Abstract
Temporal and spatial variability of soil moisture has an important impact on hydrological processes in mountainous areas. Understanding such variability requires soil moisture datasets at multiple temporal and spatial scales. Remote sensing is a very effective method to obtain surface (~5 cm depth) soil moisture at the regional scale but cannot directly measure soil moisture at deep soil layers (>5 cm depth) currently. This study chose the upstream of the Heihe River Watershed in the Qilian Mountain Ranges in Northwest China as the study area to estimate the profile soil moisture (0–70 cm depth) at the regional scale using satellite Vegetation Index (NDVI) and Land Surface Temperature (LST) products. The study area was divided into 31 zones according to the combination of altitude, vegetation and soil type. Long-term in situ soil moisture observation stations were set up at each of the zones. Soil moisture probe, ECH2O, was used to collect soil moisture at five layers (0–10, 10–20, 20–30, 30–50 and 50–70 cm) continuously. Multiple linear regression equations of time series MODIS (Moderate-resolution Imaging Spectroradiometer) NDVI, LST and soil moisture were developed for each of the five soil layers at the 31 zones to estimate the soil moisture (0–70 cm) on a regional scale with a spatial resolution of 1 km2 and a temporal resolution of 16-d from October, 2013 to September, 2016. The correlation coefficient R of the regression equations was between 0.47 and 0.94, the RMSE was 0.03, indicating that the estimation method based on the MODIS NDVI and LST data was suitable and could be applied to alpine mountainous areas with complex topography, soil and vegetation types. The overall pattern of soil moisture spatial distribution indicated that soil moisture was higher in the eastern region than in the western region, and the soil moisture content in the whole study area was 14.5%. The algorithm and results provide novel applications of remote sensing to support soil moisture data acquisition and hydrological research in mountainous areas.
Highlights
Soil moisture is an essential component of the terrestrial water cycle, and serves as a critical link between the precipitation, surface water, groundwater and vegetation water [1,2,3,4,5,6]
Tobin et al [27] downscaled AMSR-E and ERS-CCI (European remote sensing satellite-climate change initiative) soil moisture data using an exponential filter (ExpF) with soil moisture index derived from MODIS NDVI
The seasonal NDVIAG and the residual NDVIRES were consistent with the growth pattern of the vegetation [48]
Summary
Soil moisture is an essential component of the terrestrial water cycle, and serves as a critical link between the precipitation, surface water, groundwater and vegetation water [1,2,3,4,5,6]. For alpine and heterogeneous areas, the complexity of the underlying surface leads to a series of uncertainties, such as the inconsistent linear relationship between soil moisture and ATI, resulting in large estimation errors In addition to these deficiencies, current remote sensing methods mainly concentrate on the relationship between the surface reflection value and the in situ observations to estimate the surface soil moisture at the regional scale [23]. Land Surface Temperature (LST) is a key parameter for agricultural drought monitoring, hydrological research and urban thermal environment [34,35] Both MODIS LST and NDVI data are widely used for soil moisture estimation based on the TVDI and ATI methods (e.g., [21,36]). Compared with the aforementioned soil moisture products, both MODIS LST and NDVI have higher
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