Systematic Review of Remote Sensing Prediction Models and Tools for Estimating Surface Soil Moisture Content of an Area - P. Yamakili; M. R. Nicholaus; K. A. Greyson (Journal of Applied Sciences and Environmental Management)
Soil Moisture is a critical parameter for water resource management, agriculture, and disaster prediction. Different methods are used to estimate Soil Moisture. Hence, the objective of this paper was to systematically review remote sensing (RS) prediction models and tools for estimating surface soil moisture (SM) content of an area using different scholar ‘s methodologies, and their performance. Survey of previous studies have highlighted some general areas and explored RS methods for soil moisture estimation, focusing on both active and passive sensors. Studies have also discussed the principles, strengths, and limitations of different techniques. However, there are some key areas that were less covered and need attention. As a result, this systematic review paper presents a wide range of comparative assessments of RS SM estimation models and tools by assessing their technique and methods, their performance Evaluation level (Coefficient of Determination R), the environment where the model could suitably perform better and the essential parameters considered for improving the known Machine Learning models for SM prediction further attention as discussed under this paper.