Abstract
Soil salinization, one of the most severe global land degradation problems, leads to the loss of arable land and declines in crop yields. Monitoring the distribution of salinized soil and degree of salinization is critical for management, remediation, and utilization of salinized soil; however, there is a lack of thorough assessment of various data sources including remote sensing and landscape characteristics for estimating soil salinity in arid and semi-arid areas. The overall goal of this study was to develop a framework for estimating soil salinity in diverse landscapes by fusing information from satellite images, landscape characteristics, and appropriate machine learning models. To explore the spatial distribution of soil salinity in southern Xinjiang, China, as a case study, we obtained 151 soil samples in a field campaign, which were analyzed in laboratory for soil electrical conductivity. A total of 35 indices including remote sensing classifiers (11), terrain attributes (3), vegetation spectral indices (8), and salinity spectral indices (13) were calculated or derived and correlated with soil salinity. Nine were used to model and estimate soil salinity using four predictive modelling approaches: partial least squares regression (PLSR), convolutional neural network (CNN), support vector machine (SVM) learning, and random forest (RF). Testing datasets were divided into vegetation-covered and bare soil samples and were used for accuracy assessment. The RF model was the best regression model in this study, with R2 = 0.75, and was most effective in revealing the spatial characteristics of salt distribution. Importance analysis and path modeling of independent variables indicated that environmental factors and soil salinity indices including digital elevation model (DEM), B10, and green atmospherically resistant vegetation index (GARI) showed the strongest contribution in soil salinity estimation. This showed a great promise in the measurement and monitoring of soil salinity in arid and semi-arid areas from the integration of remote sensing, landscape characteristics, and using machine learning model.
Highlights
Soil salinization is a severe environmental hazard posing a considerable threat to global land degradation [1,2]
This research aimed to (a) explore suitable covariates to reduce interference by soil moisture, vegetation, and other factors on the remote sensing estimation of soil salinization; (b) estimate soil salinity by employing partial least squares regression (PLSR), convolutional neural network (CNN), support vector machine (SVM), and random forest (RF) models using factors derived from remote sensing imagery and landscape characteristics; (c) quantitatively estimate and map the soil salinization in diverse landscapes of southern Xinjiang, China, as a case study area with high accuracy; and (d) analyze and identify the important factors
There was a high variation in EC from 2.09 dS m−1 to 46.70 dS m−1, both extreme values found in bare soil
Summary
Soil salinization is a severe environmental hazard posing a considerable threat to global land degradation [1,2]. Soil salinity results from a complicated progression related to climate, groundwater, topography, and human activities [5,6]. Salt-induced degradation is more pronounced in semi-arid and arid regions. Human activities such as tillage in a natural environment characterized by low precipitation, high soil evaporation, and high groundwater level [7,8] make cultivated soils more vulnerable to serious salinization problems. The demand for natural resources and food from the increasing population require more land to be used for farming, including marginal, vulnerable, and already degraded lands such deserts and lands affected by salinity [9,10]. Careful monitoring, quantitative assessment and analysis, and mapping to reveal the temporal and spatial distribution of soil salinity have become pressing concerns for land management and reclamation of salinized soil [11,12]
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