Remotely Sensed High-Resolution Soil Moisture and Evapotranspiration: Bridging the Gap Between Science and Society

Jingyi Huang; Vinit Sehgal; Laura V. Alvarez; Luca Brocca; Shuohao Cai; Rui Cheng; Xinghua Cheng; Jinyang Du; Bassil El Masri; K. Arthur Endsley; Yilin Fang; Jie Hu; Mahesh Jampani; Md Golam Kibria; Gerbrand Koren; Lingcheng Li; Laibao Liu; Jiafu Mao; Hernan A. Moreno; Angela Rigden; Mingjie Shi; Xiaoying Shi; Yaoping Wang; Xi Zhang; Joshua B. Fisher

doi:10.1029/2024WR037929

Remotely Sensed High-Resolution Soil Moisture and Evapotranspiration: Bridging the Gap Between Science and Society

Jingyi Huang
, Vinit Sehgal
, Laura V. Alvarez
, Luca Brocca
, Shuohao Cai
, Rui Cheng
, Xinghua Cheng
, Jinyang Du
, Bassil El Masri
, K. Arthur Endsley
, Yilin Fang
, Jie Hu
, Mahesh Jampani
, Md Golam Kibria
, Gerbrand Koren
, Lingcheng Li
, Laibao Liu
, Jiafu Mao
, Hernan A. Moreno
, Angela Rigden

Mingjie Shi, Xiaoying Shi, Yaoping Wang, Xi Zhang, Joshua B. Fisher

Earth Systems Modeling

Research output: Contribution to journal › Review article › peer-review

3 Scopus citations

Abstract

This paper reviews the current state of high-resolution remotely sensed soil moisture (SM) and evapotranspiration (ET) products and modeling, and the coupling relationship between SM and ET. SM downscaling approaches for satellite passive microwave products leverage advances in artificial intelligence and high-resolution remote sensing using visible, near-infrared, thermal-infrared, and synthetic aperture radar sensors. Remotely sensed ET continues to advance in spatiotemporal resolutions from MODIS to ECOSTRESS to Hydrosat and beyond. These advances enable a new understanding of bio-geo-physical controls and coupled feedback mechanisms between SM and ET reflecting the land cover and land use at field scale (3–30 m, daily). Still, the state-of-the-science products have their challenges and limitations, which we detail across data, retrieval algorithms, and applications. We describe the roles of these data in advancing 10 application areas: drought assessment, food security, precision agriculture, soil salinization, wildfire modeling, dust monitoring, flood forecasting, urban water, energy, and ecosystem management, ecohydrology, and biodiversity conservation. We discuss that future scientific advancement should focus on developing open-access, high-resolution (3–30 m), sub-daily SM and ET products, enabling the evaluation of hydrological processes at finer scales and revolutionizing the societal applications in data-limited regions of the world, especially the Global South for socio-economic development.

Original language	English
Article number	e2024WR037929
Journal	Water Resources Research
Volume	61
Issue number	5
DOIs	https://doi.org/10.1029/2024WR037929
State	Published - May 2025

Funding

J. Huang was supported by the USDA Agriculture and Food Research Initiative Foundational Program (Grant 2023‐67021‐40007) and Marvin T. Beatty Professorship in Soil Science from the University of Wisconsin‐Madison. V. Sehgal is funded in parts by the Louisiana Agricultural Experiment Station, the Hatch Program of USDA‐NIFA, and USDA‐NRCS grant (NR247217XXXXG001). J.B. Fisher was supported in part by NASA's ECOSTRESS Science and Applications Team (ESAT) (80NSSC23K0309) and NASA's Earth Science Applications: Water Resources (WATER) (80NSSC22K0936) programs. B. El Masri was supported by NASA Kentucky EPSCOR under NASA award No: 80NSSC20M0047 and NASA EPSCoR FY22 R3 under NASA award No: 80NSSC22M0247, DOE (Environmental System Science: DE‐SC0022228), and NSF (MRI: 2215877 and EPSCoR Fellowship: 2327374). J. Mao, Y. Wang, and X. Shi were supported by the Reducing Uncertainties in Biogeochemical Interactions through Synthesis and Computing Scientific Focus Area (RUBISCO SFA) project funded through the Earth and Environmental Systems Sciences Division of the Biological and Environmental Research Office in the US Department of Energy (DOE) Office of Science. Oak Ridge National Laboratory (ORNL) is supported by the Office of Science of the DOE under Contract No. DE‐AC05‐00OR22725. This research is also an outcome of the Soil Moisture Working Group supported by ORNL RUBISCO SFA. L. Brocca was supported by the European Union “Open Earth Monitor Cyberinfrastructure” project (grant agreement No. 101059548), the Italian Department of Civil Protection, and the European Space Agency project DTE Hydrology Evolution (Grant ESA 4000136272/21/I539 EF—CCN N. 1). L. V. Alvarez was supported by the National Science Foundation (NSF) CAREER award 2239550 and the National Oceanic and Atmospheric Administration (NOAA) Cooperative Science Center Educational Partnership Program with Minority Serving Institutions (MSI) award NA22SEC4810016, and the Army Research Office (ARO) Cooperative Agreement Numbers W911NF‐24‐1‐0296 and W911NF‐23‐2‐0046. H. A. Moreno was funded by the National Oceanic and Atmospheric Administration (NOAA) Cooperative Science Center Educational Partnership Program with Minority Serving Institutions (MSI) award NA22SEC4810016. B. El Masri was supported by NASA Kentucky under NASA award No: 80NSSC20M0047, NASA Kentucky R3 award, DOE (Environmental System Science: DE‐SC0022228), and NSF (NSF EPSCoR Fellowship: 2327374). The views and conclusions contained in this document are those of the authors and should not be interpreted as representing the official policies, either expressed or implied, of the ARO, NSF, NOAA or the U.S. Government. The U.S. Government authorizes and distributes reprints for Government purposes notwithstanding any copyright notation herein. K.A. Endsley was supported in part by NASA's Early Career Investigator Program (Grant 80NSSC24K1051). M. Jampani was supported by the CGIAR Initiative on Asian Mega‐Deltas (AMD) and the CGIAR Trust Fund contributors ( https://www.cgiar.org/funders ). Y. Fang, L. Li, and M. Shi were supported by the Earth and Biological Sciences Directorate (EBSD)'s Laboratory Directed Research and Development (LDRD) Program at Pacific Northwest National Laboratory (PNNL). PNNL is a multi‐program national laboratory operated for the U.S. Department of Energy (DOE) by Battelle Memorial Institute under Contract No. DE‐AC05‐76RL01830. The authors would like to thank Professor Ole Wendroth (University of Kentucky) and an anonymous reviewer for the constructive comments on the original version of the manuscript. J. Huang was supported by the USDA Agriculture and Food Research Initiative Foundational Program (Grant 2023-67021-40007) and Marvin T. Beatty Professorship in Soil Science from the University of Wisconsin-Madison. V. Sehgal is funded in parts by the Louisiana Agricultural Experiment Station, the Hatch Program of USDA-NIFA, and USDA-NRCS grant (NR247217XXXXG001). J.B. Fisher was supported in part by NASA's ECOSTRESS Science and Applications Team (ESAT) (80NSSC23K0309) and NASA's Earth Science Applications: Water Resources (WATER) (80NSSC22K0936) programs. B. El Masri was supported by NASA Kentucky EPSCOR under NASA award No: 80NSSC20M0047 and NASA EPSCoR FY22 R3 under NASA award No: 80NSSC22M0247, DOE (Environmental System Science: DE-SC0022228), and NSF (MRI: 2215877 and EPSCoR Fellowship: 2327374). J. Mao, Y. Wang, and X. Shi were supported by the Reducing Uncertainties in Biogeochemical Interactions through Synthesis and Computing Scientific Focus Area (RUBISCO SFA) project funded through the Earth and Environmental Systems Sciences Division of the Biological and Environmental Research Office in the US Department of Energy (DOE) Office of Science. Oak Ridge National Laboratory (ORNL) is supported by the Office of Science of the DOE under Contract No. DE-AC05-00OR22725. This research is also an outcome of the Soil Moisture Working Group supported by ORNL RUBISCO SFA. L. Brocca was supported by the European Union “Open Earth Monitor Cyberinfrastructure” project (grant agreement No. 101059548), the Italian Department of Civil Protection, and the European Space Agency project DTE Hydrology Evolution (Grant ESA 4000136272/21/I539 EF—CCN N. 1). L. V. Alvarez was supported by the National Science Foundation (NSF) CAREER award 2239550 and the National Oceanic and Atmospheric Administration (NOAA) Cooperative Science Center Educational Partnership Program with Minority Serving Institutions (MSI) award NA22SEC4810016, and the Army Research Office (ARO) Cooperative Agreement Numbers W911NF-24-1-0296 and W911NF-23-2-0046. H. A. Moreno was funded by the National Oceanic and Atmospheric Administration (NOAA) Cooperative Science Center Educational Partnership Program with Minority Serving Institutions (MSI) award NA22SEC4810016. B. El Masri was supported by NASA Kentucky under NASA award No: 80NSSC20M0047, NASA Kentucky R3 award, DOE (Environmental System Science: DE-SC0022228), and NSF (NSF EPSCoR Fellowship: 2327374). The views and conclusions contained in this document are those of the authors and should not be interpreted as representing the official policies, either expressed or implied, of the ARO, NSF, NOAA or the U.S. Government. The U.S. Government authorizes and distributes reprints for Government purposes notwithstanding any copyright notation herein. K.A. Endsley was supported in part by NASA's Early Career Investigator Program (Grant 80NSSC24K1051). M. Jampani was supported by the CGIAR Initiative on Asian Mega-Deltas (AMD) and the CGIAR Trust Fund contributors (https://www.cgiar.org/funders). Y. Fang, L. Li, and M. Shi were supported by the Earth and Biological Sciences Directorate (EBSD)'s Laboratory Directed Research and Development (LDRD) Program at Pacific Northwest National Laboratory (PNNL). PNNL is a multi-program national laboratory operated for the U.S. Department of Energy (DOE) by Battelle Memorial Institute under Contract No. DE-AC05-76RL01830. The authors would like to thank Professor Ole Wendroth (University of Kentucky) and an anonymous reviewer for the constructive comments on the original version of the manuscript.

Keywords

evapotranspiration
high-resolution
machine learning
remote sensing
soil moisture
water resources management

Access to Document

10.1029/2024WR037929

Cite this

Huang, J., Sehgal, V., Alvarez, L. V., Brocca, L., Cai, S., Cheng, R., Cheng, X., Du, J., El Masri, B., Endsley, K. A., Fang, Y., Hu, J., Jampani, M., Kibria, M. G., Koren, G., Li, L., Liu, L., Mao, J., Moreno, H. A., ... Fisher, J. B. (2025). Remotely Sensed High-Resolution Soil Moisture and Evapotranspiration: Bridging the Gap Between Science and Society. Water Resources Research, 61(5), Article e2024WR037929. https://doi.org/10.1029/2024WR037929

@article{3016ebbfae40411e87767cf7fae78996,

title = "Remotely Sensed High-Resolution Soil Moisture and Evapotranspiration: Bridging the Gap Between Science and Society",

abstract = "This paper reviews the current state of high-resolution remotely sensed soil moisture (SM) and evapotranspiration (ET) products and modeling, and the coupling relationship between SM and ET. SM downscaling approaches for satellite passive microwave products leverage advances in artificial intelligence and high-resolution remote sensing using visible, near-infrared, thermal-infrared, and synthetic aperture radar sensors. Remotely sensed ET continues to advance in spatiotemporal resolutions from MODIS to ECOSTRESS to Hydrosat and beyond. These advances enable a new understanding of bio-geo-physical controls and coupled feedback mechanisms between SM and ET reflecting the land cover and land use at field scale (3–30 m, daily). Still, the state-of-the-science products have their challenges and limitations, which we detail across data, retrieval algorithms, and applications. We describe the roles of these data in advancing 10 application areas: drought assessment, food security, precision agriculture, soil salinization, wildfire modeling, dust monitoring, flood forecasting, urban water, energy, and ecosystem management, ecohydrology, and biodiversity conservation. We discuss that future scientific advancement should focus on developing open-access, high-resolution (3–30 m), sub-daily SM and ET products, enabling the evaluation of hydrological processes at finer scales and revolutionizing the societal applications in data-limited regions of the world, especially the Global South for socio-economic development.",

keywords = "evapotranspiration, high-resolution, machine learning, remote sensing, soil moisture, water resources management",

author = "Jingyi Huang and Vinit Sehgal and Alvarez, \{Laura V.\} and Luca Brocca and Shuohao Cai and Rui Cheng and Xinghua Cheng and Jinyang Du and \{El Masri\}, Bassil and Endsley, \{K. Arthur\} and Yilin Fang and Jie Hu and Mahesh Jampani and Kibria, \{Md Golam\} and Gerbrand Koren and Lingcheng Li and Laibao Liu and Jiafu Mao and Moreno, \{Hernan A.\} and Angela Rigden and Mingjie Shi and Xiaoying Shi and Yaoping Wang and Xi Zhang and Fisher, \{Joshua B.\}",

note = "Publisher Copyright: {\textcopyright} 2025. The Author(s).",

year = "2025",

month = may,

doi = "10.1029/2024WR037929",

language = "English",

volume = "61",

journal = "Water Resources Research",

issn = "0043-1397",

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Huang, J, Sehgal, V, Alvarez, LV, Brocca, L, Cai, S, Cheng, R, Cheng, X, Du, J, El Masri, B, Endsley, KA, Fang, Y, Hu, J, Jampani, M, Kibria, MG, Koren, G, Li, L, Liu, L, Mao, J, Moreno, HA, Rigden, A, Shi, M, Shi, X, Wang, Y, Zhang, X & Fisher, JB 2025, 'Remotely Sensed High-Resolution Soil Moisture and Evapotranspiration: Bridging the Gap Between Science and Society', Water Resources Research, vol. 61, no. 5, e2024WR037929. https://doi.org/10.1029/2024WR037929

TY - JOUR

T1 - Remotely Sensed High-Resolution Soil Moisture and Evapotranspiration

T2 - Bridging the Gap Between Science and Society

AU - Huang, Jingyi

AU - Sehgal, Vinit

AU - Alvarez, Laura V.

AU - Brocca, Luca

AU - Cai, Shuohao

AU - Cheng, Rui

AU - Cheng, Xinghua

AU - Du, Jinyang

AU - El Masri, Bassil

AU - Endsley, K. Arthur

AU - Fang, Yilin

AU - Hu, Jie

AU - Jampani, Mahesh

AU - Kibria, Md Golam

AU - Koren, Gerbrand

AU - Li, Lingcheng

AU - Liu, Laibao

AU - Mao, Jiafu

AU - Moreno, Hernan A.

AU - Rigden, Angela

AU - Shi, Mingjie

AU - Shi, Xiaoying

AU - Wang, Yaoping

AU - Zhang, Xi

AU - Fisher, Joshua B.

PY - 2025/5

Y1 - 2025/5

N2 - This paper reviews the current state of high-resolution remotely sensed soil moisture (SM) and evapotranspiration (ET) products and modeling, and the coupling relationship between SM and ET. SM downscaling approaches for satellite passive microwave products leverage advances in artificial intelligence and high-resolution remote sensing using visible, near-infrared, thermal-infrared, and synthetic aperture radar sensors. Remotely sensed ET continues to advance in spatiotemporal resolutions from MODIS to ECOSTRESS to Hydrosat and beyond. These advances enable a new understanding of bio-geo-physical controls and coupled feedback mechanisms between SM and ET reflecting the land cover and land use at field scale (3–30 m, daily). Still, the state-of-the-science products have their challenges and limitations, which we detail across data, retrieval algorithms, and applications. We describe the roles of these data in advancing 10 application areas: drought assessment, food security, precision agriculture, soil salinization, wildfire modeling, dust monitoring, flood forecasting, urban water, energy, and ecosystem management, ecohydrology, and biodiversity conservation. We discuss that future scientific advancement should focus on developing open-access, high-resolution (3–30 m), sub-daily SM and ET products, enabling the evaluation of hydrological processes at finer scales and revolutionizing the societal applications in data-limited regions of the world, especially the Global South for socio-economic development.

AB - This paper reviews the current state of high-resolution remotely sensed soil moisture (SM) and evapotranspiration (ET) products and modeling, and the coupling relationship between SM and ET. SM downscaling approaches for satellite passive microwave products leverage advances in artificial intelligence and high-resolution remote sensing using visible, near-infrared, thermal-infrared, and synthetic aperture radar sensors. Remotely sensed ET continues to advance in spatiotemporal resolutions from MODIS to ECOSTRESS to Hydrosat and beyond. These advances enable a new understanding of bio-geo-physical controls and coupled feedback mechanisms between SM and ET reflecting the land cover and land use at field scale (3–30 m, daily). Still, the state-of-the-science products have their challenges and limitations, which we detail across data, retrieval algorithms, and applications. We describe the roles of these data in advancing 10 application areas: drought assessment, food security, precision agriculture, soil salinization, wildfire modeling, dust monitoring, flood forecasting, urban water, energy, and ecosystem management, ecohydrology, and biodiversity conservation. We discuss that future scientific advancement should focus on developing open-access, high-resolution (3–30 m), sub-daily SM and ET products, enabling the evaluation of hydrological processes at finer scales and revolutionizing the societal applications in data-limited regions of the world, especially the Global South for socio-economic development.

KW - evapotranspiration

KW - high-resolution

KW - machine learning

KW - remote sensing

KW - soil moisture

KW - water resources management

UR - https://www.scopus.com/pages/publications/105005025653

U2 - 10.1029/2024WR037929

DO - 10.1029/2024WR037929

M3 - Review article

AN - SCOPUS:105005025653

SN - 0043-1397

VL - 61

JO - Water Resources Research

JF - Water Resources Research

IS - 5

M1 - e2024WR037929

ER -

Remotely Sensed High-Resolution Soil Moisture and Evapotranspiration: Bridging the Gap Between Science and Society

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