Soil Moisture Estimation for the Chinese Loess Plateau Using MODIS-derived ATI and TVDI

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.

Improving Soil Moisture Estimation by Identification of NDVI Thresholds Optimization: An Application to the Chinese Loess Plateau

“…We carried out NDVI ranges of the iteration in the programing design used for loops statement and it was easy to run programming using the Figure 2. Flowchart of relative soil moisture (RSM) estimation by the apparent thermal inertia (ATI)-based models, the ATI/TVDI-temperature vegetation dryness index joint models, and the TVDI-based models with Criterion 2 (adapted from [35]). NDVI 0 was used for computing TVDI, and both NDVI ATI and NDVI TVDI were applied for dividing the entire Chinese Loess Plateau (CLP).…”

“…In addition, the NDVI threshold (NDVI 0 ), the lower limit of NDVI, below which the data are excluded when we derive dry/wet edges, would be used for generating TVDI [35]. To obtain optimal NDVI thresholds, NDVI 0 and NDVI ATI , both ranging from 0 to 0.5, and NDVI TVDI , ranging from 0 to 0.7 with an interval of 0.01, were successively tested in the iterative process ( Figure 2).…”

“…Between two edges (dry/wet edges), all intermediate conditions can occur, and all RSM conditions can conse- D ry E dg e Figure 3. The NDVI-LST triangle space defining the dry/wet edges on DOY-day of the year 113 over the CLP (adapted from [35,48,51]). Theoretically, in the triangular figure (pink region area), the base edge of the triangle with maximum evapotranspiration pixels, and the top edge of the triangle with zero evapotranspiration pixels are displayed.…”

“…A detailed explanation regarding RSM is presented in Section 3.1.2. Yuan et al [35] applied the MODIS-derived ATI and TVDI models to subregional RSM estimation for the CLP. They highlighted the identification of three optimal NDVI thresholds (NDVI 0 was for computing TVDI, both NDVI ATI and NDVI TVDI were used for dividing the whole CLP) and concluded that the ATI/TVDI joint models were more applicable (accounting for 36/38 8-day periods) and accurate (R: 0.75 ± 0.008 on DOY-day of the year (hereafter referred to as DOY), 313) than the ATI-based and TVDIbased models.…”

“…They highlighted the identification of three optimal NDVI thresholds (NDVI 0 was for computing TVDI, both NDVI ATI and NDVI TVDI were used for dividing the whole CLP) and concluded that the ATI/TVDI joint models were more applicable (accounting for 36/38 8-day periods) and accurate (R: 0.75 ± 0.008 on DOY-day of the year (hereafter referred to as DOY), 313) than the ATI-based and TVDIbased models. Here, the limiting condition (NDVI 0 ≤ NDVI ATI ≤ NDVI TVDI ) when selecting optimal NDVI thresholds, as proposed by Yuan et al [35], is regarded as Criterion 1. It failed to select the highest R in certain subregions, resulting in no corresponding subregional RSM maps being produced, thereby causing incomplete RSM maps for 8-day periods.…”