Soil moisture content (SMC) is one of the most significant variables in drought assessment and climate change. Near-real time and accurate monitoring of this quantity by means of remote sensing (RS) is a useful strategy at regional scales. So far, various methods for the SMC estimation using a RS data have been developed. The use of spectral information based on a small range of electromagnetic spectrum in the form of a single index cannot be a suitable way to estimate the moisture content in the optical and thermal RS. Single use of each index has its own limitations. These limitations can include the effect of atmospheric conditions, solar illumination geometry, topographic conditions and soil characteristics. Therefore, the use of all potential of the electromagnetic spectrum (visible to short-wave infrared bands) in the form of regression combinations of spectral indices might be useful in improving the accuracy of SMC estimation. In this study, at first, the correlation of 20 indices commonly used in soil moisture estimation studies with in situ soil moisture measurements were evaluated. In the next step, based on the correlation results, the indices gradually were added into the soil moisture linear regression and the accuracy of each stage was evaluated. The best SMC estimation model was included the linear regression made of LST, VSDI, NDWI and SASI indices. This ended up with and improvement accuracy (RMSE = 0.048). Soil texture is one of the most important factors in the estimation of SMC by means of RS data especially in optical and thermal regions of spectrum. On the other hand, due to the fact that the soils have different levels of porosities, it seems that the SMC modelling should be based on soil texture. Therefore, the SMC model was evaluated for three dataset, medium textured, moderately coarse texture and coarse texture soils. The results showed that the medium texture soils have a profound relationship with in situ measured compared to coarse texture soils (RRMSE=29%…, RRMSE=0.032). On the other words, the SMC model is not generalizable for all soil types. The results showed an inverse relationship between the accuracy in the SMC estimation and the soil particle size. In other words, the accuracy of the SMC model decreased by the increase in soil particle size. In the case of medium texture soils, better response to the SMC estimation have been seen in optical bands. Coarse texture soils such as sandy soil, because of porosity, water penetrates rapidly and freely inside the soil due to the force of gravity and show a lower water content capacity. On the contrary, medium texture soils have the ability to retain more water in their textures and the length of capillary rise in these soils is greater than those of coarse texture soils. Thus, moisture variations in this type of soils have a greater effect on the soil spectral responses compared to the coarse texture soils where the results of the SMC modelling for loamy medium texture soils approves this.
Development of an Index-based Regression Model for Soil Moisture Estimation Using MODIS Imageries by Considering Soil Texture Effects
Journal of Geomatics Science and Technology, Volume:9 Issue:1, 2019
173 - 187
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