Abstract
Soil moisture is an important indicator that is widely used in meteorology, hydrology, and agriculture. Two key problems must be addressed in the process of downscaling soil moisture: the selection of the downscaling method and the determination of the environmental variables, namely, the influencing factors of soil moisture. This study attempted to utilize machine learning and data mining algorithms to downscale the Advanced Microwave Scanning Radiometer-Earth Observing System (AMSR-E) soil moisture data from 25 km to 1 km and compared the advantages and disadvantages of the random forest model and the Cubist algorithm to determine the more suitable soil moisture downscaling method for the middle and lower reaches of the Yangtze River Basin (MLRYRB). At present, either the normalized difference vegetation index (NDVI) or a digital elevation model (DEM) is selected as the environmental variable for the downscaling models. In contrast, variables, such as albedo and evapotranspiration, are infrequently applied; nevertheless, this study selected these two environmental variables, which have a considerable impact on soil moisture. Thus, the selected environmental variables in the downscaling process included the longitude, latitude, elevation, slope, NDVI, daytime and nighttime land surface temperature (LST_D and LST_N, respectively), albedo, evapotranspiration (ET), land cover (LC) type, and aspect. This study achieved downscaling on a 16-day timescale based on Moderate Resolution Imaging Spectroradiometer (MODIS) data. A comparison of the random forest model with the Cubist algorithm revealed that the R2 of the random forest-based downscaling method is higher than that of the Cubist algorithm-based method by 0.0161; moreover, the root-mean-square error (RMSE) is reduced by 0.0006 and the mean absolute error (MAE) is reduced by 0.0014. Testing the accuracies of these two downscaling methods showed that the random forest model is more suitable than the Cubist algorithm for downscaling AMSR-E soil moisture data from 25 km to 1 km in the MLRYRB, which provides a theoretical basis for obtaining high spatial resolution soil moisture data.
Highlights
Soil moisture is an important component of both the water cycle and the surface energy cycle; it is an important indicator for reflecting land degradation and characterizing surface drought information [1,2,3,4,5]
1 km spatial resolution according to the soil moisture downscaling model that was constructed with the km spatial resolution according to thekm soilspatial moisture downscaling modeltothat was constructed with
The results showed that the mean R2, root-mean-square error (RMSE), and mean absolute error (MAE) values between the original Advanced Microwave Scanning Radiometer-Earth Observing System (AMSR-E) data and in situ soil moisture were 0.6018, 0.0131 m3 /m3, and 0.0113 m3 /m3, respectively
Summary
Soil moisture is an important component of both the water cycle and the surface energy cycle; it is an important indicator for reflecting land degradation and characterizing surface drought information [1,2,3,4,5]. Soil moisture has been widely used in various environmental applications, such as hydrological modeling, land surface evapotranspiration simulation, Water 2019, 11, 1401; doi:10.3390/w11071401 www.mdpi.com/journal/water. The traditional methods that are used to measure soil moisture, which include the drying and weighing method, negative pressure meter method, neutron meter detection method, indirect resistance method, and time domain reflection (TDR) method, suffer from numerous disadvantages, namely, they are limited by sparse sampling, they have poor representativeness and poor dynamics, and they are time consuming and laborious, while offering only a small monitoring range.
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