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Soil Moisture (SM) is an environmental descriptor, which acts as the affiliation between the atmosphere and the earth’s surface. Various SM retrieval methods are developed to abolish the influence of vegetation cover attenuation, surface roughness, and scattering to find an association among SM and backscatter coefficient. To understand the relationship between various vegetation parameters and backscatter coefficient poses a great challenge in SM retrieval. Hence, an efficacious SM retrieval method is afforded using the proposed Sail Squirrel Search Optimization-based Deep Convolutional Neural Network (SSSO-based Deep CNN). Here, the proposed SSSO is derived by concatenating the Sail Fish Optimization (SFO) with Squirrel Search Algorithm (SSA). The Deep CNN performs the process of SM retrieval using vegetation indices. The fitness measure of the proposed optimization enables to find the best solution to update the weights of the classifier for increasing the efficiency of the retrieval mechanism. By training Deep CNN with the proposed optimization, the soil moisture of an area is effectively retrieved. However, the proposed SSSO-based Deep CNN obtained minimal estimation error and minimal RMSE of 0.550 and 0.726 using sentinel-1 data, respectively.

The vegetation index mainly reflects the difference between the visible and near-infrared reflectance and soil background, quantitative description of vegetation growth conditions. Data was preprocessed, 6 vegetation index was extracted, vegetation index trend was analysis in time scales based on 2013 year 5-10 month MODIS09QA image of northern Tibet grassland growth intervals. Based on the main factors of grassland growth, we described a partition of the grassland, Overlay statistics MSAVI mean information of each suitability partition , explored the weight of major influence factors on the spatial scale. It showed that: In temporal dimension, MSAVI vegetation index accord with normal distribution, R2 is 0.885, the grass growth during the changing process of vegetation coverage was consistent. In spatial scale Relative weight of impact factors: orographic influence weight was the largest, followed by water, human activities influences least.