Spaceborne GNSS-R Retrieving on Global Soil Moisture Approached by Support Vector Machine Learning

Abstract

Abstract. GNSS Reflectometry system is an excellent to sense soil moisture content. In recent, GNSS-R technique could be aided to detect soil moisture contents but still have many difficulities issues, most especially vegetation impact. Soil moisture observing is a major concept for enhancing the sustainability of the earth’s system and process. On retrieving soil moisture from spaceborne GNSS-R technology has been challenging to the system, retrieving model and geophysical parameters. In this research, we use the Support Vector Machine (SVM) method to retrieve global soil moisture, the TDS-1 Delay Doppler Map (DDM) and the AVHRR Normalized Difference Vegetation Index (NDVI) imagery as inputs and the Soil Moisture and Ocean Salinity (SMOS) soil moisture data as a reference to retrieve global SM daily basis. The results have shown that the squared correlation coefficient (R) values are much higher in TDS-1 fused with NDVI than using DDM alone, which indicates that vegetation impact has effectively weakened. The feasibility of this approach could provide the performance for spaceborne GNSS-R retrieving to soil moisture analysis.

A Systematic Review of Machine Learning Techniques for GNSS Use Cases

“…Other ML-based methods include Bayesian Regularization Neural Network (BRNN) used in [35]. RF, and SVM used in [36], SVM used in [37], ANN in [38], and ANN, RF, and SVM in [39].…”

Analysis of CYGNSS Soil Moisture Retrieval Based on ANN over a Selected Region in Ethiopia

“…They only take reflectivity, elevation angle, and dielectric constant into account. Lwin et al used the Support Vector Machine (SVM) approach to estimate global Soil Moisture (SM) [11]. Senyurek et al [12] and Eroglu et al [1] have looked at the usage of non-parametric, non-linear machine learning algorithms.…”