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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. [ABSTRACT FROM AUTHOR]

Accurate assessment of the spatial distribution and severity of soil salinization has long plagued local governments and researchers in the arid parts of Xinjiang Uygur Autonomous Region (XJUAR). The emergence of machine learning has brought hope to this research field, such as Random Forest (RF) and Stochastic Gradient Treeboost (SGT),however, which are few applications to the quantitative assessment of soil salinization. Therefore, in order to evaluate the accuracy level of the two algorithms for predicting soil salinity, twenty-seven environmental subsets were designed. Each data set is calculated using both RF and SGT to produce an optimal set of variables. The simulation results from 70.37% (19/27) of the subsets showed that the predicted value of soil salinity from SGT is closer to the observed value than is that from RF. The statistics of all datasets showed that the average values of R2 value for RF and SGT were 0.38 and 0.40, the average Root Mean Squared Error (RMSE) value were 28.59 and 27.46, and the Ratio of Prediction to Deviation (RPD) averages were 1.20 and 1.24, respectively. The important dominant factor were topographic variables with coarse resolution, temperature and vegetation indices, land use and landform. [ABSTRACT FROM AUTHOR]

• The SOC in two depths were mapped at a resolution of 1 km and five-year interval during 1980–2100. • Three climate scenarios of SSP 1–1.9, SSP2-4.5 and SSP5-8.5 in CMIP6 were considered. • The top 20 cm and 100 cm SOC of China both acted as carbon sinks during 1980–2020. • The SOC will fluctuate significantly in the future under different climate scenarios. • Under the SSP2-4.5 scenario, SOC presented a larger likelihood of being a carbon source in most region. Revealing historical changes in soil organic carbon (SOC) and exploring its future status are important for safeguarding soil health and food security, giving full play to the service function of soil ecosystems, and coping with climate change. However, there is still a gap in our understanding of the SOC stocks in China and their spatial patterns in response to future climate change. Therefore, we attempted to fill this knowledge gap using a large amount of soil observation data, digital soil mapping technology, and global circulation models from Coupled Model Inter-comparison Project phase 6 (CMIP6). In this study, a random forest model was selected to construct the spatial relationship between SOC in the top 0–20 cm (SOC020) and 0–100 cm (SOC0100) of the soil and 21 environmental factors. Spatiotemporal changes in SOC from 1980 to 2100 were revealed at a resolution of 1 km and five-year interval for three climate scenarios in CMIP6. The cross-validation results indicated acceptable predictions for both depths of SOC; however, relatively large prediction uncertainties were observed for SOC0100 in the Tibetan Plateau and northeastern China. The mean values of SOC020 and SOC0100 over the last four decades were 35.77 and 84.62 Tg, respectively, and showed carbon sinks at the national scale, with accumulation rates of 0.05 Pg yr-1 and 0.036 Pg y-1 for the two depths. Compared with historical values (1980–2020), SOC will fluctuate significantly in the future under different climate scenarios. Among them, SOC showed a slow increasing trend at both depths under the SSP1-1.9 low emission scenario, while SOC presented a decreasing trend under the SSP2-4.5 and SSP5-8.5 medium–high emission scenarios. In particular, in most of China under the SSP2-4.5 scenario, SOC0100 presented a larger likelihood of being a carbon source. This study provides a reference for exploring the response of soil carbon pools to climate change and evaluating the effectiveness of land management and ecological protection. [ABSTRACT FROM AUTHOR]

Soil salinization is a major soil health issue globally. Over the past 40 years, extreme weather and increasing human activity have profoundly changed the spatial distribution of land use and water resources across seven oases in southern Xinjiang, China. However, knowledge of the spatial distribution of soil salinization in this region has not been updated since a land survey in the 1970s to 1980s (the harmonized world soil database, HWSD) due to scarce observational data. Now, given the uncertainty raised by near future climate change, it is important to develop quick, reliable and accurate estimates of soil salinity at larger scales for a better manage strategy to the local fragile ecosystem that with limited land and water resources. This study collected electromagnetic induction (EMI) readings near surface soil to update on the spatial distribution and changes of water and salt in the region and to map apparent electrical conductivity (ECa, mS·m−1), in four coil configurations: vertical dipole in 1.50 m (ECav01) and 0.75 m (ECav05), so as the horizontal dipole in 0.75 m (ECah01) and 0.37 m (ECah05), then all the ECa coil configurations were modeled with random forest algorithm. The validation results showed an R2 range of 0.77–0.84 and an RMSE range of 115.17–142.76 mS·m−1. The validation accuracy of deep ECa dipole (ECah01, ECav05, and ECav01) was greater than that of shallow ECa (ECah05), as the former integrated a thicker portion of the subsurface. The range of EC spatial variability that can be explained by ECa is 0.19–0.36 (farmland, mean value is 0.28), grassland is 0.16–0.49 (shrub/grassland, mean value is 0.34), and bare land is 0.28–0.70 (bare land, mean value is 0.56). Among them, ECav01 has the best predictive ability. As the depth increased, the influence of soil-related variables decreased, and the contribution of climate-related variables increased. The main factor affecting ECa variation was climate-related variables, followed by vegetation-related variables and soil-related variables. Scatter plot show ECa was significantly correlated with ECe_HWSD_030 (0–30 cm, r = 0.482, p < 0.01) and ECe_HWSD_30100 (30–100 cm, r = 0.556, p < 0.01). The predicted spatial ECa maps were similar to the ECe values from HWSD, but also implies that the distribution of soil water and salt has undergone tremendous changes since 1980s. The study demonstrates that EMI data provide a reliable and cost-effective tool for obtaining high-resolution soil maps that can be used for better land evaluation and soil improvement at larger scales. [ABSTRACT FROM AUTHOR]