than by changes in net evaporation or dam release patterns.
Robin van der Schalie, Mendy van der Vliet, Clément Albergel, Wouter Dorigo, Piotr Wolski, and Richard de Jeu
Hydrol. Earth Syst. Sci., 26, 3611–3627, https://doi.org/10.5194/hess-26-3611-2022, https://doi.org/10.5194/hess-26-3611-2022, 2022
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Climate data records of surface soil moisture, vegetation optical depth, and land surface temperature can be derived from passive microwave observations. The ability of these datasets to properly detect anomalies and extremes is very valuable in climate research and can especially help to improve our insight in complex regions where the current climate reanalysis datasets reach their limitations. Here, we present a case study over the Okavango Delta, where we focus on inter-annual variability.
Paolo Filippucci, Luca Brocca, Raphael Quast, Luca Ciabatta, Carla Saltalippi, Wolfgang Wagner, and Angelica Tarpanelli
Hydrol. Earth Syst. Sci., 26, 2481–2497, https://doi.org/10.5194/hess-26-2481-2022, https://doi.org/10.5194/hess-26-2481-2022, 2022
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A high-resolution (1 km) rainfall product with 10–30 d temporal resolution was obtained starting from SM data from Sentinel-1. Good performances are achieved using observed data (gauge and radar) over the Po River Valley, Italy, as a benchmark. The comparison with a product characterized by lower spatial resolution (25 km) highlights areas where the high spatial resolution of Sentinel-1 has great benefits. Possible applications include water management, agriculture and index-based insurances.
Zhaofei Liu
Hydrol. Earth Syst. Sci., 25, 4417–4433, https://doi.org/10.5194/hess-25-4417-2021, https://doi.org/10.5194/hess-25-4417-2021, 2021
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Instantaneous evapotranspiration (ET), which is detected by the remote sensing technique, needs to be upscaled to daily values in order to practical applications. The accuracy of seven upscaling methods is evaluated by using global observations. The sine function and the evaporative fraction method using extraterrestrial solar irradiance are recommended. Although every upscaling scheme has high accuracy at most sites, it is less accurate at tropical rainforest and tropical monsoon sites.
Hanqing Chen, Bin Yong, Pierre-Emmanuel Kirstetter, Leyang Wang, and Yang Hong
Hydrol. Earth Syst. Sci., 25, 3087–3104, https://doi.org/10.5194/hess-25-3087-2021, https://doi.org/10.5194/hess-25-3087-2021, 2021
Alka Singh, John Thomas Reager, and Ali Behrangi
Hydrol. Earth Syst. Sci., 25, 511–526, https://doi.org/10.5194/hess-25-511-2021, https://doi.org/10.5194/hess-25-511-2021, 2021
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The study demonstrates the utility of Gravity Recovery and Climate Experiment (GRACE) terrestrial water storage anomalies (TWSAs) for obtaining statistics of hydrological droughts, i.e., recovery periods and required precipitation in different precipitation scenarios. The findings of this study are that the GRACE-based drought index is valid for estimating the required precipitation for drought recovery, and the period of drought recovery depends on the intensity of the precipitation.
Marloes Gutenstein, Karsten Fennig, Marc Schröder, Tim Trent, Stephan Bakan, J. Brent Roberts, and Franklin R. Robertson
Hydrol. Earth Syst. Sci., 25, 121–146, https://doi.org/10.5194/hess-25-121-2021, https://doi.org/10.5194/hess-25-121-2021, 2021
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The net exchange of water between the surface and atmosphere is mainly determined by the freshwater flux: the difference between evaporation (E) and precipitation (P), or E−P. Although there is consensus among modelers that with a warming climate E−P will increase, evidence from satellite data is still not conclusive, mainly due to sensor calibration issues. We here investigate the degree of correspondence among six recent
satellite-based climate data records and ERA5 reanalysis E−P data.
Xianfeng Liu, Xiaoming Feng, Philippe Ciais, and Bojie Fu
Hydrol. Earth Syst. Sci., 24, 3663–3676, https://doi.org/10.5194/hess-24-3663-2020, https://doi.org/10.5194/hess-24-3663-2020, 2020
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Freshwater availability is crucial for sustainable development across the Asian and eastern European regions. Our results indicate widespread decline in terrestrial water storage (TWS) over the region during 2002–2017, primarily due to the intensive over-extraction of groundwater and warmth-induced surface water loss. The findings provide insights into changes in TWS and its components over the Asian and eastern European regions, where there is growing demand for food grains and water supplies.
Victor Pellet, Filipe Aires, Fabrice Papa, Simon Munier, and Bertrand Decharme
Hydrol. Earth Syst. Sci., 24, 3033–3055, https://doi.org/10.5194/hess-24-3033-2020, https://doi.org/10.5194/hess-24-3033-2020, 2020
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The water mass variation at and below the land surface is a major component of the water cycle that was first estimated using GRACE observations (2002–2017). Our analysis shows the advantages of the use of satellite observation for precipitation and evapotranspiration along with river discharge measurement to perform an indirect and coherent reconstruction of this water component estimate over longer time periods.
Joseph T. D. Lucey, John T. Reager, and Sonya R. Lopez
Hydrol. Earth Syst. Sci., 24, 1415–1427, https://doi.org/10.5194/hess-24-1415-2020, https://doi.org/10.5194/hess-24-1415-2020, 2020
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This work relates total water storage (TWS) and rainfall to surface water inundation (SWI) using NASA satellite data. We determine whether TWS and/or rainfall control global SWI developments. Regression methods and cross-correlations were used to relate the measurements and correct for time differences among peaks. Results show TWS and rainfall control most global SWI developments. To our knowledge, this is the first global study on SWI controls and validates previous findings.
Pierre Gentine, Adam Massmann, Benjamin R. Lintner, Sayed Hamed Alemohammad, Rong Fu, Julia K. Green, Daniel Kennedy, and Jordi Vilà-Guerau de Arellano
Hydrol. Earth Syst. Sci., 23, 4171–4197, https://doi.org/10.5194/hess-23-4171-2019, https://doi.org/10.5194/hess-23-4171-2019, 2019
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Land–atmosphere interactions are key for the exchange of water, energy, and carbon dioxide, especially in the tropics. We here review some of the recent findings on land–atmosphere interactions in the tropics and where we see potential challenges and paths forward.
Serena Ceola, Francesco Laio, and Alberto Montanari
Hydrol. Earth Syst. Sci., 23, 3933–3944, https://doi.org/10.5194/hess-23-3933-2019, https://doi.org/10.5194/hess-23-3933-2019, 2019
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A simple and effective index for the quantitative estimation of the evolution of human pressure on rivers at global scale is proposed. This index, based on nightlights and river discharge data, shows a significant increase from 1992 to 2013 worldwide. The most notable changes are found in river basins across Africa and Asia, where human pressure on rivers is growing markedly. This index identifies priority areas that can be targeted for the implementation of mitigation strategies and plans.
Heloisa Ehalt Macedo, Ralph Edward Beighley, Cédric H. David, and John T. Reager
Hydrol. Earth Syst. Sci., 23, 3269–3277, https://doi.org/10.5194/hess-23-3269-2019, https://doi.org/10.5194/hess-23-3269-2019, 2019
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The water stored under the surface is very important for defining the amount of water available for human and environmental applications; however, it is still a challenge to obtain such measurements. NASA's GRACE satellites provide information on total terrestrial water storage based on observations of gravity changes. Here, we relate GRACE data to streamflow measurements, providing estimations of the fraction of baseflow and total drainable storage for the Mississippi River basin.
Linlin Li, Andrew Skidmore, Anton Vrieling, and Tiejun Wang
Hydrol. Earth Syst. Sci., 23, 3037–3056, https://doi.org/10.5194/hess-23-3037-2019, https://doi.org/10.5194/hess-23-3037-2019, 2019
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We derived an 8 d, 500 m resolution surface water fraction product over the Mediterranean region for 2000–2017 based on MODIS data. This dataset complements existing surface water/wetland datasets by adding more temporal detail. It allows for the seasonal, inter-annual, and long-term dynamics of the surface water extent to be monitored, inclusive of small-sized and highly dynamic water bodies; it can also contribute to biodiversity and climate change assessment.
Siyuan Tian, Luigi J. Renzullo, Albert I. J. M. van Dijk, Paul Tregoning, and Jeffrey P. Walker
Hydrol. Earth Syst. Sci., 23, 1067–1081, https://doi.org/10.5194/hess-23-1067-2019, https://doi.org/10.5194/hess-23-1067-2019, 2019
Jiawei Hou, Albert I. J. M. van Dijk, Luigi J. Renzullo, and Robert A. Vertessy
Hydrol. Earth Syst. Sci., 22, 6435–6448, https://doi.org/10.5194/hess-22-6435-2018, https://doi.org/10.5194/hess-22-6435-2018, 2018
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Satellite-based river gauging can be constructed based on remote-sensing-derived surface water extent and modelled discharge, and used to estimate river discharges with satellite observations only. This provides opportunities for monitoring river discharge in the absence of a real-time hydrological model or gauging stations.
Seyed Hamed Alemohammad, Jana Kolassa, Catherine Prigent, Filipe Aires, and Pierre Gentine
Hydrol. Earth Syst. Sci., 22, 5341–5356, https://doi.org/10.5194/hess-22-5341-2018, https://doi.org/10.5194/hess-22-5341-2018, 2018
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A new machine learning algorithm is developed to downscale satellite-based soil moisture estimates from their native spatial scale of 9 km to 2.25 km.
Albert I. J. M. van Dijk, Jaap Schellekens, Marta Yebra, Hylke E. Beck, Luigi J. Renzullo, Albrecht Weerts, and Gennadii Donchyts
Hydrol. Earth Syst. Sci., 22, 4959–4980, https://doi.org/10.5194/hess-22-4959-2018, https://doi.org/10.5194/hess-22-4959-2018, 2018
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Evaporation from wetlands, lakes and irrigation areas needs to be measured to understand water scarcity. So far, this has only been possible for small regions. Here, we develop a solution that can be applied at a very high resolution globally by making use of satellite observations. Our results show that 16% of global water resources evaporate before reaching the ocean, mostly from surface water. Irrigation water use is less than 1% globally but is a very large water user in several dry basins.
Carlos Jiménez, Brecht Martens, Diego M. Miralles, Joshua B. Fisher, Hylke E. Beck, and Diego Fernández-Prieto
Hydrol. Earth Syst. Sci., 22, 4513–4533, https://doi.org/10.5194/hess-22-4513-2018, https://doi.org/10.5194/hess-22-4513-2018, 2018
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Observing the amount of water evaporated in nature is not easy, and we need to combine accurate local measurements with estimates from satellites, more uncertain but covering larger areas. This is the main topic of our paper, in which local observations are compared with global land evaporation estimates, followed by a weighting of the global observations based on this comparison to attempt derive a more accurate evaporation product.
Simon Zwieback, Andreas Colliander, Michael H. Cosh, José Martínez-Fernández, Heather McNairn, Patrick J. Starks, Marc Thibeault, and Aaron Berg
Hydrol. Earth Syst. Sci., 22, 4473–4489, https://doi.org/10.5194/hess-22-4473-2018, https://doi.org/10.5194/hess-22-4473-2018, 2018
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Satellite soil moisture products can provide critical information on incipient droughts and the interplay between vegetation and water availability. However, time-variant systematic errors in the soil moisture products may impede their usefulness. Using a novel statistical approach, we detect such errors (associated with changing vegetation) in the SMAP soil moisture product. The vegetation-associated biases impede drought detection and the quantification of vegetation–water interactions.
Ben T. Gouweleeuw, Andreas Kvas, Christian Gruber, Animesh K. Gain, Thorsten Mayer-Gürr, Frank Flechtner, and Andreas Güntner
Hydrol. Earth Syst. Sci., 22, 2867–2880, https://doi.org/10.5194/hess-22-2867-2018, https://doi.org/10.5194/hess-22-2867-2018, 2018
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Daily GRACE gravity field solutions have been evaluated against daily river runoff data for major flood events in the Ganges–Brahmaputra Delta in 2004 and 2007. Compared to the monthly gravity field solutions, the trends over periods of a few days in the daily gravity field solutions are able to reflect temporal variations in river runoff during major flood events. This implies that daily gravity field solutions released in near-real time may support flood monitoring for large events.
Peter J. Shellito, Eric E. Small, and Ben Livneh
Hydrol. Earth Syst. Sci., 22, 1649–1663, https://doi.org/10.5194/hess-22-1649-2018, https://doi.org/10.5194/hess-22-1649-2018, 2018
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After soil gets wet, much of the surface moisture evaporates directly back into the air. Recent satellite data show that this process is enhanced when there is more water in the soil, less humidity in the air, and less vegetation covering the ground. A widely used model shows similar effects of soil water and humidity, but it largely misses the role of vegetation and assigns outsized importance to soil type. These results are encouraging evidence that the satellite can be used to improve models.
Cassandra Normandin, Frédéric Frappart, Bertrand Lubac, Simon Bélanger, Vincent Marieu, Fabien Blarel, Arthur Robinet, and Léa Guiastrennec-Faugas
Hydrol. Earth Syst. Sci., 22, 1543–1561, https://doi.org/10.5194/hess-22-1543-2018, https://doi.org/10.5194/hess-22-1543-2018, 2018
Thomas R. H. Holmes, Christopher R. Hain, Wade T. Crow, Martha C. Anderson, and William P. Kustas
Hydrol. Earth Syst. Sci., 22, 1351–1369, https://doi.org/10.5194/hess-22-1351-2018, https://doi.org/10.5194/hess-22-1351-2018, 2018
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In an effort to apply cloud-tolerant microwave data to satellite-based monitoring of evapotranspiration (ET), this study reports on an experiment where microwave-based land surface temperature is used as the key diagnostic input to a two-source energy balance method for the estimation of ET. Comparisons of this microwave ET with the conventional thermal infrared estimates show widespread agreement in spatial and temporal patterns from seasonal to inter-annual timescales over Africa and Europe.
Jonas Meier, Florian Zabel, and Wolfram Mauser
Hydrol. Earth Syst. Sci., 22, 1119–1133, https://doi.org/10.5194/hess-22-1119-2018, https://doi.org/10.5194/hess-22-1119-2018, 2018
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The following study extends existing irrigation maps based on official reports. The main idea was to extend the reported irrigated areas using agricultural suitability data and compare them with remote sensing information about plant conditions. The analysis indicates an increase in irrigated land by 18 % compared to the reported statistics. The additional areas are mainly identified within already known irrigated regions where irrigation is more dense than previously estimated.
Matthew F. McCabe, Matthew Rodell, Douglas E. Alsdorf, Diego G. Miralles, Remko Uijlenhoet, Wolfgang Wagner, Arko Lucieer, Rasmus Houborg, Niko E. C. Verhoest, Trenton E. Franz, Jiancheng Shi, Huilin Gao, and Eric F. Wood
Hydrol. Earth Syst. Sci., 21, 3879–3914, https://doi.org/10.5194/hess-21-3879-2017, https://doi.org/10.5194/hess-21-3879-2017, 2017
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We examine the opportunities and challenges that technological advances in Earth observation will present to the hydrological community. From advanced space-based sensors to unmanned aerial vehicles and ground-based distributed networks, these emergent systems are set to revolutionize our understanding and interpretation of hydrological and related processes.
Liangjing Zhang, Henryk Dobslaw, Tobias Stacke, Andreas Güntner, Robert Dill, and Maik Thomas
Hydrol. Earth Syst. Sci., 21, 821–837, https://doi.org/10.5194/hess-21-821-2017, https://doi.org/10.5194/hess-21-821-2017, 2017
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Global numerical models perform differently, as has been found in some model intercomparison studies, which mainly focused on components like evapotranspiration, soil moisture or runoff. We have applied terrestrial water storage that is estimated from a GRACE-based state-of-art post-processing method to validate four global numerical models and try to identify the advantages and deficiencies of a certain model. GRACE-based TWS demonstrates its additional benefits to improve the models in future.
Hylke E. Beck, Albert I. J. M. van Dijk, Vincenzo Levizzani, Jaap Schellekens, Diego G. Miralles, Brecht Martens, and Ad de Roo
Hydrol. Earth Syst. Sci., 21, 589–615, https://doi.org/10.5194/hess-21-589-2017, https://doi.org/10.5194/hess-21-589-2017, 2017
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MSWEP (Multi-Source Weighted-Ensemble Precipitation) is a new global terrestrial precipitation dataset with a high 3-hourly temporal and 0.25° spatial resolution. The dataset is unique in that it takes advantage of a wide range of data sources, including gauge, satellite, and reanalysis data, to obtain the best possible precipitation estimates at global scale. The dataset outperforms existing gauge-adjusted precipitation datasets.
Oliver López, Rasmus Houborg, and Matthew Francis McCabe
Hydrol. Earth Syst. Sci., 21, 323–343, https://doi.org/10.5194/hess-21-323-2017, https://doi.org/10.5194/hess-21-323-2017, 2017
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The study evaluated the spatial and temporal consistency of satellite-based hydrological products based on the water budget equation, including three global evaporation products. The products were spatially matched using spherical harmonics analysis. The results highlighted the difficulty in obtaining agreement between independent satellite products, even over regions with simple water budgets. However, imposing a time lag on water storage data improved results considerably.
Paul A. Levine, James T. Randerson, Sean C. Swenson, and David M. Lawrence
Hydrol. Earth Syst. Sci., 20, 4837–4856, https://doi.org/10.5194/hess-20-4837-2016, https://doi.org/10.5194/hess-20-4837-2016, 2016
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We demonstrate a new approach to assess the strength of feedbacks resulting from land–atmosphere coupling on decadal timescales. Our approach was tailored to enable evaluation of Earth system models (ESMs) using data from Earth observation satellites that measure terrestrial water storage anomalies and relevant atmospheric variables. Our results are consistent with previous work demonstrating that ESMs may be overestimating the strength of land surface feedbacks compared with observations.
Thomas R. H. Holmes, Christopher R. Hain, Martha C. Anderson, and Wade T. Crow
Hydrol. Earth Syst. Sci., 20, 3263–3275, https://doi.org/10.5194/hess-20-3263-2016, https://doi.org/10.5194/hess-20-3263-2016, 2016
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We test the cloud tolerance of two technologies to estimate land surface temperature (LST) from space: microwave (MW) and thermal infrared (TIR). Although TIR has slightly lower errors than MW with ground data under clear-sky conditions, it suffers increasing negative bias as cloud cover increases. In contrast, we find no direct impact of clouds on the accuracy and bias of MW-LST. MW-LST can therefore be used to improve TIR cloud screening and increase sampling in clouded regions.
Zhi Li, Yaning Chen, Yang Wang, and Gonghuan Fang
Hydrol. Earth Syst. Sci., 20, 2169–2178, https://doi.org/10.5194/hess-20-2169-2016, https://doi.org/10.5194/hess-20-2169-2016, 2016
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Global net primary production (NPP) slightly increased in 2000–2014. More than 64 % of vegetated land in the Northern Hemisphere (NH) showed increased NPP, while 60.3 % in Southern Hemisphere (SH) showed a decreasing trend. Vegetation greening and climate change promote rises of global evapotranspiration (ET). The increased rate of ET in the NH is faster than that in the SH. Meanwhile, global warming and vegetation greening accelerate evaporation in soil moisture. Continuation of these trends will likely exacerbate the risk of ecological drought.
M. Boudou, B. Danière, and M. Lang
Hydrol. Earth Syst. Sci., 20, 161–173, https://doi.org/10.5194/hess-20-161-2016, https://doi.org/10.5194/hess-20-161-2016, 2016
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This paper presents an appraisal of flood vulnerability of two French cities, Besançon and Moissac, which have been largely impacted by two ancient major floods (resp. in January 1910 and March 1930). An analysis of historical sources allows the mapping of land use and occupation within the flood extent of the two historical floods, both in past and present contexts. It gives an insight into the complexity of flood risk evolution, at a local scale.
S. Kotsuki and K. Tanaka
Hydrol. Earth Syst. Sci., 19, 4441–4461, https://doi.org/10.5194/hess-19-4441-2015, https://doi.org/10.5194/hess-19-4441-2015, 2015
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This study aims to develop a new global data set of a satellite-derived crop calendar (SACRA) and to reveal its advantages and disadvantages compared to other global products. The cultivation period of SACRA is identified from the time series of NDVI; therefore, SACRA considers current effects of human decisions and natural disasters. The difference between the estimated sowing dates and other existing products is less than 2 months (< 62 days) in most areas.
S. Siebert, M. Kummu, M. Porkka, P. Döll, N. Ramankutty, and B. R. Scanlon
Hydrol. Earth Syst. Sci., 19, 1521–1545, https://doi.org/10.5194/hess-19-1521-2015, https://doi.org/10.5194/hess-19-1521-2015, 2015
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We developed the historical irrigation data set (HID) depicting the spatio-temporal development of the area equipped for irrigation (AEI) between 1900 and 2005 at 5arcmin resolution.
The HID reflects very well the spatial patterns of irrigated land as shown on two historical maps for 1910 and 1960.
Global AEI increased from 63 million ha (Mha) in 1900 to 111 Mha in 1950 and 306 Mha in 2005. Mean aridity on irrigated land increased and mean natural river discharge decreased from 1900 to 1950.
B. Revilla-Romero, J. Thielen, P. Salamon, T. De Groeve, and G. R. Brakenridge
Hydrol. Earth Syst. Sci., 18, 4467–4484, https://doi.org/10.5194/hess-18-4467-2014, https://doi.org/10.5194/hess-18-4467-2014, 2014
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One of the main challenges in global hydrological modelling is the limited availability of observational data for calibration and model verification. The aim of this study is to test the potentials and constraints of the remote sensing signal of the Global Flood Detection System (GFDS) for converting the flood detection signal into river discharge values. This work also provides a first analysis of the local factors influencing the accuracy of discharge measurement as provided by this system.
T. R. H. Holmes, W. T. Crow, and C. Hain
Hydrol. Earth Syst. Sci., 17, 3695–3706, https://doi.org/10.5194/hess-17-3695-2013, https://doi.org/10.5194/hess-17-3695-2013, 2013
A. Loew, T. Stacke, W. Dorigo, R. de Jeu, and S. Hagemann
Hydrol. Earth Syst. Sci., 17, 3523–3542, https://doi.org/10.5194/hess-17-3523-2013, https://doi.org/10.5194/hess-17-3523-2013, 2013
Cited articles
Albergel, C., Munier, S., Leroux, D. J., Dewaele, H., Fairbairn, D., Barbu, A. L., Gelati, E., Dorigo, W., Faroux, S., Meurey, C., Le Moigne, P., Decharme, B., Mahfouf, J.-F., and Calvet, J.-C.: Sequential assimilation of satellite-derived vegetation and soil moisture products using SURFEX_v8.0: LDAS-Monde assessment over the Euro-Mediterranean area, Geosci. Model Dev., 10, 3889–3912, https://doi.org/10.5194/gmd-10-3889-2017, 2017. a
Albergel, C., Munier, S., Bocher, A., Bonan, B., Zheng, Y., Draper, C., Leroux, D., and Calvet, J.-C.: LDAS-Monde sequential assimilation of satellite derived observations applied to the contiguous US: An ERA-5 driven reanalysis of the land surface variables, Remote Sens., 10, 1627, https://doi.org/10.3390/rs10101627, 2018. a, b
Andela, N., Liu, Y. Y., van Dijk, A. I. J. M., de Jeu, R. A. M., and McVicar, T. R.: Global changes in dryland vegetation dynamics (1988–2008) assessed by satellite remote sensing: comparing a new passive microwave vegetation density record with reflective greenness data, Biogeosciences, 10, 6657–6676, https://doi.org/10.5194/bg-10-6657-2013, 2013. a
Anderson, M., Norman, J., Mecikalski, J., Otkin, J., and Kustas, W.: A
climatological study of evapotranspiration and moisture stress across the
continental U.S. based on thermal remote sensing: I. Model formulation, J.
Geophys. Res., 112, D11112, https://doi.org/10.1029/2006JD007506, 2007. a
Anderson, M. C., Allen, R. G., Morse, A., and Kustas, W. P.: Use of Landsat
thermal imagery in monitoring evapotranspiration and managing water
resources, Remote Sens. Environ., 122, 50–65,
https://doi.org/10.1016/j.rse.2011.08.025, 2012. a
Barbu, A. L., Calvet, J.-C., Mahfouf, J.-F., Albergel, C., and Lafont, S.: Assimilation of Soil Wetness Index and Leaf Area Index into the ISBA-A-gs land surface model: grassland case study, Biogeosciences, 8, 1971–1986, https://doi.org/10.5194/bg-8-1971-2011, 2011. a
Barbu, A. L., Calvet, J.-C., Mahfouf, J.-F., and Lafont, S.: Integrating ASCAT surface soil moisture and GEOV1 leaf area index into the SURFEX modelling platform: a land data assimilation application over France, Hydrol. Earth Syst. Sci., 18, 173–192, https://doi.org/10.5194/hess-18-173-2014, 2014. a
Chan, S., Bindlish, R., O'Neill, P., Jackson, T., Njoku, E., Dunbar, S.,
Chaubell, J., Piepmeier, J., Yueh, S., Entekhabi, D., Colliander, A., Chen,
F., Cosh, M., Caldwell, T., Walker, J., Berg, A., McNairn, H., Thibeault, M.,
Martínez-Fernández, J., Uldall, F., Seyfried, M., Bosch, D., Starks, P.,
Collins, C. H., Prueger, J., van der Velde, R., Asanuma, J., Palecki, M.,
Small, E., Zreda, M., Calvet, J., Crow, W., and Kerr, Y.: Development and
assessment of the SMAP enhanced passive soil moisture product, Remote Sens. Environ., 204, 931–941,
https://doi.org/10.1016/j.rse.2017.08.025, 2018. a
Chaubell, M. J., Yueh, S. H., Dunbar, R. S., Colliander, A., Chen,
F., Chan, S. K., Entekhabi, D., Bindlish, R., O'Neill, P. E.,
Asanuma, J., Berg, A. A., Bosch, D. D., Caldwell, T., Cosh, M. H.,
Collins, C. H., Martínez-Fernández, J., Seyfried, M., Starks,
P. J., Su, Z., Thibeault, M., and Walker, J.: Improved SMAP
Dual-Channel Algorithm for the Retrieval of Soil Moisture, IEEE T. Geosci. Remote, 1–12, 2020. a
der Schalie, R., Kerr, Y., Wigneron, J., Rodríguez-Fernández, N., Al-Yaari,
A., and Jeu, R.: Global SMOS Soil Moisture Retrievals from The Land
Parameter Retrieval Model, Int. J. Appl. Earth
Obs., 45, 125–134,
https://doi.org/10.1016/j.jag.2015.08.005 2016. a
Dirmeyer, P., Gao, X., Zhao, M., Guo, Z., Oki, T., and Hanasaki, N.: GSWP-2:
Multimodel analysis and implications for our perception of the land surface,
B. Am. Meteorol. Soc., 87, 1381–1397, https://doi.org/10.1175/BAMS-87-10-1381,
2006. a
Dorigo, W. A., Wagner, W., Hohensinn, R., Hahn, S., Paulik, C., Xaver, A., Gruber, A., Drusch, M., Mecklenburg, S., van Oevelen, P., Robock, A., and Jackson, T.: The International Soil Moisture Network: a data hosting facility for global in situ soil moisture measurements, Hydrol. Earth Syst. Sci., 15, 1675–1698, https://doi.org/10.5194/hess-15-1675-2011, 2011. a
Dorigo, W. A., Xaver, A., Vreugdenhil, M., Gruber, A., Hegyiova, A.,
Sanchis-Dufau, A., Zamojski, D., Cordes, C., Wagner, W., and Drusch, M.:
Global Automated Quality Control of In Situ Soil Moisture Data from the
International Soil Moisture Network, Vadose Zone Journal, 12,
1–21, https://doi.org/10.2136/vzj2012.0097, 2013. a
Draper, C., Reichle, R., De Lannoy, G., and Liu, Q.: Assimilation of passive
and active microwave soil moisture retrievals, Geophys. Res. Lett.,
39, L04401, https://doi.org/10.1029/2011GL050655, 2012. a
Entekhabi, D., Njoku, E., O'Neill, P., Kellogg, K., Crow, W. T., Edelstein, W., Entin, J., Goodman, S., Jackson, T., Johnson, J., Kimball, J., Piepmeier, J., Koster, R., Martin, N., McDonald, K., Moghaddam, M., Moran, S., Reichle, R., Shi, J., Spencer, M., Thurman, S., Tsang, L., and Van Zyl, J.: The Soil
Moisture Active Passive (SMAP) Mission, P. IEEE, 98,
704–716, https://doi.org/10.1109/jproc.2010.2043918, 2010. a
Fang, H., Jiang, C., Li, W., Wei, S., Baret, F., Chen, J. M., Garcia-Haro, J., Liang, S., Liu, R., Myneni, R. B., Pinty, B., Xiao, Z., and Zhu, Z.:
Characterization and intercomparison of global moderate resolution leaf area
index (LAI) products: Analysis of climatologies and theoretical
uncertainties, J. Geophys. Res.-Biogeo., 118,
529–548, https://doi.org/10.1002/jgrg.20051, 2013. a
Fox, A. M., Hoar, T. J., Anderson, J. L., Arellano, A. F., Smith, W. K.,
Litvak, M. E., MacBean, N., Schimel, D. S., and Moore, D. J. P.: Evaluation
of a Data Assimilation System for Land Surface Models Using CLM4.5, J. Adv. Model. Earth Syst., 10, 2471–2494,
https://doi.org/10.1029/2018MS001362, 2018. a
Getirana, A., Boone, A., Yamazaki, D., Decharme, B., Papa, F., and Mognard, N.: The Hydrological Modeling and Analysis Platform (HyMAP): Evaluation in the Amazon Basin, J. Hydrometeor., 13, 1641–1665, https://doi.org/10.1175/JHM-D-12-021.1,
2012. a
Goetz, A., Vane, G., Solomon, J., and Rock, B.: Imaging specdtrometry for
earth remote sensing, Science, 228, 1147–1153, 1985. a
Grant, J., Wigneron, J.-P., Jeu, R. D., Lawrence, H., Mialon, A., Richaume, P., Bitar, A. A., Drusch, M., van Marle, M., and Kerr, Y.: Comparison of SMOS and AMSR-E vegetation optical depth to four MODIS-based vegetation indices, Remote Sens. Environ., 172, 87–100,
https://doi.org/10.1016/j.rse.2015.10.021, 2016. a
Guanter, L., Zhang, Y., Jung, M., Joiner, J., Voigt, M., Berry, J. A.,
Frankenberg, C., Huete, A. R., Zarco-Tejada, P., Lee, J.-E., Moran, M. S.,
Ponce-Campos, G., Beer, C., Camps-Valls, G., Buchmann, N., Gianelle, D.,
Klumpp, K., Cescatti, A., Baker, J. M., and Griffis, T. J.: Global and
time-resolved monitoring of crop photosynthesis with chlorophyll
fluorescence, P. Natl. Acad. Sci. USA, 111,
E1327–E1333, https://doi.org/10.1073/pnas.1320008111, 2014. a
Hain, C., Crow, W., Anderson, M., and Mecikalski, J.: An ensemble Kalman
filter dual assimilation of thermal infrared and microwave satellite
observations of soil moisture into the Noah land surface model, Water
Resour. Res., 48, W11517, https://doi.org/10.1029/2011WR011268, 2012. a
Hain, C. R., Crow, W. T., Anderson, M. C., and Yilmaz, M. T.: Diagnosing
Neglected Soil Moisture Source-Sink Processes via a Thermal Infrared-Based
Two-Source Energy Balance Model, J. Hydrometeorol., 16,
1070–1086, https://doi.org/10.1175/JHM-D-14-0017.1, 2015. a
Hall, D. K., Riggs, G. A., Foster, J. L., and Kumar, S. V.: Development and
evaluation of a cloud-gap-filled MODIS daily snow-cover product, Remote
Sens. Environ., 114, 496–503,
https://doi.org/10.1016/j.rse.2009.10.007, 2010. a
Hollinger, S. E. and Isard, S. A.: A Soil Moisture Climatology of Illinois,
J. Climate, 7, 822–833,
https://doi.org/10.1175/1520-0442(1994)007<0822:ASMCOI>2.0.CO;2, 1994. a
Holmes, T. R. H., Hain, C. R., Anderson, M. C., and Crow, W. T.: Cloud tolerance of remote-sensing technologies to measure land surface temperature, Hydrol. Earth Syst. Sci., 20, 3263–3275, https://doi.org/10.5194/hess-20-3263-2016, 2016. a
Houborg, R., Fisher, J. B., and Skidmore, A. K.: Advances in remote sensing of vegetation function and traits, Int. J. Appl. Earth
Obs. Geoinformation, 43, 1–6,
https://doi.org/10.1016/j.jag.2015.06.001, 2015. a
Huete, A., Liu, H., Batchily, K., and Van Leeuwen, W.: A comparison of
vegetation indices over a global set of TM images for EOS-MODIS, Remote
Sens. Environ., 59, 440–451, 1997. a
Jackson, T. J., Cosh, M. H., Bindlish, R., Starks, P. J., Bosch,
D. D., Seyfried, M., Goodrich, D. C., Moran, M. S., and Du, J.:
Validation of Advanced Microwave Scanning Radiometer Soil Moisture Products,
IEEE T. Geosci. Remote, 48, 4256–4272, 2010. a
Joiner, J., Yoshida, Y., Vasilkov, A., Schaefer, K., Jung, M., Guanter, L.,
Zhang, Y., Garrity, S., Middleton, E., Huemmrich, K., Gu, L., and Marchesini,
L. B.: The seasonal cycle of satellite chlorophyll fluorescence observations
and its relationship to vegetation phenology and ecosystem atmosphere carbon
exchange, Remote Sens. Environ., 152, 375–391,
https://doi.org/10.1016/j.rse.2014.06.022, 2014. a
Jones, M. O., Kimball, J. S., Jones, L. A., and McDonald, K. C.: Satellite
passive microwave detection of North America start of season, Remote Sens. Environ., 123, 324–333, 2012. a
Jung, M., Reichstein, M., and Bondeau, A.: Towards global empirical upscaling of FLUXNET eddy covariance observations: validation of a model tree ensemble approach using a biosphere model, Biogeosciences, 6, 2001–2013, https://doi.org/10.5194/bg-6-2001-2009, 2009. a
Jung, M., Reichstein, M., Schwalm, C., Huntingford, C., Sitch, S.,
Ahlström, A., Arneth, A., Camps-Valls, G., Ciais, P., Friedlingstein, P.,
Gans, F., Ichii, K., Jain, A., Kato, E., Papale, D., Poulter, B., Raduly, B.,
Rödenbeck, C., Tramontana, G., Viovy, N., Wang, Y.-P., Weber, U., Zaehle,
S., and Zeng, N.: Compensatory water effects link yearly global land CO2
sink changes to temperature, Nature, 541, 516–520,
https://doi.org/10.1038/nature20780, 2017. a
Knipling, E. B.: Physical and physiological basis for the reflectance of
visible and near-infrared radiation from vegetation, Remote Sens.
Environ., 1, 155–159,
https://doi.org/10.1016/S0034-4257(70)80021-9,
1970. a
Konings, A. G. and Gentine, P.: Global variations in ecosystem-scale
isohydricity, Glob. Change Biol., 23, 891–905, 2017. a
Konings, A. G., Piles, M., Rotzer, K., McColl, K. A., Chan, S. K., and
Entekhabi, D.: Vegetation optical depth and scattering albedo retrieval
using time series of dual-polarized L-band radiometer observations, Remote
Sens. Environ., 172, 178–189,
https://doi.org/10.1016/j.rse.2015.11.009, 2016. a
Konings, A. G., Piles, M., Das, N., and Entekhabi, D.: L-band vegetation
optical depth and effective scattering albedo estimation from SMAP, Remote
Sens. Environ., 198, 460–470,
https://doi.org/10.1016/j.rse.2017.06.037, 2017. a, b, c
Konings, A. G., Rao, K., and Steele-Dunne, S. C.: Macro to micro: microwave
remote sensing of plant water content for physiology and ecology, New
Phytol., 223, 1166–1172, https://doi.org/10.1111/nph.15808, 2019. a
Koster, R., Guo, Z., Dirmeyer, P., Yang, R., Mitchell, K., and Puma, M.: On the nature of soil moisture in land surface models, J. Climate, 22, 4322–4335, https://doi.org/10.1175/2009JCLI2832.1, 2009. a
Kumar, L. and Mutanga, O.: Remote sensing of above-ground biomass, Remote
Sensing, 9, 935, https://doi.org/10.3390/rs9090935, 2017. a
Kumar, S. V.: Land information system, Zenodo, https://doi.org/10.5281/zenodo.3883907, 2020. a
Kumar, S. V., Peters-Lidard, C., Tian, T., Houser, P., Geiger, J., Olden, S.,
Lighty, L., Eastman, J., Doty, B., Dirmeyer, P., Adams, J., Mitchell, K.,
Wood, E., and Sheffield, J.: Land Information System: An interoperable
framework for high resolution land surface modeling, Environ. Model. Softw., 21, 1402–1415, 2006. a
Kumar, S. V., Peters-Lidard, C. D., Santanello, J., Harrison, K., Liu, Y., and Shaw, M.: Land surface Verification Toolkit (LVT) – a generalized framework for land surface model evaluation, Geosci. Model Dev., 5, 869–886, https://doi.org/10.5194/gmd-5-869-2012, 2012. a
Kumar, S. V., Peters-Lidard, C. D., Mocko, D., Reichle, R., Liu, Y., Arsenault, K. R., Xia, Y., Ek, M., Riggs, G., Livneh, B., and Cosh, M.: Assimilation of Remotely Sensed Soil Moisture and Snow Depth Retrievals for Drought Estimation, J. Hydrometeorol., 15, 2446–2469,
https://doi.org/10.1175/JHM-D-13-0132.1, 2014. a, b, c, d
Kumar, S. V., Peters-Lidard, C. D., Santanello, J. A., Reichle, R. H., Draper, C. S., Koster, R. D., Nearing, G., and Jasinski, M. F.: Evaluating the utility of satellite soil moisture retrievals over irrigated areas and the ability of land data assimilation methods to correct for unmodeled processes, Hydrol. Earth Syst. Sci., 19, 4463–4478, https://doi.org/10.5194/hess-19-4463-2015, 2015. a, b
Kumar, S. V., Holmes, T., Mocko, D. M., Wang, S., and Peters-Lidard, C.:
Attribution of Flux Partitioning Variations between Land Surface Models over
the Continental U.S., Remote Sensing, 10, 751, https://doi.org/10.3390/rs10050751, 2018. a, b
Kumar, S. V., Jasinski, M., Mocko, D. M., Rodell, M., Borak, J., Li, B.,
Beaudoing, H. K., and Peters-Lidard, C. D.: NCA-LDAS Land Analysis:
Development and Performance of a Multisensor, Multivariate Land Data
Assimilation System for the National Climate Assessment, J.
Hydrometeorol., 20, 1571–1593, https://doi.org/10.1175/JHM-D-17-0125.1,
2019a. a, b, c
Kumar, S. V., Mocko, D. M., Wang, S., Peters-Lidard, C. D., and Borak, J.:
Assimilation of remotely sensed Leaf Area Index into the Noah-MP land
surface model: Impacts on water and carbon fluxes and states over the
Continental U.S., J. Hydrometeorol., 20, 1359–1377,
https://doi.org/10.1175/JHM-D-18-0237.1, 2019b. a, b, c, d, e, f, g, h, i, j, k, l, m, n, o, p, q
Landerer, F. and Swenson, S.: Accuracy of scaled GRACE terrestrial water
storage estimates, Water Resour. Res., 48, W04531, https://doi.org/10.1029/2011WR011453,
2012. a
Liao, Y., Gai, Y., Fan, W., Xu, X., Yan, B., and Liu, Y.:
Validation methods of LAI products based on scaling effect, in: 2012 IEEE
International Geoscience and Remote Sensing Symposium, 22–27 July 2012, Munich, Germany, 1692–1694,
https://doi.org/10.1109/IGARSS.2012.6351200, 2012. a
Lievens, H., Reichle, R. H., Liu, Q., De Lannoy, G. J. M., Dunbar, R. S., Kim,
S. B., Das, N. N., Cosh, M., Walker, J. P., and Wagner, W.: Joint Sentinel-1
and SMAP data assimilation to improve soil moisture estimates, Geophys.
Res. Lett., 44, 6145–6153, https://doi.org/10.1002/2017GL073904, 2017. a
Liu, Q., Reichle, R., Bindlish, R., Cosh, M., Crow, W., de Jeu, R., De Lannoy,
G. J. M., Huffman, G., and Jackson, T.: The contributions of precipitation
and soil moisture observations to the skill of soil moisture estimates in a
land data assimilation system, J. Hydrometeorol., 12, 750–765,
https://doi.org/10.1175/JHM-D-10-05000, 2011. a
Liu, Y. Y., de Jeu, R. A., McCabe, M. F., Evans, J. P., and van Dijk, A. I.:
Global long-term passive microwave satellite-based retrievals of vegetation
optical depth, Geophys. Res. Lett., 38, L18402, https://doi.org/10.1029/2011GL048684, 2011. a
Liu, Y. Y., Evans, J., McCabe, M., de Jeu, R., van Dijk, A., Dolman, A., and
Saizen, I.: Changing climate and overgrazing are decimating Mongolian
steppes, PloS one, 8, e57599, https://doi.org/10.1371/journal.pone.0057599, 2013. a
Liu, Y. Y., Van Dijk, A. I., De Jeu, R. A., Canadell, J. G., McCabe, M. F.,
Evans, J. P., and Wang, G.: Recent reversal in loss of global terrestrial
biomass, Nature Climate Change, 5, 470–474, https://doi.org/10.1038/nclimate2581, 2015. a
Martens, B., Miralles, D., Lievens, H., Fernández-Prieto, D., and Verhoest,
N.: Improving terrestrial evaporation estimates over continental Australia
through assimilation of SMOS soil moisture, Int. J. Appl.
Earth Obs., 48, 146–162,
https://doi.org/10.1016/j.jag.2015.09.012, 2016. a
Meesters, A. G. C. A., De Jeu, R. A. M., and Owe, M.: Analytical
derivation of the vegetation optical depth from the microwave polarization
difference index, IEEE Geosci. Remote S., 2, 121–123,
https://doi.org/10.1109/LGRS.2005.843983, 2005. a
Mo, T., Choudhury, B. J., Schmugge, T. J., Wang, J. R., and Jackson, T. J.: A
model for microwave emission from vegetation-covered fields, J.
Geophys. Res.-Oceans, 87, 11229–11237,
https://doi.org/10.1029/JC087iC13p11229,
1982. a
Moesinger, L., Dorigo, W., de Jeu, R., van der Schalie, R., Scanlon, T., Teubner, I., and Forkel, M.: The global long-term microwave Vegetation Optical Depth Climate Archive (VODCA), Earth Syst. Sci. Data, 12, 177–196, https://doi.org/10.5194/essd-12-177-2020, 2020. a
Myneni, R., Hoffman, S., Knyazikhin, Y., Privette, J., Glassy, J., Tian, Y.,
Wang, Y., Song, X., Zhang, Y., Smith, G., Lotsch, A., Friedl, M., Morisette,
J., Votava, P., Nemani, R., and Running, S.: Global products of vegetation
leaf area and fraction absorbed PAR from year one of MODIS data, Remote
Sens. Environ., 83, 214–231,
https://doi.org/10.1016/S0034-4257(02)00074-3, 2002. a
Myneni, R., Knyazikhin, Y., and Shabanov, N.: Leaf area index and fraction of
absorbed PAR products from Terra and Aqua MODIS sensors: Analysis, Validation
and Refinement, in: Land remote sensing and global
environmental change, vol. 11, 603–633, Springer, New York, NY, USA, 2011. a
Niu, G.-Y., Yang, Z.-L., Mitchell, K., Chen, F., Ek, M., Barlage, M., Kumar,
A., Manning, K., Niyogi, D., Rosero, E., Tewari, M., and Xia, Y.: The
community Noah land surface model with multiparameterization options
(Noah-MP): 1. Model description and evaluation with local-scale
measurements, J. Geophys. Res.-Atmos., 116, D12109, https://doi.org/10.1029/2010JD015139,
2011. a
Njoku, E. G. and Entekhabi, D.: Passive microwave remote sensing of soil
moisture, J. Hydrol., 184, 101–129,
https://doi.org/10.1016/0022-1694(95)02970-2, 1996. a
Njoku, E. G., Ashcroft, P., Chan, T. K., and Li, L.: Global survey and
statistics of radio-frequency interference in AMSR-E land observations, IEEE
T. Geosci. Remote, 43, 938–947,
https://doi.org/10.1109/TGRS.2004.837507, 2005. a
Owe, M., de Jeu, R., and Walker, J.: A methodology for surface soil
moisture and vegetation optical depth retrieval using the microwave
polarization difference index, IEEE T. Geosci. Remote, 39, 1643–1654, https://doi.org/10.1109/36.942542, 2001. a
Owe, M., de Jeu, R., and Holmes, T. : Multi-sensor historical climatology
of satellite-derived global land surface moisture, J. Geophys.
Res., 13, F01002, https://doi.org/10.1029/2007JF000769, 2008. a, b
Parinussa, R. M., Holmes, T. R. H., Yilmaz, M. T., and Crow, W. T.: The impact of land surface temperature on soil moisture anomaly detection from passive microwave observations, Hydrol. Earth Syst. Sci., 15, 3135–3151, https://doi.org/10.5194/hess-15-3135-2011, 2011. a
Peters-Lidard, C. D., Kumar, S. V., Mocko, D. M., and Tian, Y.: Estimating
evapotranspiration with land data assimilation systems, Hydrol.
Process., 25, 3979–3992, https://doi.org/10.1002/hyp.8387, 2011. a
Price, J. C. and Bausch, W. C.: Leaf area index estimation from visible and
near-infrared reflectance data, Remote Sens. Environ., 52, 55–65,
1995. a
Reichle, R. and Koster, R.: Bias reduction in short records of satellite soil
moisture, Geophys. Res. Lett., 31, L19501, https://doi.org/10.1029/2004GL020938, 2004. a
Reichle, R., McLaughlin, D., and Entekhabi, D.: Hydrologic data assimilation
with the ensemble Kalman Filter, Mon. Weather Rev., 130, 103–114,
2002. a
Reichle, R., Koster, R., Liu, P. and. Mahanama, S., Njoku, E., and Owe, M.:
Comparison and assimilation of global soil moisture retrievals from the
Advanced Microwave Scanning Radiometer for the Earth Observing System
(AMSR-E) and the Scanning Multichannel Microwave Radiometer (SMMR), J. Geophys. Res.-Atmos., 112, D09108, https://doi.org/10.1029/2006JD008033, 2007. a
Sabater, J., Rudiger, C., Calvet, J.-C., Fritz, N., Jarlan, L., and Kerr, Y.:
Joint assimilation of surface soil moisture and LAI observations into a land
surface model, Agr. Forest Meteorol., 148, 1362–1373, https://doi.org/10.1016/j.agrformet.2008.04.003, 2008. a
Santanello, J. A., Kumar, S. V., Peters-Lidard, C. D., and Lawston, P. M.:
Impact of Soil Moisture Assimilation on Land Surface Model Spinup and
Coupled Land–Atmosphere Prediction, J. Hydrometeorol. 17,
517–540, https://doi.org/10.1175/JHM-D-15-0072.1, 2016. a
Smith, W. K., Fox, A. M., MacBean, N., Moore, D. J. P., and Parazoo, N. C.:
Constraining estimates of terrestrial carbon uptake: new opportunities using
long-term satellite observations and data assimilation, New Phytol.,
225, 105–112, https://doi.org/10.1111/nph.16055, 2020. a
Teubner, I. E., Forkel, M., Jung, M., Liu, Y. Y., Miralles, D. G., Parinussa,
R., van der Schalie, R., Vreugdenhil, M., Schwalm, C. R., Tramontana, G.,
Camps-Valls, G., and Dorigo, W. A.: Assessing the relationship between
microwave vegetation optical depth and gross primary production,
Int. J. Appl. Earth Obs., 65, 79–91, https://doi.org/10.1016/j.jag.2017.10.006,
2018. a
Teubner, I. E., Forkel, M., Camps-Valls, G., Jung, M., Miralles, D. G.,
Tramontana, G., van der Schalie, R., Vreugdenhil, M., Mösinger, L., and
Dorigo, W. A.: A carbon sink-driven approach to estimate gross primary
production from microwave satellite observations, Remote Sens.
Environ., 229, 100–113,
https://doi.org/10.1016/j.rse.2019.04.022,
2019. a
Tramontana, G., Jung, M., Schwalm, C. R., Ichii, K., Camps-Valls, G., Ráduly, B., Reichstein, M., Arain, M. A., Cescatti, A., Kiely, G., Merbold, L., Serrano-Ortiz, P., Sickert, S., Wolf, S., and Papale, D.: Predicting carbon dioxide and energy fluxes across global FLUXNET sites with regression algorithms, Biogeosciences, 13, 4291–4313, https://doi.org/10.5194/bg-13-4291-2016, 2016. a
Tucker, C. J., Pinzon, J. E., Brown, M. E., Slayback, D. A., Pak, E. W.,
Mahoney, R., Vermote, E. F., and Saleous, N. E.: An extended AVHRR 8-km NDVI
dataset compatible with MODIS and SPOT vegetation NDVI data, Int.
J. Remote Sens., 26, 4485–4498, https://doi.org/10.1080/01431160500168686,
2005. a
Van der Schalie, R., De Jeu, R., Parinussa, R., Rodríguez-Fernández, N.,
Kerr, Y., Al-Yaari, A., Wigneron, J.-P., and Drusch, M.: The Effect of Three
Different Data Fusion Approaches on the Quality of Soil Moisture Retrievals
from Multiple Passive Microwave Sensors, Remote Sensing, 10, 107,
https://doi.org/10.3390/rs10010107, 2018.
a
van Marle, M. J. E., van der Werf, G. R., de Jeu, R. A. M., and Liu, Y. Y.: Annual South American forest loss estimates based on passive microwave remote sensing (1990–2010), Biogeosciences, 13, 609–624, https://doi.org/10.5194/bg-13-609-2016, 2016. a
Vreugdenhil, M., Dorigo, W. A., Wagner, W., de Jeu, R. A. M., Hahn,
S., and van Marle, M. J. E.: Analyzing the Vegetation Parameterization in
the TU-Wien ASCAT Soil Moisture Retrieval, IEEE T. Geosci. Remote, 54, 3513–3531, 2016. a
Vreugdenhil, M., Hahn, S., Melzer, T., Bauer-Marschallinger, B.,
Reimer, C., Dorigo, W. A., and Wagner, W.: Assessing Vegetation
Dynamics Over Mainland Australia With Metop ASCAT, IEEE J. Sel.
Top. Appl., 10, 2240–2248,
2017. a
Xia, Y., Mitchell, K., Ek, M., Sheffield, J., Cosgrove, B., Wood, E., Luo, L., Alonge, C., Wei, H., Meng, J., Livneh, B., Lettenmaier, D.and Koren, V.,
Duan, Q., K., M., Fan, Y., and Mocko, D.: Continental-scale water and energy
flux analysis and validation for the North American Land Data Assimilation
System Project Phase 2 (NLDAS-2), Part 1: Comparison Analysis and Application
of Model Products, J. Geophys. Res.-Atmos., 117, D03109,
https://doi.org/10.1029/2011JD016048, 2012. a
Xiao, Z., Liang, S., Wang, J., Xiang, Y., Zhao, X., and Song, J.:
Long-Time-Series Global Land Surface Satellite Leaf Area Index Product
Derived From MODIS and AVHRR Surface Reflectance, IEEE T.
Geosci. Remote, 54, 5301–5318,
https://doi.org/10.1109/TGRS.2016.2560522, 2016. a, b
Yang, Z.-L., Niu, G.-Y., Mitchell, K., Chen, F., Ek, M., Barlage, M.,
Longuevergne, L., Manning, K., Niyogi, D., Tewari, M., and Xia, Y.: The
community Noah land surface model with multiparameterization options
(Noah-MP): 2. Evaluation over global river basins, J. Geophys.
Res.-Atmos., 116, D12110, https://doi.org/10.1029/2010JD015140,
2011. a
Zheng, G. and Moskal, L.: Retrieving Leaf Area Index (LAI) Using Remote
Sensing: Theories, Methods and Sensors, Sensors, 9, 2719–2745,
https://doi.org/10.3390/s90402719, 2009. a
Short summary
Vegetation optical depth (VOD) is a byproduct of the soil moisture retrieval from passive microwave instruments. This study demonstrates that VOD information can be utilized for improving land surface water budget and carbon conditions through data assimilation.
Vegetation optical depth (VOD) is a byproduct of the soil moisture retrieval from passive...
