Articles | Volume 25, issue 4
https://doi.org/10.5194/hess-25-1827-2021
© Author(s) 2021. 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-25-1827-2021
© Author(s) 2021. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Research article
| 
09 Apr 2021
Research article |
| 09 Apr 2021
| 09 Apr 2021
Field-scale soil moisture bridges the spatial-scale gap between drought monitoring and agricultural yields
Civil and Environmental Engineering Department, Princeton University, Princeton, NJ, USA
Sitian Xiong
School of Geography, Clark University, Worcester, MA, USA
Lyndon Estes
School of Geography, Clark University, Worcester, MA, USA
Niko Wanders
Department of Physical Geography, Faculty of Geosciences, Utrecht University, Utrecht, the Netherlands
Nathaniel W. Chaney
Department of Civil and Environmental Engineering, Duke University, Durham, NC, USA
Eric F. Wood
Civil and Environmental Engineering Department, Princeton University, Princeton, NJ, USA
Megan Konar
Civil and Environmental Engineering Department, University of Illinois at Urbana-Champaign, Urbana, IL, USA
Kelly Caylor
Department of Geography, University of California, Santa Barbara, CA, USA
Bren School of Environmental Science and Management, University of California, Santa Barbara, CA, USA
Hylke E. Beck
Civil and Environmental Engineering Department, Princeton University, Princeton, NJ, USA
Nicolas Gatti
Department of Agricultural and Consumer Economics, University of Illinois at Urbana-Champaign, Urbana, IL, USA
Tom Evans
School of Geography, Development and Environment, University of Arizona, Tucson, AZ, USA
Justin Sheffield
School of Geography and Environmental Science, University of Southampton, Southampton, UK
Viewed
Total article views: 5,663 (including HTML, PDF, and XML)
Cumulative views and downloads
(calculated since 24 Jul 2020)
| HTML | XML | Total | Supplement | BibTeX | EndNote | |
|---|---|---|---|---|---|---|
| 4,182 | 1,400 | 81 | 5,663 | 329 | 80 | 87 |
- HTML: 4,182
- PDF: 1,400
- XML: 81
- Total: 5,663
- Supplement: 329
- BibTeX: 80
- EndNote: 87
Total article views: 4,557 (including HTML, PDF, and XML)
Cumulative views and downloads
(calculated since 09 Apr 2021)
| HTML | XML | Total | Supplement | BibTeX | EndNote | |
|---|---|---|---|---|---|---|
| 3,541 | 953 | 63 | 4,557 | 191 | 56 | 66 |
- HTML: 3,541
- PDF: 953
- XML: 63
- Total: 4,557
- Supplement: 191
- BibTeX: 56
- EndNote: 66
Total article views: 1,106 (including HTML, PDF, and XML)
Cumulative views and downloads
(calculated since 24 Jul 2020)
| HTML | XML | Total | Supplement | BibTeX | EndNote | |
|---|---|---|---|---|---|---|
| 641 | 447 | 18 | 1,106 | 138 | 24 | 21 |
- HTML: 641
- PDF: 447
- XML: 18
- Total: 1,106
- Supplement: 138
- BibTeX: 24
- EndNote: 21
Viewed (geographical distribution)
Total article views: 5,663 (including HTML, PDF, and XML)
Thereof 5,154 with geography defined
and 509 with unknown origin.
Total article views: 4,557 (including HTML, PDF, and XML)
Thereof 4,304 with geography defined
and 253 with unknown origin.
Total article views: 1,106 (including HTML, PDF, and XML)
Thereof 850 with geography defined
and 256 with unknown origin.
| Country | # | Views | % |
|---|
| Country | # | Views | % |
|---|
| Country | # | Views | % |
|---|
| Total: | 0 |
| HTML: | 0 |
| PDF: | 0 |
| XML: | 0 |
- 1
1
| Total: | 0 |
| HTML: | 0 |
| PDF: | 0 |
| XML: | 0 |
- 1
1
| Total: | 0 |
| HTML: | 0 |
| PDF: | 0 |
| XML: | 0 |
- 1
1
Cited
22 citations as recorded by crossref.
- River reach-level machine learning estimation of nutrient concentrations in Great Britain C. Tso et al. 10.3389/frwa.2023.1244024
- Enhancing spatial resolution of satellite soil moisture data through stacking ensemble learning techniques M. Tahmouresi et al. 10.1038/s41598-024-77050-0
- Towards an Optimal Representation of Sub‐Grid Heterogeneity in Land Surface Models L. Torres‐Rojas et al. 10.1029/2022WR032233
- A mediation analysis of the linkages between climate variability, water insecurity, and interpersonal violence A. Ross et al. 10.1080/17565529.2023.2186746
- On the potential of Sentinel-1 for sub-field scale soil moisture monitoring T. van Hateren et al. 10.1016/j.jag.2023.103342
- SMAP-HydroBlocks, a 30-m satellite-based soil moisture dataset for the conterminous US N. Vergopolan et al. 10.1038/s41597-021-01050-2
- High‐Resolution Soil Moisture Data Reveal Complex Multi‐Scale Spatial Variability Across the United States N. Vergopolan et al. 10.1029/2022GL098586
- A novel land surface temperature reconstruction method and its application for downscaling surface soil moisture with machine learning O. Güngör Şahin & O. Gündüz 10.1016/j.jhydrol.2024.131051
- Satellite rainfall bias correction incorporating effects on simulated crop water requirements C. Omondi et al. 10.1080/01431161.2024.2326801
- Causal inference of root zone soil moisture performance in drought S. Xue & G. Wu 10.1016/j.agwat.2024.109123
- An agricultural digital twin for mandarins demonstrates the potential for individualized agriculture S. Kim & S. Heo 10.1038/s41467-024-45725-x
- The Benefits of Using State‐Of‐The‐Art Digital Soil Properties Maps to Improve the Modeling of Soil Moisture in Land Surface Models C. Xu et al. 10.1029/2022WR032336
- Improving generalisability and transferability of machine-learning-based maize yield prediction model through domain adaptation R. Priyatikanto et al. 10.1016/j.agrformet.2023.109652
- High-resolution European daily soil moisture derived with machine learning (2003–2020) S. O et al. 10.1038/s41597-022-01785-6
- How much control do smallholder maize farmers have over yield? M. Cecil et al. 10.1016/j.fcr.2023.109014
- A 1 km daily surface soil moisture dataset of enhanced coverage under all-weather conditions over China in 2003–2019 P. Song et al. 10.5194/essd-14-2613-2022
- A comprehensive assessment of in situ and remote sensing soil moisture data assimilation in the APSIM model for improving agricultural forecasting across the US Midwest M. Kivi et al. 10.5194/hess-27-1173-2023
- Is closing the agricultural yield gap a “risky” endeavor? N. Gatti et al. 10.1016/j.agsy.2023.103657
- Drivers of maize yield variability at household level in Northern Ghana and Malawi S. Gachoki & F. Muthoni 10.1080/10106049.2023.2230948
- Improving Generalisability and Transferability of Machine-Learning-Based Maize Yield Prediction Model Through Domain Adaptation R. Priyatikanto et al. 10.2139/ssrn.4122021
- FarmCan: a physical, statistical, and machine learning model to forecast crop water deficit for farms S. Sadri et al. 10.5194/hess-26-5373-2022
- Multi-model ensemble projections of soil moisture drought over North Africa and the Sahel region under 1.5, 2, and 3 °C global warming A. Elkouk et al. 10.1007/s10584-021-03202-0
22 citations as recorded by crossref.
- River reach-level machine learning estimation of nutrient concentrations in Great Britain C. Tso et al. 10.3389/frwa.2023.1244024
- Enhancing spatial resolution of satellite soil moisture data through stacking ensemble learning techniques M. Tahmouresi et al. 10.1038/s41598-024-77050-0
- Towards an Optimal Representation of Sub‐Grid Heterogeneity in Land Surface Models L. Torres‐Rojas et al. 10.1029/2022WR032233
- A mediation analysis of the linkages between climate variability, water insecurity, and interpersonal violence A. Ross et al. 10.1080/17565529.2023.2186746
- On the potential of Sentinel-1 for sub-field scale soil moisture monitoring T. van Hateren et al. 10.1016/j.jag.2023.103342
- SMAP-HydroBlocks, a 30-m satellite-based soil moisture dataset for the conterminous US N. Vergopolan et al. 10.1038/s41597-021-01050-2
- High‐Resolution Soil Moisture Data Reveal Complex Multi‐Scale Spatial Variability Across the United States N. Vergopolan et al. 10.1029/2022GL098586
- A novel land surface temperature reconstruction method and its application for downscaling surface soil moisture with machine learning O. Güngör Şahin & O. Gündüz 10.1016/j.jhydrol.2024.131051
- Satellite rainfall bias correction incorporating effects on simulated crop water requirements C. Omondi et al. 10.1080/01431161.2024.2326801
- Causal inference of root zone soil moisture performance in drought S. Xue & G. Wu 10.1016/j.agwat.2024.109123
- An agricultural digital twin for mandarins demonstrates the potential for individualized agriculture S. Kim & S. Heo 10.1038/s41467-024-45725-x
- The Benefits of Using State‐Of‐The‐Art Digital Soil Properties Maps to Improve the Modeling of Soil Moisture in Land Surface Models C. Xu et al. 10.1029/2022WR032336
- Improving generalisability and transferability of machine-learning-based maize yield prediction model through domain adaptation R. Priyatikanto et al. 10.1016/j.agrformet.2023.109652
- High-resolution European daily soil moisture derived with machine learning (2003–2020) S. O et al. 10.1038/s41597-022-01785-6
- How much control do smallholder maize farmers have over yield? M. Cecil et al. 10.1016/j.fcr.2023.109014
- A 1 km daily surface soil moisture dataset of enhanced coverage under all-weather conditions over China in 2003–2019 P. Song et al. 10.5194/essd-14-2613-2022
- A comprehensive assessment of in situ and remote sensing soil moisture data assimilation in the APSIM model for improving agricultural forecasting across the US Midwest M. Kivi et al. 10.5194/hess-27-1173-2023
- Is closing the agricultural yield gap a “risky” endeavor? N. Gatti et al. 10.1016/j.agsy.2023.103657
- Drivers of maize yield variability at household level in Northern Ghana and Malawi S. Gachoki & F. Muthoni 10.1080/10106049.2023.2230948
- Improving Generalisability and Transferability of Machine-Learning-Based Maize Yield Prediction Model Through Domain Adaptation R. Priyatikanto et al. 10.2139/ssrn.4122021
- FarmCan: a physical, statistical, and machine learning model to forecast crop water deficit for farms S. Sadri et al. 10.5194/hess-26-5373-2022
- Multi-model ensemble projections of soil moisture drought over North Africa and the Sahel region under 1.5, 2, and 3 °C global warming A. Elkouk et al. 10.1007/s10584-021-03202-0
Latest update: 16 Nov 2024
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.
Drought monitoring and yield prediction often rely on coarse-scale hydroclimate data or...
