Articles | Volume 28, issue 11
https://doi.org/10.5194/hess-28-2357-2024
© Author(s) 2024. 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-28-2357-2024
© Author(s) 2024. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Research article
| 
05 Jun 2024
Research article |
| 05 Jun 2024
| 05 Jun 2024
Machine learning and global vegetation: random forests for downscaling and gap filling
Barry van Jaarsveld
CORRESPONDING AUTHOR
Department of Physical Geography, Utrecht University, Princetonlaan 8a, Utrecht, the Netherlands
Sandra M. Hauswirth
Department of Physical Geography, Utrecht University, Princetonlaan 8a, Utrecht, the Netherlands
Niko Wanders
Department of Physical Geography, Utrecht University, Princetonlaan 8a, Utrecht, the Netherlands
Related authors
Barry van Jaarsveld, Niko Wanders, Edwin H. Sutanudjaja, Jannis Hoch, Bram Droppers, Joren Janzing, Rens L. P. H. van Beek, and Marc F. P. Bierkens
Earth Syst. Dynam., 16, 29–54, https://doi.org/10.5194/esd-16-29-2025, https://doi.org/10.5194/esd-16-29-2025, 2025
Short summary
Short summary
Policy makers use global hydrological models to develop water management strategies and policies. However, it would be better if these models provided information at higher resolution. We present a first-of-its-kind, truly global hyper-resolution model and show that hyper-resolution brings about better estimates of river discharge, and this is especially true for smaller catchments. Our results also suggest that future hyper-resolution models need to include more detailed land cover information.
Joren Janzing, Niko Wanders, Marit van Tiel, Barry van Jaarsveld, Dirk Nikolaus Karger, and Manuela Irene Brunner
EGUsphere, https://doi.org/10.5194/egusphere-2024-3072, https://doi.org/10.5194/egusphere-2024-3072, 2024
Short summary
Short summary
Process representation in hyper-resolution large-scale hydrological models (LHM) limits model performance, particularly in mountain regions. Here, we update mountain process representation in an LHM and compare different meteorological forcing products. Structural and parametric changes in snow, glacier and soil processes improve discharge simulations, while meteorological forcing remains a major control on model performance. Our work can guide future development of LHMs.
Hannes Müller Schmied, Simon Newland Gosling, Marlo Garnsworthy, Laura Müller, Camelia-Eliza Telteu, Atiq Kainan Ahmed, Lauren Seaby Andersen, Julien Boulange, Peter Burek, Jinfeng Chang, He Chen, Lukas Gudmundsson, Manolis Grillakis, Luca Guillaumot, Naota Hanasaki, Aristeidis Koutroulis, Rohini Kumar, Guoyong Leng, Junguo Liu, Xingcai Liu, Inga Menke, Vimal Mishra, Yadu Pokhrel, Oldrich Rakovec, Luis Samaniego, Yusuke Satoh, Harsh Lovekumar Shah, Mikhail Smilovic, Tobias Stacke, Edwin Sutanudjaja, Wim Thiery, Athanasios Tsilimigkras, Yoshihide Wada, Niko Wanders, and Tokuta Yokohata
Geosci. Model Dev., 18, 2409–2425, https://doi.org/10.5194/gmd-18-2409-2025, https://doi.org/10.5194/gmd-18-2409-2025, 2025
Short summary
Short summary
Global water models contribute to the evaluation of important natural and societal issues but are – as all models – simplified representation of reality. So, there are many ways to calculate the water fluxes and storages. This paper presents a visualization of 16 global water models using a standardized visualization and the pathway towards this common understanding. Next to academic education purposes, we envisage that these diagrams will help researchers, model developers, and data users.
Jennie C. Steyaert, Edwin Sutanudjaja, Marc Bierkens, and Niko Wanders
EGUsphere, https://doi.org/10.5194/egusphere-2024-3658, https://doi.org/10.5194/egusphere-2024-3658, 2025
Short summary
Short summary
Using machine learning techniques and remotely sensed reservoir data, we develop a workflow to derive reservoir storage bounds. We put these bounds in a global hydrologic model, PCR-GLOBWB 2, and evaluate the difference between generalized operations (the schemes typically in global models) and this data derived method. We find that modelled storage is more accurate in the data derived operations. We also find that generalized operations over estimate storage and can underestimate water gaps.
Barry van Jaarsveld, Niko Wanders, Edwin H. Sutanudjaja, Jannis Hoch, Bram Droppers, Joren Janzing, Rens L. P. H. van Beek, and Marc F. P. Bierkens
Earth Syst. Dynam., 16, 29–54, https://doi.org/10.5194/esd-16-29-2025, https://doi.org/10.5194/esd-16-29-2025, 2025
Short summary
Short summary
Policy makers use global hydrological models to develop water management strategies and policies. However, it would be better if these models provided information at higher resolution. We present a first-of-its-kind, truly global hyper-resolution model and show that hyper-resolution brings about better estimates of river discharge, and this is especially true for smaller catchments. Our results also suggest that future hyper-resolution models need to include more detailed land cover information.
Joren Janzing, Niko Wanders, Marit van Tiel, Barry van Jaarsveld, Dirk Nikolaus Karger, and Manuela Irene Brunner
EGUsphere, https://doi.org/10.5194/egusphere-2024-3072, https://doi.org/10.5194/egusphere-2024-3072, 2024
Short summary
Short summary
Process representation in hyper-resolution large-scale hydrological models (LHM) limits model performance, particularly in mountain regions. Here, we update mountain process representation in an LHM and compare different meteorological forcing products. Structural and parametric changes in snow, glacier and soil processes improve discharge simulations, while meteorological forcing remains a major control on model performance. Our work can guide future development of LHMs.
Riccardo Biella, Ansastasiya Shyrokaya, Monica Ionita, Raffaele Vignola, Samuel Sutanto, Andrijana Todorovic, Claudia Teutschbein, Daniela Cid, Maria Carmen Llasat, Pedro Alencar, Alessia Matanó, Elena Ridolfi, Benedetta Moccia, Ilias Pechlivanidis, Anne van Loon, Doris Wendt, Elin Stenfors, Fabio Russo, Jean-Philippe Vidal, Lucy Barker, Mariana Madruga de Brito, Marleen Lam, Monika Bláhová, Patricia Trambauer, Raed Hamed, Scott J. McGrane, Serena Ceola, Sigrid Jørgensen Bakke, Svitlana Krakovska, Viorica Nagavciuc, Faranak Tootoonchi, Giuliano Di Baldassarre, Sandra Hauswirth, Shreedhar Maskey, Svitlana Zubkovych, Marthe Wens, and Lena Merete Tallaksen
EGUsphere, https://doi.org/10.5194/egusphere-2024-2069, https://doi.org/10.5194/egusphere-2024-2069, 2024
Short summary
Short summary
This research by the Drought in the Anthropocene (DitA) network highlights gaps in European drought management exposed by the 2022 drought and proposes a new direction. Using a Europe-wide survey of water managers, we examine four areas: increasing drought risk, impacts, drought management strategies, and their evolution. Despite growing risks, management remains fragmented and short-term. However, signs of improvement suggest readiness for change. We advocate for a European Drought Directive.
Edward R. Jones, Marc F. P. Bierkens, Niko Wanders, Edwin H. Sutanudjaja, Ludovicus P. H. van Beek, and Michelle T. H. van Vliet
Geosci. Model Dev., 16, 4481–4500, https://doi.org/10.5194/gmd-16-4481-2023, https://doi.org/10.5194/gmd-16-4481-2023, 2023
Short summary
Short summary
DynQual is a new high-resolution global water quality model for simulating total dissolved solids, biological oxygen demand and fecal coliform as indicators of salinity, organic pollution and pathogen pollution, respectively. Output data from DynQual can supplement the observational record of water quality data, which is highly fragmented across space and time, and has the potential to inform assessments in a broad range of fields including ecological, human health and water scarcity studies.
Jannis M. Hoch, Edwin H. Sutanudjaja, Niko Wanders, Rens L. P. H. van Beek, and Marc F. P. Bierkens
Hydrol. Earth Syst. Sci., 27, 1383–1401, https://doi.org/10.5194/hess-27-1383-2023, https://doi.org/10.5194/hess-27-1383-2023, 2023
Short summary
Short summary
To facilitate locally relevant simulations over large areas, global hydrological models (GHMs) have moved towards ever finer spatial resolutions. After a decade-long quest for hyper-resolution (i.e. equal to or smaller than 1 km), the presented work is a first application of a GHM at 1 km resolution over Europe. This not only shows that hyper-resolution can be achieved but also allows for a thorough evaluation of model results at unprecedented detail and the formulation of future research.
Sandra M. Hauswirth, Marc F. P. Bierkens, Vincent Beijk, and Niko Wanders
Hydrol. Earth Syst. Sci., 27, 501–517, https://doi.org/10.5194/hess-27-501-2023, https://doi.org/10.5194/hess-27-501-2023, 2023
Short summary
Short summary
Forecasts on water availability are important for water managers. We test a hybrid framework based on machine learning models and global input data for generating seasonal forecasts. Our evaluation shows that our discharge and surface water level predictions are able to create reliable forecasts up to 2 months ahead. We show that a hybrid framework, developed for local purposes and combined and rerun with global data, can create valuable information similar to large-scale forecasting models.
Sigrid Jørgensen Bakke, Niko Wanders, Karin van der Wiel, and Lena Merete Tallaksen
Nat. Hazards Earth Syst. Sci., 23, 65–89, https://doi.org/10.5194/nhess-23-65-2023, https://doi.org/10.5194/nhess-23-65-2023, 2023
Short summary
Short summary
In this study, we developed a machine learning model to identify dominant controls of wildfire in Fennoscandia and produce monthly fire danger probability maps. The dominant control was shallow-soil water anomaly, followed by air temperature and deep soil water. The model proved skilful with a similar performance as the existing Canadian Forest Fire Weather Index (FWI). We highlight the benefit of using data-driven models jointly with other fire models to improve fire monitoring and prediction.
Vili Virkki, Elina Alanärä, Miina Porkka, Lauri Ahopelto, Tom Gleeson, Chinchu Mohan, Lan Wang-Erlandsson, Martina Flörke, Dieter Gerten, Simon N. Gosling, Naota Hanasaki, Hannes Müller Schmied, Niko Wanders, and Matti Kummu
Hydrol. Earth Syst. Sci., 26, 3315–3336, https://doi.org/10.5194/hess-26-3315-2022, https://doi.org/10.5194/hess-26-3315-2022, 2022
Short summary
Short summary
Direct and indirect human actions have altered streamflow across the world since pre-industrial times. Here, we apply a method of environmental flow envelopes (EFEs) that develops the existing global environmental flow assessments by methodological advances and better consideration of uncertainty. By assessing the violations of the EFE, we comprehensively quantify the frequency, severity, and trends of flow alteration during the past decades, illustrating anthropogenic effects on streamflow.
Veit Blauhut, Michael Stoelzle, Lauri Ahopelto, Manuela I. Brunner, Claudia Teutschbein, Doris E. Wendt, Vytautas Akstinas, Sigrid J. Bakke, Lucy J. Barker, Lenka Bartošová, Agrita Briede, Carmelo Cammalleri, Ksenija Cindrić Kalin, Lucia De Stefano, Miriam Fendeková, David C. Finger, Marijke Huysmans, Mirjana Ivanov, Jaak Jaagus, Jiří Jakubínský, Svitlana Krakovska, Gregor Laaha, Monika Lakatos, Kiril Manevski, Mathias Neumann Andersen, Nina Nikolova, Marzena Osuch, Pieter van Oel, Kalina Radeva, Renata J. Romanowicz, Elena Toth, Mirek Trnka, Marko Urošev, Julia Urquijo Reguera, Eric Sauquet, Aleksandra Stevkov, Lena M. Tallaksen, Iryna Trofimova, Anne F. Van Loon, Michelle T. H. van Vliet, Jean-Philippe Vidal, Niko Wanders, Micha Werner, Patrick Willems, and Nenad Živković
Nat. Hazards Earth Syst. Sci., 22, 2201–2217, https://doi.org/10.5194/nhess-22-2201-2022, https://doi.org/10.5194/nhess-22-2201-2022, 2022
Short summary
Short summary
Recent drought events caused enormous damage in Europe. We therefore questioned the existence and effect of current drought management strategies on the actual impacts and how drought is perceived by relevant stakeholders. Over 700 participants from 28 European countries provided insights into drought hazard and impact perception and current management strategies. The study concludes with an urgent need to collectively combat drought risk via a European macro-level drought governance approach.
Marc F. P. Bierkens, Edwin H. Sutanudjaja, and Niko Wanders
Hydrol. Earth Syst. Sci., 25, 5859–5878, https://doi.org/10.5194/hess-25-5859-2021, https://doi.org/10.5194/hess-25-5859-2021, 2021
Short summary
Short summary
We introduce a simple analytical framework that allows us to estimate to what extent large-scale groundwater withdrawal affects groundwater levels and streamflow. It also calculates which part of the groundwater withdrawal comes out of groundwater storage and which part from a reduction in streamflow. Global depletion rates obtained with the framework are compared with estimates from satellites, from global- and continental-scale groundwater models, and from in situ datasets.
Camelia-Eliza Telteu, Hannes Müller Schmied, Wim Thiery, Guoyong Leng, Peter Burek, Xingcai Liu, Julien Eric Stanislas Boulange, Lauren Seaby Andersen, Manolis Grillakis, Simon Newland Gosling, Yusuke Satoh, Oldrich Rakovec, Tobias Stacke, Jinfeng Chang, Niko Wanders, Harsh Lovekumar Shah, Tim Trautmann, Ganquan Mao, Naota Hanasaki, Aristeidis Koutroulis, Yadu Pokhrel, Luis Samaniego, Yoshihide Wada, Vimal Mishra, Junguo Liu, Petra Döll, Fang Zhao, Anne Gädeke, Sam S. Rabin, and Florian Herz
Geosci. Model Dev., 14, 3843–3878, https://doi.org/10.5194/gmd-14-3843-2021, https://doi.org/10.5194/gmd-14-3843-2021, 2021
Short summary
Short summary
We analyse water storage compartments, water flows, and human water use sectors included in 16 global water models that provide simulations for the Inter-Sectoral Impact Model Intercomparison Project phase 2b. We develop a standard writing style for the model equations. We conclude that even though hydrologic processes are often based on similar equations, in the end these equations have been adjusted, or the models have used different values for specific parameters or specific variables.
Noemi Vergopolan, Sitian Xiong, Lyndon Estes, Niko Wanders, Nathaniel W. Chaney, Eric F. Wood, Megan Konar, Kelly Caylor, Hylke E. Beck, Nicolas Gatti, Tom Evans, and Justin Sheffield
Hydrol. Earth Syst. Sci., 25, 1827–1847, https://doi.org/10.5194/hess-25-1827-2021, https://doi.org/10.5194/hess-25-1827-2021, 2021
Short summary
Short summary
Drought monitoring and yield prediction often rely on coarse-scale hydroclimate data or (infrequent) vegetation indexes that do not always indicate the conditions farmers face in the field. Consequently, decision-making based on these indices can often be disconnected from the farmer reality. Our study focuses on smallholder farming systems in data-sparse developing countries, and it shows how field-scale soil moisture can leverage and improve crop yield prediction and drought impact assessment.
Sarah F. Kew, Sjoukje Y. Philip, Mathias Hauser, Mike Hobbins, Niko Wanders, Geert Jan van Oldenborgh, Karin van der Wiel, Ted I. E. Veldkamp, Joyce Kimutai, Chris Funk, and Friederike E. L. Otto
Earth Syst. Dynam., 12, 17–35, https://doi.org/10.5194/esd-12-17-2021, https://doi.org/10.5194/esd-12-17-2021, 2021
Short summary
Short summary
Motivated by the possible influence of rising temperatures, this study synthesises results from observations and climate models to explore trends (1900–2018) in eastern African (EA) drought measures. However, no discernible trends are found in annual soil moisture or precipitation. Positive trends in potential evaporation indicate that for irrigated regions more water is now required to counteract increased evaporation. Precipitation deficit is, however, the most useful indicator of EA drought.
Cited articles
Adams, J.: Climate Indices in Python, GitHub [code], https://github.com/monocongo/climate_indices (last access: 22 November 2022), 2017. a
AghaKouchak, A., Farahmand, A., Melton, F. S., Teixeira, J., Anderson, M. C., Wardlow, B. D., and Hain, C. R.: Remote sensing of drought: Progress, challenges and opportunities: Remote Sensing Of Drought, Rev. Geophys., 53, 452–480, https://doi.org/10.1002/2014RG000456, 2015. a, b
Banerjee, O., Bark, R., Connor, J., and Crossman, N. D.: An ecosystem services approach to estimating economic losses associated with drought, Ecol. Econ., 91, 19–27, https://doi.org/10.1016/j.ecolecon.2013.03.022, 2013. a, b
Blauhut, V., Stahl, K., Stagge, J. H., Tallaksen, L. M., De Stefano, L., and Vogt, J.: Estimating drought risk across Europe from reported drought impacts, drought indices, and vulnerability factors, Hydrol. Earth Syst. Sci., 20, 2779–2800, https://doi.org/10.5194/hess-20-2779-2016, 2016. a
Box, E. O., Holben, B. N., and Kalb, V.: Accuracy of the AVHRR vegetation index as a predictor of biomass, primary productivity and net CO2 flux, Vegetatio, 80, Springer, 71–89, https://doi.org/10.1007/BF00048034, 1989. a
Cammalleri, C., Naumann, G., Mentaschi, L., Formetta, G., Forzieri, G., Gosling, S., Bisselink, B., De Roo, A., and Feyen, L.: Global warming and drought impacts in the EU: JRC PESETA IV project: Task 7, Publications Office, LU, https://data.europa.eu/doi/10.2760/597045 (last access: 16 June 2022), 2020. a
Chen, J. M., Chen, X., and Ju, W.: Effects of vegetation heterogeneity and surface topography on spatial scaling of net primary productivity, Biogeosciences, 10, 4879–4896, https://doi.org/10.5194/bg-10-4879-2013, 2013. a
Chen, Q., Timmermans, J., Wen, W., and van Bodegom, P. M.: A multi-metric assessment of drought vulnerability across different vegetation types using high resolution remote sensing, Sci. Total Environ., 832, 154970, https://doi.org/10.1016/j.scitotenv.2022.154970, 2022. a
Chen, Z., Liu, H., Xu, C., Wu, X., Liang, B., Cao, J., and Chen, D.: Modeling vegetation greenness and its climate sensitivity with deep‐learning technology, Ecol. Evol., 11, 7335–7345, https://doi.org/10.1002/ece3.7564, 2021. a, b, c
Crausbay, S. D., Ramirez, A. R., Carter, S. L., Cross, M. S., Hall, K. R., Bathke, D. J., Betancourt, J. L., Colt, S., Cravens, A. E., Dalton, M. S., Dunham, J. B., Hay, L. E., Hayes, M. J., McEvoy, J., McNutt, C. A., Moritz, M. A., Nislow, K. H., Raheem, N., and Sanford, T.: Defining Ecological Drought for the Twenty-First Century, B. Am. Meteorol. Soc., 98, 2543–2550, https://doi.org/10.1175/BAMS-D-16-0292.1, 2017. a
Das, P., Naganna, S. R., Deka, P. C., and Pushparaj, J.: Hybrid wavelet packet machine learning approaches for drought modeling, Environ. Earth Sci., 79, 221, https://doi.org/10.1007/s12665-020-08971-y, 2020. a
Didan, K.: MOD13A2 MODIS/Terra Vegetation Indices 16-Day L3 Global 1 km SIN Grid V006, NASA EOSDIS LP DAAC [data set], https://doi.org/10.5067/MODIS/MYD13A2.061, 2015. a, b
Didan, K.: MODIS/Aqua Vegetation Indices Monthly L3 Global 0.05 Deg CMG V061, NASA EOSDIS Land Processes DAAC [data set], https://doi.org/10.5067/MODIS/MOD13A2.061, 2021. a
Ebrahimy, H., Aghighi, H., Azadbakht, M., Amani, M., Mahdavi, S., and Matkan, A. A.: Downscaling MODIS Land Surface Temperature Product Using an Adaptive Random Forest Regression Method and Google Earth Engine for a 19-Years Spatiotemporal Trend Analysis Over Iran, IEEE J. Select. Top. Appl. Earth Obs. Remote Sens., 14, 2103–2112, https://doi.org/10.1109/JSTARS.2021.3051422, 2021. a
Franklin, O., Harrison, S. P., Dewar, R., Farrior, C. E., Brännström, A., Dieckmann, U., Pietsch, S., Falster, D., Cramer, W., Loreau, M., Wang, H., Mäkelä, A., Rebel, K. T., Meron, E., Schymanski, S. J., Rovenskaya, E., Stocker, B. D., Zaehle, S., Manzoni, S., van Oijen, M., Wright, I. J., Ciais, P., van Bodegom, P. M., Peñuelas, J., Hofhansl, F., Terrer, C., Soudzilovskaia, N. A., Midgley, G., and Prentice, I. C.: Organizing principles for vegetation dynamics, Nat. Plants, 6, 444–453, https://doi.org/10.1038/s41477-020-0655-x, 2020. a
Friedl, M. and Sulla-Menashe, D.: MCD12Q1 MODIS/Terra+Aqua Land Cover Type Yearly L3 Global 500 m SIN Grid V006, USGS [data set], https://doi.org/10.5067/MODIS/MCD12Q1.006, 2019. a
Fu, R., Chen, R., Wang, C., Chen, X., Gu, H., Wang, C., Xu, B., Liu, G., and Yin, G.: Generating High-Resolution and Long-Term SPEI Dataset over Southwest China through Downscaling EEAD Product by Machine Learning, Remote Sens., 14, 1662, https://doi.org/10.3390/rs14071662, 2022. a
Gao, X., Huete, A. R., Ni, W., and Miura, T.: Optical–Biophysical Relationships of Vegetation Spectra without Background Contamination, Remote Sens. Environ., 74, 609–620, https://doi.org/10.1016/S0034-4257(00)00150-4, 2000. a, b
Gensheimer, J., Turner, A. J., Köhler, P., Frankenberg, C., and Chen, J.: A convolutional neural network for spatial downscaling of satellite-based solar-induced chlorophyll fluorescence (SIFnet), Biogeosciences, 19, 1777–1793, https://doi.org/10.5194/bg-19-1777-2022, 2022. a, b
Gorelick, N., Hancher, M., Dixon, M., Ilyushchenko, S., Thau, D., and Moore, R.: Google Earth Engine: Planetary-scale geospatial analysis for everyone, Remote Sens. Environ., 202, 18–27, https://doi.org/10.1016/j.rse.2017.06.031, 2017. a, b
Grieger, R., Capon, S. J., Hadwen, W. L., Mackey, B., Grieger, R., Capon, S. J., Hadwen, W. L., and Mackey, B.: Spatial variation and drivers of vegetation structure and composition in coastal freshwater wetlands of subtropical Australia, Mar. Freshwater Res., 72, 1746–1759, https://doi.org/10.1071/MF21023, 2021. a
Han, D., Wang, G., Liu, T., Xue, B.-L., Kuczera, G., and Xu, X.: Hydroclimatic response of evapotranspiration partitioning to prolonged droughts in semiarid grassland, J. Hydrol., 563, 766–777, https://doi.org/10.1016/j.jhydrol.2018.06.048, 2018. a
Hauswirth, S. M., Bierkens, M. F. P., Beijk, V., and Wanders, N.: The suitability of a seasonal ensemble hybrid framework including data-driven approaches for hydrological forecasting, Hydrol. Earth Syst. Sci., 27, 501–517, https://doi.org/10.5194/hess-27-501-2023, 2023. a
Hawkins, B. A., Field, R., Cornell, H. V., Currie, D. J., Guégan, J.-F., Kaufman, D. M., Kerr, J. T., Mittelbach, G. G., Oberdorff, T., O'Brien, E. M., Porter, E. E., and Turner, J. R. G.: Energy, water, and broad-scale geographic patterns of species richness, Ecology, 84, 3105–3117, https://doi.org/10.1890/03-8006, 2003. a, b
Hobeichi, S., Nishant, N., Shao, Y., Abramowitz, G., Pitman, A., Sherwood, S., Bishop, C., and Green, S.: Using Machine Learning to Cut the Cost of Dynamical Downscaling, Earth's Future, 11, e2022EF003291, https://doi.org/10.1029/2022EF003291, 2023. a
Hoyer, S. and Hamman, J.: xarray: N-D labeled Arrays and Datasets in Python, J. Open Res. Softw., 5, 10, https://doi.org/10.5334/jors.148, 2017. a, b
Huang, S., Tang, L., Hupy, J. P., Wang, Y., and Shao, G.: A commentary review on the use of normalized difference vegetation index (NDVI) in the era of popular remote sensing, J. Forest. Res., 32, 1–6, https://doi.org/10.1007/s11676-020-01155-1, 2021. a
Jung, M., Dahal, P. R., Butchart, S. H. M., Donald, P. F., De Lamo, X., Lesiv, M., Kapos, V., Rondinini, C., and Visconti, P.: A global map of terrestrial habitat types, Sci. Data, 7, 256, https://doi.org/10.1038/s41597-020-00599-8, 2020. a
Kladny, K.-R., Milanta, M., Mraz, O., Hufkens, K., and Stocker, B. D.: Enhanced prediction of vegetation responses to extreme drought using deep learning and Earth observation data, Ecol. Inform., 80, 102474, https://doi.org/10.1016/j.ecoinf.2024.102474, 2024. a
Lal, P., Singh, G., Das, N. N., Colliander, A., and Entekhabi, D.: Assessment of ERA5-Land Volumetric Soil Water Layer Product Using In Situ and SMAP Soil Moisture Observations, IEEE Geosci. Remote Sens. Lett., 19, 1–5, https://doi.org/10.1109/LGRS.2022.3223985, 2022. a
Li, S., Xu, L., Jing, Y., Yin, H., Li, X., and Guan, X.: High-quality vegetation index product generation: A review of NDVI time series reconstruction techniques, Int. J. Appl. Earth Obs. Geoinf., 105, 102640, https://doi.org/10.1016/j.jag.2021.102640, 2021. a, b
Li, X., Yuan, W., and Dong, W.: A Machine Learning Method for Predicting Vegetation Indices in China, Remote Sens., 13, 1147, https://doi.org/10.3390/rs13061147, 2021. a, b, c
Liu, Y., Jing, W., Wang, Q., and Xia, X.: Generating high-resolution daily soil moisture by using spatial downscaling techniques: a comparison of six machine learning algorithms, Adv. Water Resour., 141, 103601, https://doi.org/10.1016/j.advwatres.2020.103601, 2020. a, b
Lundberg, S. M., Erion, G., Chen, H., DeGrave, A., Prutkin, J. M., Nair, B., Katz, R., Himmelfarb, J., Bansal, N., and Lee, S.-I.: From local explanations to global understanding with explainable AI for trees, Nat. Mach. Intel., 2, 56–67, https://doi.org/10.1038/s42256-019-0138-9, 2020. a
McColl, K. A., Alemohammad, S. H., Akbar, R., Konings, A. G., Yueh, S., and Entekhabi, D.: The global distribution and dynamics of surface soil moisture, Nat. Geosci., 10, 100–104, https://doi.org/10.1038/ngeo2868, 2017. a
McKee, T. B., Doesken, N. J., and Kleist, J.: The relationship of drought frequency and duration to time scales, in: Proceedings of the 8th Conference on Applied Climatology, vol. 17, 17–22 January 1993, Boston, 179–183, https://climate.colostate.edu/pdfs/relationshipofdroughtfrequency.pdf (last access: 3 June 2024), 1993. a
Meza, I., Siebert, S., Döll, P., Kusche, J., Herbert, C., Eyshi Rezaei, E., Nouri, H., Gerdener, H., Popat, E., Frischen, J., Naumann, G., Vogt, J. V., Walz, Y., Sebesvari, Z., and Hagenlocher, M.: Global-scale drought risk assessment for agricultural systems, Nat. Hazards Earth Syst. Sci., 20, 695–712, https://doi.org/10.5194/nhess-20-695-2020, 2020. a
Monteith, J. L.: Evaporation and environment, Symposia of the Society for Experimental Biology, 19, 205–234, https://repository.rothamsted.ac.uk/item/8v5v7/evaporation-and-environment (last access: 16 July 2022), 1965. a
Moreno-Martínez, A., Camps-Valls, G., Kattge, J., Robinson, N., Reichstein, M., van Bodegom, P., Kramer, K., Cornelissen, J. H. C., Reich, P., Bahn, M., Niinemets, Ü., Peñuelas, J., Craine, J. M., Cerabolini, B. E. L., Minden, V., Laughlin, D. C., Sack, L., Allred, B., Baraloto, C., Byun, C., Soudzilovskaia, N. A., and Running, S. W.: A methodology to derive global maps of leaf traits using remote sensing and climate data, Remote Sens. Environ., 218, 69–88, https://doi.org/10.1016/j.rse.2018.09.006, 2018. a
Moussa Kourouma, J., Eze, E., Negash, E., Phiri, D., Vinya, R., Girma, A., and Zenebe, A.: Assessing the spatio-temporal variability of NDVI and VCI as indices of crops productivity in Ethiopia: a remote sensing approach, Geomat. Nat. Hazards Risk, 12, 2880–2903, https://doi.org/10.1080/19475705.2021.1976849, 2021. a, b
Muñoz Sabater, J.: ERA5-Land hourly data from 1950 to present, Copernicus Climate Change Service (C3S) Climate Data Store (CDS) [data set], https://doi.org/10.24381/cds.e2161bac, 2019. a
Muñoz-Sabater, J., Dutra, E., Agustí-Panareda, A., Albergel, C., Arduini, G., Balsamo, G., Boussetta, S., Choulga, M., Harrigan, S., Hersbach, H., Martens, B., Miralles, D. G., Piles, M., Rodríguez-Fernández, N. J., Zsoter, E., Buontempo, C., and Thépaut, J.-N.: ERA5-Land: a state-of-the-art global reanalysis dataset for land applications, Earth Syst. Sci. Data, 13, 4349–4383, https://doi.org/10.5194/essd-13-4349-2021, 2021. a, b, c
Naumann, G., Barbosa, P., Garrote, L., Iglesias, A., and Vogt, J.: Exploring drought vulnerability in Africa: an indicator based analysis to be used in early warning systems, Hydrol. Earth Syst. Sci., 18, 1591–1604, https://doi.org/10.5194/hess-18-1591-2014, 2014. a
Park, S., Singh, K., Nellikkattil, A., Zeller, E., Mai, T. D., and Cha, M.: Downscaling Earth System Models with Deep Learning, in: Proceedings of the 28th ACM SIGKDD Conference on Knowledge Discovery and Data Mining, ACM, Washington, D.C., USA, 3733–3742, ISBN 9781450393850, https://doi.org/10.1145/3534678.3539031, 2022. a
Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Prettenhofer, P., Weiss, R., Dubourg, V., Vanderplas, J., Passos, A., Cournapeau, D., Brucher, M., Perrot, M., and Duchesnay, E.: Scikit-learn: Machine Learning in Python, J. Mach. Learn. Res., 12, 2825–2830, 2011. a
Peng, J., Loew, A., Merlin, O., and Verhoest, N. E. C.: A review of spatial downscaling of satellite remotely sensed soil moisture, Rev. Geophys., 55, 341–366, https://doi.org/10.1002/2016RG000543, 2017. a
Pretzsch, H., Biber, P., Uhl, E., Dahlhausen, J., Schütze, G., Perkins, D., Rötzer, T., Caldentey, J., Koike, T., Con, T. v., Chavanne, A., Toit, B. D., Foster, K., and Lefer, B.: Climate change accelerates growth of urban trees in metropolises worldwide, Sci. Rep., 7, 15403, https://doi.org/10.1038/s41598-017-14831-w, 2017. a
Priestley, C. H. B. and Taylor, R. J.: On the Assessment of Surface Heat Flux and Evaporation Using Large-Scale Parameters, Mon. Weather Rev., 100, 81–92, https://doi.org/10.1175/1520-0493(1972)100<0081:OTAOSH>2.3.CO;2, 1972. a
Prodhan, F. A., Zhang, J., Hasan, S. S., Pangali Sharma, T. P., and Mohana, H. P.: A review of machine learning methods for drought hazard monitoring and forecasting: Current research trends, challenges, and future research directions, Environ. Model. Softw., 149, 105327, https://doi.org/10.1016/j.envsoft.2022.105327, 2022. a
Reichstein, M., Camps-Valls, G., Stevens, B., Jung, M., Denzler, J., Carvalhais, N., and Prabhat: Deep learning and process understanding for data-driven Earth system science, Nature, 566, 195–204, https://doi.org/10.1038/s41586-019-0912-1, 2019. a, b
Ribeiro, R. P. and Moniz, N.: Imbalanced regression and extreme value prediction, Mach. Learn., 109, 1803–1835, https://doi.org/10.1007/s10994-020-05900-9, 2020. a
Rossum, G. V. and Drake, F. L.: The Python language reference, no. Pt. 2 in Python documentation manual, in: release 3.0.1 [repr.] edn., edited by: van Rossum, G. and Drake, F. L., Python Software Foundation, Hampton, NH, ISBN 9781441412690, 2010. a
Roy, B.: Optimum machine learning algorithm selection for forecasting vegetation indices: MODIS NDVI & EVI, Remote Sens. Appl.: Soc. Environ., 23, 100582, https://doi.org/10.1016/j.rsase.2021.100582, 2021. a, b, c
Sarafanov, M., Kazakov, E., Nikitin, N. O., and Kalyuzhnaya, A. V.: A Machine Learning Approach for Remote Sensing Data Gap-Filling with Open-Source Implementation: An Example Regarding Land Surface Temperature, Surface Albedo and NDVI, Remote Sens., 12, 3865, https://doi.org/10.3390/rs12233865, 2020. a
Schneider, T., Lan, S., Stuart, A., and Teixeira, J.: Earth System Modeling 2.0: A Blueprint for Models That Learn From Observations and Targeted High‐Resolution Simulations, Geophys. Res. Lett., 44, 12396–12417, https://doi.org/10.1002/2017GL076101, 2017. a
Schwalm, C. R., Anderegg, W. R. L., Michalak, A. M., Fisher, J. B., Biondi, F., Koch, G., Litvak, M., Ogle, K., Shaw, J. D., Wolf, A., Huntzinger, D. N., Schaefer, K., Cook, R., Wei, Y., Fang, Y., Hayes, D., Huang, M., Jain, A., and Tian, H.: Global patterns of drought recovery, Nature, 548, 202–205, https://doi.org/10.1038/nature23021, 2017. a, b
Shamshirband, S., Hashemi, S., Salimi, H., Samadianfard, S., Asadi, E., Shadkani, S., Kargar, K., Mosavi, A., Nabipour, N., and Chau, K.-W.: Predicting Standardized Streamflow index for hydrological drought using machine learning models, Eng. Appl. Comput. Fluid Mech., 14, 339–350, https://doi.org/10.1080/19942060.2020.1715844, 2020. a
Sharifi, A.: Yield prediction with machine learning algorithms and satellite images, J. Sci. Food Agricult., 101, 891–896, https://doi.org/10.1002/jsfa.10696, 2021. a
Shen, R., Huang, A., Li, B., and Guo, J.: Construction of a drought monitoring model using deep learning based on multi-source remote sensing data, Int. J. Appl. Earth Obs. Geoinf., 79, 48–57, https://doi.org/10.1016/j.jag.2019.03.006, 2019. a
Sicard, P., Agathokleous, E., Araminiene, V., Carrari, E., Hoshika, Y., De Marco, A., and Paoletti, E.: Should we see urban trees as effective solutions to reduce increasing ozone levels in cities?, Environ. Pollut., 243, 163–176, https://doi.org/10.1016/j.envpol.2018.08.049, 2018a. a
Sicard, P., Agathokleous, E., Araminiene, V., Carrari, E., Hoshika, Y., De Marco, A., and Paoletti, E.: Should we see urban trees as effective solutions to reduce increasing ozone levels in cities?, Environ. Pollut., 243, 163–176, https://doi.org/10.1016/j.envpol.2018.08.049, 2018b. a
Singer, M., Asfaw, D., Rosolem, R., Cuthbert, M. O., Miralles, D. G., Quichimbo Miguitama, E., MacLeod, D., and Michaelides, K.: Hourly potential evapotranspiration (hPET) at 0.1 degs grid resolution for the global land surface from 1981–present, University of Bristol [data set], https://doi.org/10.5523/bris.qb8ujazzda0s2aykkv0oq0ctp, 2020. a
Singer, M. B., Asfaw, D. T., Rosolem, R., Cuthbert, M. O., Miralles, D. G., MacLeod, D., Quichimbo, E. A., and Michaelides, K.: Hourly potential evapotranspiration at 0.1° resolution for the global land surface from 1981–present, Sci. Data, 8, 224, https://doi.org/10.1038/s41597-021-01003-9, 2021. a, b
Smith, N. E., Kooijmans, L. M. J., Koren, G., van Schaik, E., van der Woude, A. M., Wanders, N., Ramonet, M., Xueref-Remy, I., Siebicke, L., Manca, G., Brümmer, C., Baker, I. T., Haynes, K. D., Luijkx, I. T., and Peters, W.: Spring enhancement and summer reduction in carbon uptake during the 2018 drought in northwestern Europe, Philos. T. Roy. Soc. B, 375, 20190509, https://doi.org/10.1098/rstb.2019.0509, 2020. a
Sun, M., Gong, A., Zhao, X., Liu, N., Si, L., and Zhao, S.: Reconstruction of a Monthly 1 km NDVI Time Series Product in China Using Random Forest Methodology, Remote Sens., 15, 3353, https://doi.org/10.3390/rs15133353, 2023. a
Sutanto, S. J., van der Weert, M., Wanders, N., Blauhut, V., and Van Lanen, H. A. J.: Moving from drought hazard to impact forecasts, Nat. Commun., 10, 4945, https://doi.org/10.1038/s41467-019-12840-z, 2019. a
Tang, L., Chen, X., Cai, X., and Li, J.: Disentangling the roles of land-use-related drivers on vegetation greenness across China, Environ. Res. Lett., 16, 124033, https://doi.org/10.1088/1748-9326/ac37d2, 2021. a
Thornthwaite, C. W.: An Approach toward a Rational Classification of Climate, Geogr. Rev., 38, 55–94, https://doi.org/10.2307/210739, 1948. a
Tufaner, F. and Özbeyaz, A.: Estimation and easy calculation of the Palmer Drought Severity Index from the meteorological data by using the advanced machine learning algorithms, Environ. Monit. Assess., 192, 576, https://doi.org/10.1007/s10661-020-08539-0, 2020. a
USGS: MCD12C1 v061 MODIS/Terra+Aqua Land Cover Type Yearly L3 Global 0.05 Deg CMG, USGS [data set], https://doi.org/10.5067/MODIS/MCD12C1.061, 2024. a
Vogt, J. V., Naumann, G., Masante, D., Spinoni, J., Cammalleri, C., Erian, W., Pischke, F., Pulwarty, R., and Barbosa, P.: Drought risk assessment and management: a conceptual framework, Publications Office, LU, https://doi.org/10.2760/057223, 2018. a
Wanders, N. and Wada, Y.: Human and climate impacts on the 21st century hydrological drought, J. Hydrol., 526, 208–220, https://doi.org/10.1016/j.jhydrol.2014.10.047, 2015. a
Wang, H., Seaborn, T., Wang, Z., Caudill, C. C., and Link, T. E.: Modeling tree canopy height using machine learning over mixed vegetation landscapes, Int. J. Appl. Earth Obs. Geoinform., 101, 102353, https://doi.org/10.1016/j.jag.2021.102353, 2021. a
Wang, Q., Wang, L., Zhu, X., Ge, Y., Tong, X., and Atkinson, P. M.: Remote sensing image gap filling based on spatial-spectral random forests, Sci. Remote Sens., 5, 100048, https://doi.org/10.1016/j.srs.2022.100048, 2022. a, b
Ward, P. J., Blauhut, V., Bloemendaal, N., Daniell, J. E., de Ruiter, M. C., Duncan, M. J., Emberson, R., Jenkins, S. F., Kirschbaum, D., Kunz, M., Mohr, S., Muis, S., Riddell, G. A., Sch”afer, A., Stanley, T., Veldkamp, T. I. E., and Winsemius, H. C.: Review article: Natural hazard risk assessments at the global scale, Nat. Hazards Earth Syst. Sci., 20, 1069–1096, https://doi.org/10.5194/nhess-20-1069-2020, 2020. a
West, H., Quinn, N., and Horswell, M.: Remote sensing for drought monitoring & impact assessment: Progress, past challenges and future opportunities, Remote Sens. Environ., 232, 111291, https://doi.org/10.1016/j.rse.2019.111291, 2019. a
Wu, Q.: geemap: A Python package for interactive mapping with Google Earth Engine, J. Open Sour. Softw., 5, 2305, https://doi.org/10.21105/joss.02305, 2020. a, b
Xiong, C., Ma, H., Liang, S., He, T., Zhang, Y., Zhang, G., and Xu, J.: Improved global 250 m 8-day NDVI and EVI products from 2000–2021 using the LSTM model, Sci. Data, 10, 800, https://doi.org/10.1038/s41597-023-02695-x, 2023. a, b
Xu, Z., Zhou, G., and Shimizu, H.: Plant responses to drought and rewatering, Plant Signal. Behav., 5, 649–654, https://doi.org/10.4161/psb.5.6.11398, 2010. a
Yamazaki, D., Ikeshima, D., Sosa, J., Allen, G. H., Bates, P. D., and Pavelsky, T. M.: MERIT Hydro: A High-Resolution Global Hydrography Map Based on Latest Topography Dataset, Water Resour. Res., 55, 5053–5073, https://doi.org/10.1029/2019WR024873, 2019. a, b, c
Zeng, C., Shen, H., and Zhang, L.: Recovering missing pixels for Landsat ETM+ SLC-off imagery using multi-temporal regression analysis and a regularization method, Remote Sens. Environ., 131, 182–194, https://doi.org/10.1016/j.rse.2012.12.012, 2013. a
Zhang, J., Liu, K., and Wang, M.: Downscaling Groundwater Storage Data in China to a 1-km Resolution Using Machine Learning Methods, Remote Sens., 13, 523, https://doi.org/10.3390/rs13030523, 2021. a
Zhang, L., Zeng, Y., Zhuang, R., Szabó, B., Manfreda, S., Han, Q., and Su, Z.: In Situ Observation-Constrained Global Surface Soil Moisture Using Random Forest Model, Remote Sens., 13, 4893, https://doi.org/10.3390/rs13234893, 2021. a
Zhang, X., Friedl, M. A., Schaaf, C. B., and Strahler, A. H.: Climate controls on vegetation phenological patterns in northern mid- and high latitudes inferred from MODIS data: Climate Controls On Vegetation Phenological Patterns, Global Change Biol., 10, 1133–1145, https://doi.org/10.1111/j.1529-8817.2003.00784.x, 2004. a
Zhang, Y., Keenan, T. F., and Zhou, S.: Exacerbated drought impacts on global ecosystems due to structural overshoot, Nat. Ecol. Evol., 5, 1490–1498, https://doi.org/10.1038/s41559-021-01551-8, 2021. a, b, c
Zhao, W. and Duan, S.-B.: Reconstruction of daytime land surface temperatures under cloud-covered conditions using integrated MODIS/Terra land products and MSG geostationary satellite data, Remote Sens. Environ., 247, 111931, https://doi.org/10.1016/j.rse.2020.111931, 2020. a
Zhu, S., Clement, R., McCalmont, J., Davies, C. A., and Hill, T.: Stable gap-filling for longer eddy covariance data gaps: A globally validated machine-learning approach for carbon dioxide, water, and energy fluxes, Agr. Forest Meteorol., 314, 108777, https://doi.org/10.1016/j.agrformet.2021.108777, 2022. a
Zhu, Z.: Change detection using landsat time series: A review of frequencies, preprocessing, algorithms, and applications, ISPRS J. Photogram. Remote Sens., 130, 370–384, https://doi.org/10.1016/j.isprsjprs.2017.06.013, 2017. a
Short summary
Drought often manifests itself in vegetation; however, obtaining high-resolution remote-sensing products that are spatially and temporally consistent is difficult. In this study, we show that machine learning (ML) can fill data gaps in existing products. We also demonstrate that ML can be used as a downscaling tool. By relying on ML for gap filling and downscaling, we can obtain a more holistic view of the impacts of drought on vegetation.
Drought often manifests itself in vegetation; however, obtaining high-resolution remote-sensing...
