Exploring PlanetScope Satellite Capabilities for Soil Salinity Estimation and Mapping in Arid Regions Oases

Abstract

One reason for soil degradation is salinization in inland dryland, which poses a substantial threat to arable land productivity. Remote-sensing technology provides a rapid and accurate assessment for soil salinity monitoring, but there is a lack of high-resolution remote-sensing spatial salinity estimations. The PlanetScope satellite array provides high-precision mapping for land surface monitoring through its 3-m spatial resolution and near-daily revisiting frequency. This study’s use of the PlanetScope satellite array is a new attempt to estimate soil salinity in inland drylands. We hypothesized that field observations, PlanetScope data, and spectral indices derived from the PlanetScope data using the partial least-squares regression (PLSR) method would produce reasonably accurate regional salinity maps based on 84 ground-truth soil salinity data and various spectral parameters, like satellite band reflectance, and published satellite salinity indices. The results showed that using the newly constructed red-edge salinity and yellow band salinity indices, we were able to develop several inversion models to produce regional salinity maps. Different algorithms, including Boruta feature preference, Random Forest algorithm (RF), and Extreme Gradient Boosting algorithm (XGBoost), were applied for variable selection. The newly constructed yellow salinity indices (YRNDSI and YRNDVI) had the best Pearson correlations of 0.78 and −0.78. We also found that the proportions of the newly constructed yellow and red-edge bands accounted for a large proportion of the essential strategies of the three algorithms, with Boruta feature preference at 80%, RF at 80%, and XGBoost at 60%, indicating that these two band indices contributed more to the soil salinity estimation results. The best PLSR model estimation for different strategies is the XGBoost-PLSR model with coefficient of determination (R2), root mean square error (RMSE), and ratio of performance to deviation (RPD) values of 0.832, 12.050, and 2.442, respectively. These results suggest that PlanetScope data has the potential to significantly advance the field of soil salinity research by providing a wealth of fine-scale salinity information.

Application of Machine Learning Algorithms for Digital Mapping of Soil Salinity Levels and Assessing Their Spatial Transferability in Arid Regions

“…The study outcomes have revealed the substantial transferability of satellite-based radar and optical environmental variables within an arid region, substantiating their potential for generating beneficial outputs. For transferability of soil salinity levels in arid regions using ML algorithms, the PlanetScope satellite [96] can offer important opportunities to capture the spatial variability of salinity [97].…”

Application of a Bi-Directional Gated Recurrent Unit Combined with a Recurrent Neural Network Model Based on Fusion Attention Mechanism in Estimating Soil Salinity

“…In addition, soil salinization also affects surface and underground water quality, animal and plant communities, and human health, posing a serious threat to the ecological environment and biosphere [13]. Therefore, rapid acquisition and accurate monitoring of soil surface salinity information have important reference significance for achieving comprehensive utilization and productivity improvement of soil [14].…”

UAV hyperspectral analysis of secondary salinization in arid oasis cotton fields: effects of FOD feature selection and SOA-RF

“…Prior studies commonly utilized conventional satellite-mounted multispectral sensors or synthetic aperture radar (SAR) to acquire remotely sensed imagery to invert soil salinity ( Wang J. et al., 2019 ). Despite their easy accessibility, these data sources often exhibit drawbacks like low spatial ( Tan et al., 2023 ) and spectral resolution alongside lengthy revisit periods ( Fan et al., 2021 ). While the lower spatial resolution and longer revisit period are advantageous for soil salinity detection across vast regions ( Sahbeni et al., 2023 ), precision agriculture requires a method apt for monitoring soil salinity in smaller areas.…”