Soil moisture is one of the most difficult soil properties to measure because of its large spatial and temporal variability.

Thus it is useful to measure the change of the water content in soil by means of time and space. This cannot be achieved with traditional methods such as in situ measurements but with the technology that remote sensing can offer. Soil moisture can be retrieved through different remote sensing methods using a variety of data such as thermal, infrared, visible and microwave data. There are three different remote sensing groups of methods: Active, Passive and combined Active-Passive remote sensing methods. Several studies and surveys have been conducted and establish Active remote sensing methods as the most appropriate for soil moisture retrieval. 

The first methods developed to retrieve the soil moisture used thermal infrared data. Their main task was to compare simulations from soil–vegetation–atmosphere transfer (SVAT) models with temperatures measured by the satellite sensor. This approach has a couple of important limitations such as the multiple solutions found and the difficulty to estimate the SVAT parameters such as the features of the different soil layers and the vegetation. 

Table – Remote sensing Instruments and satellite platforms (past and current) for global soil moisture retrieval

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The methods that are commonly used to date are the Model-Based methods based on SAR images. These methods represent a general approach to retrieve soil moisture using microwaves measurements, as they are not site-dependent and they can be easily adapted to different experimental conditions handling single or multi-specification SAR data. Soil moisture retrieval from SAR images is a laborious procedure and even after 4 decades from the first SAR-satellite and despite the extensive efforts by researchers it poses many difficulties. The main issue is to isolate the information of interest due to the strong dependence of the backscattering coefficient on the soil properties.

For this purpose a lot of forward and inverse backscattering models have been developed over the years. It is worth to note that backscattering models for soil moisture estimation can be divided into three approaches: Empirical, semi-empirical and theoretical approach. The most known models are Small Perturbation (SP) Model, Physical Optics (PO) Model, Geometrical Optics (GO) Model and the Integral Equation Model (IEM) 

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Finally several approaches for the inversion of the forward models were developed during last decades by the researchers and the most commonly used are based on iterative algorithms such as the Neural Network (NN) and the Fuzzy Model (FM).

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Written by

Mr. Fotias is a Remote Sensing Specialist with specialty in Radar Remote Sensing (SAR). He graduated as a MEng in Informatics and Telecommunications Engineer in 2012, and MSc in Space Technology and Planetary Exploration in 2014 from University of Surrey. From his graduation till today he has work for several private and public entities related to E/O and GNSS application, participating in more than 15 RTD funded Projects. He currently works as a Technical Consultant in Worldwide, European R&D and Commercial Industry Projects. He is a motivated, enthusiast and results-oriented professional in Satellite Remote Sensing (high level of technical competence and ability in SAR theory, applications and general image) with expertise in radar processing algorithms. He has more than 4 years experience in geospatial and remote sensing analysis. Well versed in quantitative analytical research, particularly with ESRI ArcGIS suite and ENVI. Expertise in advanced satellite imaging processing, interpretation, land cover mapping, agriculture applications, time-series analysis, and land use and land cover change (LULC) analysis. He is also involved in managing a dynamic portfolio of Technological Innovation projects

Vasilis Fotias

Remote Sensing Specialist

Vasilis Fotias