Downscaling of AMSR-E soil moisture with MODIS products using machine learning approaches

Abstract

Passive microwave remotely sensed soil moisture products, such as Advanced Microwave Scanning Radiometer on the Earth Observing System (AMSR-E) data, have been routinely used to monitor global soil moisture patterns. However, they are often limited in their ability to provide reliable spatial distribution data for soil moisture due to their coarse spatial resolutions. In this study, three machine learning approaches-random forest, boosted regression trees, and Cubist-were examined for the downscaling of AMSR-E soil moisture (25 9 25 km) data over two regions (South Korea and Australia) with different climatic characteristics using moderate resolution imaging spectroradiometer products (1 km), including surface albedo, land surface temperature (LST), Normalized Difference Vegetation Index, Enhanced Vegetation Index, Leaf Area Index, and evapotranspiration (ET). Results showed that the random forest approach was superior to the other machine learning models for downscaling AMSR-E soil moisture data in terms of the correlation coefficient [r = 0.71/0.84 (South Korea/Australia) for random forest, 0.75/0.77 for boosted regression trees, and 0.70/0.61 for Cubist] and root-mean-square error (RMSE = 0.049/0.057, 0.052/0.078, and 0.051/0.063, respectively) through crossvalidation. The ET and LST were identified as the most influential among the six input parameters when estimating AMSR-E soil moisture for South Korea, while ET, albedo, and LST were very useful for Australia. In overall, the downscaled soil moisture with 1 km resolution yielded a higher correlation with in situ observations than the original AMSR-E soil moisture data. The latter appeared higher than the downscaled data in forested areas, possibly due to the overestimation of soil moisture by passive microwave sensors over forests, which implies that downscaling can mitigate such overestimation of soil moisture.

Classification and Mapping of Paddy Rice by Combining Landsat and SAR Time Series Data

“…RF also provides information about relative variable importance. Relative variable importance is based on an increased mean squared error (MSE) using out-of-bag data (i.e., unused training samples for each tree), which is calculated when randomly permuting an independent variable [52,53]. In this study, RF was implemented by R software (https://www.r-project.org/) through Random forest package with default settings (i.e., the number of randomly sampled variables as candidates at each split is the square root of the number of input variables; the minimum size of the terminal node is 1), except for the number of trees.…”

A Machine Learning Based Reconstruction Method for Satellite Remote Sensing of Soil Moisture Images with In Situ Observations

“…One of the most recent projects is the SMAP-soil moisture active passive mission, which is driven by JPL NASA [16]. Other projects include the moderate resolution imaging spectroradiometer (MODIS) and the Advanced Microwave Scanning Radiometer-EOS (AMSR-E) onboard Aqua [17,18], the Soil Moisture and Ocean Salinity (SMOS) mission driven by the ESA [19].…”

Downscaling GLDAS Soil Moisture Data in East Asia through Fusion of Multi-Sensors by Optimizing Modified Regression Trees

“…For local and regional applications of soil moisture data on agriculture and water resources, such coarse resolution data is not particularly useful since it does not provide details on local variations in soil moisture [26,27]. Both microwave satellite sensor-derived soil moisture and reanalysis data have a common problem in that they have low spatial resolution; thus research efforts have been made to improve the spatial resolution of soil moisture data [28][29][30][31][32][33].…”

“…AMSR2 provides soil moisture products at 10 km resolution spatially enhanced from the C-band brightness temperature data by applying the smoothing filter-based intensity modulation (SFIM) downscaling technique using the high resolution Ka-band measurements [34]. Other downscaling approaches are based on the disaggregation of passive microwave soil moisture using high resolution optical/thermal sensor data [32,[35][36][37][38]. Optical/thermal data has been used to downscale soil moisture since the concept of the 'universal triangle' was introduced [39,40].…”

“…Srivastava et al [58] conducted SMOS soil moisture downscaling using the MODerate Resolution Imaging Spectroradiometer (MODIS) Land Surface Temperature (LST) through SVM, Relevance Vector Machine, ANN, and Generalized Linear Model (GLM). Im et al [32] downscaled AMSR-E soil moisture using MODIS LST, NDVI, Enhanced Vegetation Index (EVI), Leaf Area Index (LAI), Evapotranspiration (ET), and albedo through rule-based machine learning approaches, including random forest, Cubist, and boosted regression trees.…”