In this study, seven filter algorithms were compared. The Lee sigma method was more suitable for estimating SM than the other filtering methods under different land cover types. Firstly, we used a combination of roughness and the dual-polarized Sentinel-1A backscattering coefficients (VV and VH) to estimate SM in bare soil areas. Secondly, we employed water cloud model (WCM) to remove the influence of vegetation signals on the land surface backscattering and estimate SM in vegetation-covered areas. SM was also retrieved by Modified soil moisture monitoring index (MSMMI) and Modified perpendicular drought index (MPDI) of Sentinel-2A images. The results show that MSMMI can more accurately monitor SM in bare soil areas, which was slightly better than SAR results. The SAR backscattering coefficients after the removal of vegetation influence by WCM can more precisely estimate SM in vegetation-covered areas, which is significantly better than MSMMI and MPDI, especially in high vegetation cover areas. Optics and SAR differ in their abilities to estimate SM under different land cover, but the powerful fitting ability of machine learning can make full use of their advantages. We employed the generalized regression neural network (GRNN), support vector regression (SVR), random forest regression (RFR), and deep neural network (DNN) algorithms to estimate SM combining Sentinel-1A with Sentinel-2A images. The estimation accuracies of SM by regression algorithms were higher than those by the semi-empirical SAR and optical models. The accuracy of estimated SM by DNN was higher than that of GRNN and RFR, which were better than SVR.

This article aims to explore the applicability of SMMI (soil moisture monitoring index), MSMMI (modified soil moisture monitoring index), PDI (perpendicular drought index), and MPDI (modified perpendicular drought index) in estimating soil moisture (SM) in farmland. The random forest classifier was used to obtain two-stage land cover types maps. The sensitivity of Sentinel-2 spectral bands to the measured SM at a depth of 0-5 cm was optimized by random forest regression. According to the sensitive bands, SMMI and PDI from different feature spaces were constructed to explore their feasibility for monitoring SM under different land cover types. Second, fractional vegetation cover (FVC) in the study area was estimated by nine kinds of FVC estimation models and compared with the measured FVC. The effects of different FVC methods on estimating SM by MSMMI and MPDI were evaluated. The results show that red edge and short-wave infrared (SWIR) bands of Sentinel-2 had irreplaceable effects on the land cover classification. In terms of monitoring SM in bare soil areas, the SM indices with SWIR bands had high correlations with measured SM. For vegetation-covered areas, MSMMI from the FVCgr model (dimidiate pixel model with red edge bands) and the Short wave infrared1-Short wave infrared2 feature space had the highest correlation with the measured 0-5 cm depth SM. Whether vegetation-covered areas or bare soil areas, the combination of red edge and SWIR bands can effectively improve the estimation accuracy of SM. MSMMI can be used as the best SMMI in the study area. Sentinel-2 images, with great potential, can effectively estimate SM at a depth of 0-5 cm in farmland with complex environments.

Traditional NDVI-T_s space is triangular or trapezoidal, but Liu et al. (2015) discovered that the NDVI-T_s space was bi-parabolic when the study area was covered with low biomass vegetation. Moreover, the numerical value of the indicator was considered in most of the study when the drought conditions in the space domain were evaluated. In addition, quantitatively assessing the spatial-temporal changes of the drought was not enough. In this study, first, we used MODIS NDVI and T_s data to reexamine if the NDVI-T_s space with “time” and a single pixel domain is bi-parabolic in the Shaanxi province of China, which is vegetated with low biomass to high biomass. This is compared with the triangular NDVI-T_s space and one of the well-known drought indexes called the temperature-vegetation index (TVX). The results demonstrated that dry and wet edges exhibited a parabolic shape again in scatter plots of T_s and NDVI in the Shaanxi province, which was linear in the triangular NDVI-T_s space. The Temperature Vegetation Dryness Index (TVDI_c) was obtained from bi-parabolic NDVI-T_s andTVDI_t was obtained from the triangular NDVI-T_s space and TVX were compared with 10-cm depth relative soil moisture. By estimating the 10-cm depth soil moisture, TVDI_c was better than TVDI_t, which were all apparently better than TVX. Second, combined with MODIS data, the drought conditions of the study area were assessed by TVDI_c between 2000 to 2016. Spatially, the drought in the Shaanxi Province between 2000 to 2016 were mainly distributed in the northwest, North Shaanxi, and the North and East Guanzhong plain. The drought area of the Shaanxi province accounted for 31.95% in 2000 and 27.65% in 2016, respectively. Third, we quantitatively evaluated the variation of the drought status by using Gradient-based Structural Similarity (GSSIM) methods. The area of the drought conditions significantly changed and moderately changed at 5.34% and 40.22%, respectively, between 2000 and 2016. Finally, the possible reasons for drought change were discussed. The change of precipitation, temperature, irrigation, destruction or betterment of vegetation, and the enlargement of opening mining, etc., can lead to the variations of drought.