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.

Normalized difference vegetation index (NDVI) and land surface temperature (T_s) data from the multitemporal Moderate Resolution Imaging Spectroradiometer (MODIS) were used to analyze the NDVI-T_s space. Unlike the traditional triangular or trapezoidal NDVI-T_s space, the NDVI-T_s space in this study was biparabolic when NDVI values below 0.15 were included, describing areas that are vegetated but have low biomass due to arid conditions. Moreover, the NDVI-T_s biparabolic space is slightly better than the NDVI-T_s triangular space at discerning soil moisture (validation at a 10-cm depth). Linear relationships exist between TVDI_C obtained from the NDVI-T_s biparabolic space and relative soil moisture with R² > 0.28, while correlation coefficient between TVDI_T obtained from the triangular NDVI-T_s space and relative soil moisture is R² > 0.08. The soil moisture situations in Henan Province from February 26, 2011 to May 16, 2011 and in the Shendong mining area on October 8, 2010 were evaluated based on the temperature vegetation dryness index (TVDI) obtained from the NDVI-T_s biparabolic space. Moreover, the general spatiotemporal features of soil moisture conditions in Henan Province and the Shendong mining area were revealed. Résumé. Des données de l’indice de végétation par différence normalisé (NDVI) et de la température de la surface terrestre (T_s) du spectroradiomètre imageur à résolution moyenne «Moderate Resolution Imaging Spectroradiometer» (MODIS) ont été utilisées pour analyser l’espace NDVI-T_s. Contrairement à l’espace traditionnel NDVI-T_s qui est triangulaire ou trapézoïdal, l’espace NDVI-T_s dans cette étude était biparabolique lorsque les valeurs de NDVI inférieures à 0,15 ont été incluses, décrivant les régions qui ont un couvert végétal, mais qui ont une faible biomasse en raison des conditions arides. En outre, l’espace biparabolique NDVI-T_s est légèrement mieux que l’espace triangulaire NDVI-T_s pour discerner l’humidité du sol (validation à une profondeur de 10 cm). Des relations linéaires existent entre le TVDI_C obtenu à partir du NDVI-T_s biparabolique et l’humidité du sol relative avec R² > 0,28, tandis que le coefficient de corrélation entre le TVDI_T obtenu à partir de l’espace NDVI-T_s triangulaire et l’humidité du sol relative est R² > 0,08. L’humidité du sol dans la province du Henan du 26 février 2011 au 16 mai 2011 et dans la région d’exploitation minière Shendong le 8 octobre 2010 a été évaluée sur la base de l’indice de température-sécheresse de la végétation (TVDI) obtenu à partir de l’espace biparabolique NDVI-T_s. En outre, les caractéristiques générales spatio-temporelles de conditions d’humidité du sol dans la province du Henan et la zone minière Shendong ont été révélées.

Soil moisture has become the dominant factor in the Shendong mining area for vegetation growth and recovery. Soil moisture monitoring index (SMMI) and the Perpendicular Drought Index (PDI) were widely used to monitor soil moisture, which were all obtained from the NIR-Red spectral space. To validate whether SMMI and PDI were all can be applied to evaluate the soil moisture conditions, SPOT-6 images on October 5, 2015 in Shendong mining area are used to calculate the SMMI and PDI over ground measuring points. The SMMI and PDI are then compared to ground-measured soil moisture data with the satellite overpass. This study concludes that the SMMI and PDI provide quite similar results for soil moisture monitoring and they are quite suitable to assess the soil moisture statue in 0-5-cm-depth. The highest correlation of R2=0.54 for the SMMI and R2=0.52 for the PDI is obtained when compared with relative soil moisture from 0 to 5 cm soil depth. It is evident from the results showing the spatial distribution of drought in Shendong mining area that the SMMI and PDI are highly accordant with landform types and underlying.