Abstract
Timely and effective estimation and monitoring of soil moisture (SM) provides not only an understanding of regional SM status for agricultural management or potential drought but also a basis for characterizing water and energy exchange. The apparent thermal inertia (ATI) and Temperature Vegetation Dryness Index (TVDI) are two widely used indices to reflect SM from remote sensing data. While the ATI-based model is routinely used to estimate the SM of bare soil and sparsely vegetated areas, the TVDI-based model is more suitable for areas with dense vegetation coverage. In this study, we present an iteration procedure that allows us to identify optimal Normalized Difference Vegetation Index (NDVI) thresholds for subregions and estimate their relative soil moisture (RSM) using three models (the ATI-based model, the TVDI-based model, and the ATI/TVDI joint model) from 1 January to 31 December 2017, in the Chinese Loess Plateau. The initial NDVI (NDVI0) was first introduced to obtain TVDI value and two other thresholds of NDVIATI and NDVITVDI were designed for dividing the whole area into three subregions (the ATI subregion, the TVDI subregion, and the ATI/TVDI subregion). The NDVI values corresponding to maximum R-values (correlation coefficient) between estimated RSM and in situ RSM measurements were chosen as optimal NDVI thresholds after performing as high as 48,620 iterations with 10 rounds of 10-fold cross-calibration and validation for each period. An RSM map of the whole study area was produced by merging the RSM of each of the three subregions. The spatiotemporal and comparative analysis further indicated that the ATI/TVDI joint model has higher applicability (accounting for 36/38 periods) and accuracy than the ATI-based and TVDI-based models. The highest average R-value between the estimated RSM and in situ RSM measurements was 0.73 ± 0.011 (RMSE—root mean square error, 3.43 ± 0.071% and MAE—mean absolute error, 0.05 ± 0.025) on the 137th day of 2017 (DOY—day of the year, 137). Although there is potential for improved mapping of RSM for the entire Chinese Loess Plateau, the iteration procedure of identifying optimal thresholds determination offers a promising method for achieving finer-resolution and robust RSM estimation in large heterogeneous areas.
Highlights
Soil moisture (SM) is a key hydrological variable influencing water availability for vegetation and plays a fundamental role in land-atmosphere interactions [1]
Temperature Vegetation Dryness Index (TVDI) is derived from vegetation indices (e.g., Normalized Difference Vegetation Index (NDVI)—normalized difference vegetation index) and land surface temperature (LST) [8,9,10], whereas apparent thermal inertia (ATI) is obtained from LST and albedo [11,12,13,14,15,16]
We proposed an iteration procedure that allows us to identify optimal NDVI thresholds by performing 10 rounds of 10-fold cross-calibration and to estimate the region-specific relative soil moisture of the Chinese Loess Plateau in 2017 using different models—namely the ATI-based model, the TVDI-based model, and the ATI/TVDI joint model
Summary
Soil moisture (SM) is a key hydrological variable influencing water availability for vegetation and plays a fundamental role in land-atmosphere interactions [1]. Based on the relationship between remotely sensed LST and NDVI (generally called NDVI-LST space) [28], Sandholt et al defined TVDI [29], which can represent the degree of SM in the area with vegetation coverage. Since this index has been adopted by many researchers to estimate SM from Moderate Resolution Imaging Spectroradiometer (MODIS) data on global and regional scales [18,30,31,32,33,34,35]. Few attempts have been made to test the joint use of the ATI-based and TVDI-based models and whether such joint can yield a more accurate SM estimation should be further investigated [17,39,40]
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