Mapping Soil Moisture at a High Resolution over Mountainous Regions by Integrating In Situ Measurements, Topography Data, and MODIS Land Surface Temperatures

Abstract

Hydro-agricultural applications often require surface soil moisture (SM) information at high spatial resolutions. In this study, daily spatial patterns of SM at a spatial resolution of 1 km over the Babao River Basin in northwestern China were mapped using a Bayesian-based upscaling algorithm, which upscaled point-scale measurements to the grid-scale (1 km) by retrieving SM information using Moderate Resolution Imaging Spectroradiometer (MODIS)-derived land surface temperature (LST) and topography data (including aspect and elevation data) and in situ measurements from a wireless sensor network (WSN). First, the time series of pixel-scale (1 km) representative SM information was retrieved from in situ measurements of SM, topography data, and LST. Second, Bayesian linear regression was used to calibrate the relationship between the representative SM and the WSN measurements. Last, the calibrated relationship was used to upscale a network of in situ measured SM to map spatially continuous SM at a high resolution.The upscaled SM data were evaluated against ground-based SM measurements with satisfactory accuracy-the overall correlation coefficient (r), slope, and unbiased root mean square difference (ubRMSD) values were 0.82, 0.61, and 0.025 m 3 /m 3 , respectively. Moreover, when accounting for topography, the proposed upscaling algorithm outperformed the algorithm based only on SM derived from LST (r = 0.80, slope = 0.31, and ubRMSD = 0.033 m 3 /m 3 ). Notably, the proposed upscaling algorithm was able to capture the dynamics of SM under extreme dry and wet conditions. In conclusion, the proposed upscaled method can provide accurate high-resolution SM estimates for hydro-agricultural applications.

Sensitivity of Coarse‐Scale Snow‐Free Land Surface Shortwave Albedo to Topography

“…Mountain covers approximately 24% of the Earth's landscape and plays an important role in the global environment change and human society (Fan et al, ; Grêt‐Regamey et al, ; Payne et al, ; Wen et al, ; Wu et al, ). The land surface albedo over rugged terrain directly controls the shortwave radiation absorbed at the land surface and thus the surface energy balance process over mountainous areas, thereby driving mountainous circulation systems and ecosystem functioning.…”

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

“…Soil thermal inertia (TI) is a factor that reflects the temperature change of soil surface and is highly correlated with SM [17,18]. Based on this principle, simplified TI, namely ATI, is routinely used to estimate the SM of bare and less-vegetated soil [19][20][21][22][23][24][25][26]. There are no strict limitations on the number and time of day of LST observations using the ATI-based model comparing to other methods [27].…”

“…Since then, 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]. The potential of the MODIS-derived land surface dryness index for SM estimation over large geographical areas has been well demonstrated in specific seasons [20,36]. During the early stages of crop growth, the monitoring accuracy of ATI is better than that of TVDI.…”

Comparing Assimilation of Synthetic Soil Moisture Versus C‐Band Backscatter for Hyper‐Resolution Land Surface Modeling

“…Another potential improvement on this simple linear regression method is to include more surface characteristic (such as vegetation index) and remote sensing information (backscatter, Tb) as input, then either apply multi-variable SUN ET AL. regression analysis or neutral networks, which was widely used in the generation of high resolution soil moisture from remote sensing images (Fan et al, 2019;Rodriguez-Fernandez et al, 2019).…”