The spacing of Gansu Province from the eastern to western regions is very large and adjacent to the Qinghai–Tibet Plateau. On the one hand, Agriculture in western area is irrigated rather than non-irrigated agriculture in eastern area. On the other hand, elevation where adjacent to the Tibetan Plateau is much higher than other places. In this study, remote sensing drought indices, such as temperature vegetation dryness index (TVDI), vegetation condition index (VCI), temperature condition index (TCI), perpendicular drought index (PDI), and modified perpendicular drought index (MPDI), were calculated using historical MODIS data. The applicability of these remote sensing indices was preliminarily studied by comparing the Relative Soil Moisture (RSM) of the sites. Results showed that:1) In whole area, irrigated areas and high-altitude areas, the remote sensing indices have different degree of indication for the spatial distribution of RSM in the superficial layer in spring, summer, and autumn. Among them, TVDI has the best indication, followed by VCI and TCI, and PDI and MPDI have very limited indication. But TVDI has no indication in May in irrigated areas at all. 2) None of them can indicate the temporal variation characteristics of the RSM in the irrigated areas, and TVDI and TCI based on the surface temperature can indicate the temporal variation of the 10 and 20 cm-deep RSM in the high-altitude areas. In general, TVDI is a good indicator for RSM in the superficial layer in Gansu Province during spring, summer, and autumn.
As an important component of the Earth’s ecosystem, soil moisture plays a vital role in the global water cycle and serves as an important parameter in the study of hydrology, meteorology, and agroecology. Based on the energy balance theory of underlying surface, the atmospheric temperature data recorded by an automatic weather station as well as unmanned aerial vehicle (UAV)-borne thermal infrared and multispectral remote-sensing data were used to establish inversion models of relative soil moisture at different depths based on remote-sensing UAV data and date from near-ground quadrats, respectively. Spatial differences and data accuracy verification were then performed using the 2017 spring wheat moisture data as a control. The results showed that: (1) the relative moisture of farmland soil can be effectively estimated using the proposed soil moisture inversion model. In terrestrial ecosystems, the ratio of actual to potential evapotranspiration, which is often used to characterize potential drought levels, is linearly correlated to soil moisture at different depths; (2) during the inversion of farmland soil moisture, the UAV-based observation method is superior to the near-ground quadrat observation method in both efficiency and accuracy. In addition, the relative soil moisture estimation model based on UAV data has a high accuracy, with R2 reaching 0.629, and a root mean square error (RMSE) of <0.100; and (3) the number and size of quadrats are important factors affecting the inversion accuracy. The data collected by the UAVs covered a wide range and had high spatial matching degree at the field scale. Especially, during estimation of the relative moisture of surface soil (0 to 10 and 0 to 20 cm), the linear fitting between the inversion model based on UAV data and the measured value was optimal. The error was minimal (RMSE < 0.07) and R2 was >0.714, so this method is more suitable for estimating and dynamically monitoring relative soil moisture of farmland at the field scale.
Radarsat-2 Synthetic Aperature Radar (SAR) remote sensing data were used to record soil surface moisture and evaluate
the utility of a cross polarization (VV/VH) combination. Studies were conducted at Dingxi, in the semi-arid region of the
Loess Plateau, China. We combined these data with MODIS optical data, used a Water-Cloud model to correct for the
influence of vegetation, and then estimated the soil moisture under crop cover. For bare surfaces, the value of the cross
polarization combination model was highly correlated to the measurement of soil moisture at 10~20 cm depth (R=0.75,
P<0.01). The correlations between estimated values and the measured soil moisture at 0~10 cm and 20~30 cm depths
were lower but still significant (R=0.47 and R=0.52, respectively, P<0.05). For soil surfaces covered with vegetation the
model significantly underestimated soil moisture. After vegetation removal, the correlation coefficient increased from
0.30 to 0.70, the standard deviation decreased from 4.99 to 3.05, and the accuracy of the soil moisture model improved.
Most soil moisture readings in the study area were 10~30% and these were consistent with the actual field moisture
levels. Improving the accuracy of soil moisture readings in agricultural fields using optical and microwave remote
sensing data will promote increased use of this technology.
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