Soil salinization is a progressive degradation process that spreads globally and leads to a decline in soil fertility. Assessing the scale of salinization is crucial for sustainable agricultural development and saline‐land rehabilitation. In this study, we proposed a support vector regression spatial (SVR‐S) model that utilizes spatial dependence information to predict soil salinity over the irrigation area of the Shule River Basin in northwestern China. To investigate the performance of the SVR‐S model, 50 soil samples were collected in the field. Semivariograms of soil salinity (measured as total salt content and ion concentrations) were constructed to measure spatial dependence. The SVR‐S model was compared with the original SVR model and the geographically weighted regression (GWR) model regarding the salinity prediction ability. The soil salinity in the experimental area demonstrated a strong spatial dependence pattern. The SVR‐S model delivered a better performance than the SVR‐O and GWR. SVR‐S showed a correlation coefficient R of 0.87 and a root mean square error (RMSE) of 1.83%, while the performance of SVR‐O (R = 0.75, RMSE = 3.32%) and GWR (R = 0.73, RMSE = 3.47%) was comparatively poor. Topographic indices integrating spatial information contributed the most to the estimation of salinity in the study area. This study provides a new approach to integrating spatial information for accurate soil salinity mapping.
<p>Dune field landscape patterns serve as an important signs of aeolian processes, such as wind conditions, sediment supply, and so on. A novel framework was proposed and evaluated for automatic dune detection and classification with remotely sensed images. The framework consists of two main steps: (1) The first step is to detect sand dunes from remote sensing images by SandUnet, which is firstly proposed in this paper. SandUnet, a Convolutional Neural Network (CNN), has a similar network structure with Attention U-net but modifies its attention gate module. In SandUnet, the input signals' information is not compressed as in the Attention U-net, therefore, the nuanced color and texture information of dunes are preserved. This paper demonstrated that SandUnet has better detection accuracy than other popular CNNs such as FCN, U-net, U-net++, and Attention U-net. (2) The second step is to compute the image similarity scores through MobileNet between each dune detection result image and the representative images of 6 different types of dunes. Then, each dune detection result image is classified into a dune type automatically. This paper applied the proposed framework to Taklimkan Desert in China. The average classification accuracy rate is around 80%, which proves the usefulness of this framework in automatic, no-cost, and accurate sand dune classification.</p>
<p>Soil salinity mapping is essential for sustainable land development and water resources management. In situ sampling is time-consuming, laborious, and restricted by geographical conditions. Therefore, an efficient and accurate model is necessary to monitor and assess the spatio-temporal dynamic salinization at regional a scale. In this study, Shule River Basin (SLRB) is taken as an example to develop the soil salinity mapping model based on Landsat 8 OLI images using random forest (RF) algorithms. A series of extended soil salinity indexes (ESSIs) were generated by combining any two, three, or four spectral bands were combined in expressions that include one or more of the arithmetic operations: addition, subtraction, multiplication, division, square and rooting form. The features selected from ESSIs outperformed the features selected from soil salinity indexes (SSIs) used in references. The best selected indexes are (B7^2-B5^2)^0.5, (B4^2+B5^2-B6^2)^0.5, (B1*B5-B4*B6/(B1*B5+B4*B6))^0.5,(B2*B6-B3*B7/( B2*B6+B3*B7))^0.5. In addition, three partition sampling methods of the training set and validation set for long-tail distribution problems are compared. The results showed that the resampling method considering the long-tail distribution performs better than systematic resampling and random k-fold cross-validation. The regional soil salinity mapping results showed that most areas are seriously salt-affected in the whole basin, especially along the river and the southeast mountainous area, where the soil salinity classes are highly and even over-extremely saline. This study could have implications for agricultural schemes planning and salinization control.</p>
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