High Spatial-Temporal Resolution Estimation of Ground-Based Global Navigation Satellite System Interferometric Reflectometry (GNSS-IR) Soil Moisture Using the Genetic Algorithm Back Propagation (GA-BP) Neural Network

Abstract

Soil moisture is one of the critical variables in maintaining the global water cycle balance. Moreover, it plays a vital role in climate change, crop growth, and environmental disaster event monitoring, and it is important to monitor soil moisture continuously. Recently, Global Navigation Satellite System interferometric reflectometry (GNSS-IR) technology has become essential for monitoring soil moisture. However, the sparse distribution of GNSS-IR soil moisture sites has hindered the application of soil moisture products. In this paper, we propose a multi-data fusion soil moisture inversion algorithm based on machine learning. The method uses the Genetic Algorithm Back-Propagation (GA-BP) neural network model, by combining GNSS-IR site data with other surface environmental parameters around the site. In turn, soil moisture is obtained by inversion, and we finally obtain a soil moisture product with a high spatial and temporal resolution of 500 m per day. The multi-surface environmental data include latitude and longitude information, rainfall, air temperature, land cover type, normalized difference vegetation index (NDVI), and four topographic factors (elevation, slope, slope direction, and shading). To maximize the spatial and temporal resolution of the GNSS-IR technique within a machine learning framework, we obtained satisfactory results with a cross-validated R-value of 0.8660 and an ubRMSE of 0.0354. This indicates that the machine learning approach learns the complex nonlinear relationships between soil moisture and the input multi-surface environmental data. The soil moisture products were analyzed compared to the contemporaneous rainfall and National Aeronautics and Space Administration (NASA)’s soil moisture products. The results show that the spatial distribution of the GA-BP inversion soil moisture products is more consistent with rainfall and NASA products, which verifies the feasibility of using this experimental model to generate 500 m per day the GA-BP inversion soil moisture products.

A Systematic Review of Machine Learning Techniques for GNSS Use Cases

“…In, ML models have been applied to other environmental remote sensing applications such as landslide monitoring/prediction, estimating nearshore water depths, weather forecast by monitoring and forecasting precipitable water vapor (PWV), and forecast hourly intense rainfall [11,[55][56][57][58][59][60][61][62].…”

“…7). The ML models have been compared with several conventional non-ML models: regression model [14,80,101,159,160], brute force approach [143], traditional statistical approaches [60,94,[161][162][163][164], classical KF [129], Bayes-optimal rule [118], least square (LS)-based approach [40], Saastamoinen model [110], autoregressive model and a traditional LEO propagation model (EKF-STAN) [146], conventional wind speed retrieval method [43], Maximum-Likelihood Power-Distortion (PD-ML) [165], BERNESE 5.2 [114], CYGNSS [44], Hydrostaticseasonal-time (HST) model [49], Statistical Theta method [51][52][53]166], MAPGEO2004 geoid model [73], GNSS-IR soil moisture [58], Autoregressive (AR) and Autoregressive Moving Average (ARMA) [167], ERA-Interima global atmospheric reanalysis (now ERA5 reanalysis) [107], Empirical linear algorithms (LRM and LLM) [59], International Reference Ionosphere (IRI) 2016 model [168], NeQuick and IRI-2001 global TEC model [169][170][171], EKF-based integration scheme [172], CODE GIMs (Global Ionospheric Maps) [173], autoregressive integrated moving average (ARIMA), and quadratic polynomial (QP) models [174], least square regression algorithms (LSR) and bi-ha...…”

A Real-Time Prediction Approach to Deep Soil Moisture Combining GNSS-R Data and a Water Movement Model in Unsaturated Soil

“…The L-band signals carried by Global Navigation Satellite Systems (GNSSs) are highly sensitive to soil moisture, making them particularly suitable for monitoring soil moisture variations [42]. By utilizing signal power or delay as attributes and actual soil moisture values as labels, inversion models based on navigation signals can be established through the combination of empirical dielectric constant models, Support Vector Machines (SVMs), Random Forest algorithms, and neural network methods such as BP and Deep Belief Networks [43][44][45]. Therefore, the introduction of soil moisture data retrieved from GNSS-R signals largely overcomes the limitations of traditional soil moisture measurement methods in terms of small effective measurement area and a lack of representativeness, meeting the demand for all-weather autonomous monitoring.…”

Research on Flow Decision-Making Model of Plant Protection UAV Based on Feature Selection

“…Yajie Shi used BP neural network optimized based on a genetic algorithm (GA-BP) to input geographical environment and terrain elements into the model to obtain the soil moisture prediction model. The cross-validation r value of the model was 0.86, and the ubrmse was 0.03 [17]. Jian Gu used GA-BP neural network to establish the irrigation water model of corn yield under different irrigation regimes under subsurface drip irrigation.…”