Watershed scale soil moisture estimation model using machine learning and remote sensing in a data-scarce context

Marcelo Bueno; Carlos  Baca García; Nilton  Montoya; Pedro  Rau; Hildo  Loayza

doi:10.17268/sci.agropecu.2024.008

Autores

Marcelo Bueno Departamento Académico de Agricultura, Universidad Nacional de San Antonio Abad del Cusco (UNSAAC), Cusco, Perú https://orcid.org/0000-0003-2377-0430
Carlos Baca García Departamento Académico de Agricultura, Universidad Nacional de San Antonio Abad del Cusco (UNSAAC), Cusco, Perú https://orcid.org/0000-0002-8284-0614
Nilton Montoya Departamento Académico de Agricultura, Universidad Nacional de San Antonio Abad del Cusco (UNSAAC), Cusco, Perú https://orcid.org/0000-0002-4147-2579
Pedro Rau Centro de Investigación y Tecnología del Agua (CITA), Universidad de Ingeniería y Tecnología (UTEC), Lima, Perú https://orcid.org/0000-0002-1004-6729
Hildo Loayza International Potato Center (CIP), Lima, Perú https://orcid.org/0000-0002-4145-5453

DOI:

https://doi.org/10.17268/sci.agropecu.2024.008

Palavras-chave:

soil moisture, remote sensing, machine learning, random forest, downscaling

Resumo

Soil moisture content can be used to predict drought impact on agricultural yield better than precipitation. Remote sensing is viable source of soil moisture data in instrument-scarce areas. However, space-based soil moisture estimates lack suitability for daily and high-resolution agricultural, hydrological, and environmental applications. This study aimed to assess the potential of the random forest machine learning technique to enhance the spatial resolution of remote soil moisture products from the SMAP satellite. Models were built using random forest for spatial downscaling of SMAP-L3-E, then visually and statistically evaluated for disaggregation quality. The impact of topography, soil properties, and precipitation on the downscaled soil moisture was examined. The relationship between downscaled soil moisture and in-situ soil moisture was analyzed. The results indicate that the proposed method demonstrated spatial and hydrological coherence, along with a satisfactory downscaling quality. Statistical validation indicated suitable generalization error for scientific and practical use (RMSE < 0.05 cm³ cm^-3). Random forest effectively achieved spatial downscaling of SMAP-L3-E in the study area. Principal component and spatial analysis revealed dependence of downscaled soil moisture on elevation, soil organic carbon content, clay content, and saturated hydraulic conductivity, mainly under near-saturation conditions. Regarding validation against in-situ data, downscaled soil moisture explained in-situ soil moisture well under low soil water content ( = 0.624). Downscaling performance deteriorates for water contents between 0.40 to 0.50 cm³ cm^-3, suggesting inadequacy under near saturation conditions at a daily temporal frequency. However, coarser temporal aggregations (7 to 10 days) yielded an average 0.98 correlation coefficient, regardless of saturation conditions. These results could potentially be applied in irrigation planning, soil physics studies and hydrology monitoring, to forecasting the occurrence of droughts, leaching of contaminants, surface runoff modeling, carbon cycle studies, soil's capacity to store and provide nutrients.

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