Field-Scale Spatial Variability of Soil Moisture and L-Band Brightness Temperature from Land Surface Modeling

Abstract

Although soil moisture is an essential variable within the Earth system and has been extensively investigated, there is still a limited understanding of its spatiotemporal distribution and variability. Thus, the objective of this study is to attempt to reproduce the spatial variability of soil moisture and brightness temperature as measured by point-based and airborne remote sensing measurements. To do so, Environment and Climate Change Canada’s Surface Prediction System (SPS) is run at very high resolution (100 m) over a region of Manitoba (Canada) where an extensive soil moisture experiment took place in the summer of 2012 [SMAP Validation Experiment 2012 (SMAPVEX12)]. Results show that realistic finescale soil texture improves the quality of SPS outputs. Soil moisture spatial average evolution in time is well simulated by SPS. Simulated spatial variability is underestimated when compared to point-based measurements, although results are improved when examined domainwide versus comparisons using grid points corresponding to measurement sites. SPS brightness temperature fields compare well with remote sensing data in terms of spatial variability. It is shown that during drier periods, factors other than soil texture become important with respect to soil moisture spatial variability. However, during periods with plenty of precipitation, soil texture seems essential in improving simulated soil moisture spatial variability at high resolutions. These results support the conclusion that SPS could provide very high–resolution soil moisture products for research and operational purposes if high-resolution soil texture and vegetation products are made available on a larger scale.

Comparative Analysis of High-Resolution Soil Moisture Simulations from the Soil, Vegetation, and Snow (SVS) Land Surface Model Using SAR Imagery Over Bare Soil

“…We note from Figure 8 that the correlations between the soil moisture simulated using SVS and estimated using SAR are higher than those reported between the simulated and measured soil moisture in [13]. This could be related to the fact that the SVS model was evaluated in [13] during the summer, where crops in the experimental site affect the soil moisture simulation results, while in our case the evaluation of the SVS model is conducted during the fall season over bare soil or sparsely-vegetated areas. Also, in [1], evaluation was conducted against fewer point observations, which should be representatively and statistically less stable compared to our study.…”

“…New formulas for land surface albedo and emissivity are also included. In SVS, the soil moisture is represented in the soil layers based on vertical transportation, evapotranspiration, and surface and lateral flows [13,15,23]. The evolution of the volumetric water content w within these soil layers (Figure 4) is given by the following differential equation [15]:…”

“…However, a visual interpretation of the results showed that the modeled soil moisture spatial variability was much weaker than the observed [25]. In another study [13], the performance of an updated version of the SVS model using in situ soil moisture observations was evaluated in a study area which was located within our current experimental site. In that study, the effect of the assumption of homogeneous soil texture versus spatially varying soil texture on the performance of the SVS model was assessed.…”

“…Also, soil moisture influences agricultural productivity, lateral water movement on or below the surface owing to runoff generation, and flood or drought development [7][8][9][10][11][12]. Furthermore, soil moisture is an important parameter for producing accurate meteorological and environmental forecasts, given its interaction with atmospheric variables [13]. The accurate estimation of soil moisture using remotely sensed data [14] or simulations using land surface models [13,15] is necessary at both high and low spatial resolutions.…”

“…Furthermore, soil moisture is an important parameter for producing accurate meteorological and environmental forecasts, given its interaction with atmospheric variables [13]. The accurate estimation of soil moisture using remotely sensed data [14] or simulations using land surface models [13,15] is necessary at both high and low spatial resolutions.…”