Abstract:Linkage of crop patterns with soil salinity will be of great significance for the assessment and management of ecological environment in large irrigation district, as well as be helpful for the protection of cultivated land and food security. To explore the synergic relationship between them, the coupling coordination degree was collaboratively analyzed based on accurate extraction of crop planting information and spatial analysis of soil salinity. Yongji Sub-irrigation Area in Hetao Irrigation District of Inner Mongolia, which had complex crop patterns and severe soil salinization, was selected as the study area. With remote sensing data of Landsat 8 OLI and ground observing data of crop planting survey during the growth period from 2021 to 2022, three classification models were constructed to inverse the crop planting information, which namely were the decision tree (DT), support vector machine (SVM), and random forest (RF), respectively. By comparing the accuracy of the models, an optimal model would be given accompanied by the best result of crop patterns. Combined with the spatial heterogeneity of soil salinity measured from field sampling sites, the synergic relationship between them was further explored quantitatively. Results showed that the classification accuracy of the three models performed as RF> DT> SVM. The overall accuracy and Kappa coefficient of RF model were 92.81%, 0.91 in 2021, and 91.64%, 0.89 in 2022, respectively, which was the biggest among the three models. Therefore, the RF model was ultimately employed as the optimal one to inverse crop patterns in this area. Moreover, soil salinity presented more severe in the northern part than that in the middle and southern parts. The semi-variance function of soil salinity was best fitted by the Gaussian model, and the spatial autocorrelation of soil salinity fluctuated from medium- to strong- level, which indicated that both structural factors and random factors influenced the spatial variation of soil salinity. Crop pattern, as an important factor of random factors, was essential to be further analyzed with soil salinity collaboratively. Two aspects of the collaborative relationship were mainly revealed. On one hand, the spatial heterogeneity of soil salinity determined the spatial characteristics of crop patterns, specifically that sunflower was mainly cultivated in the northern part, while maize, wheat, interplanting, and other crops (e.g. water melon, pepper, tomato, etc.) were mainly distributed in the middle and southern parts. On the other hand, the crops performed different adaptabilities and tolerance to soil salinity, with the average values of soil salt content from big to small as follows: sunflower (0.377% in 2021, and 0.328% in 2022), maize (0.358% in 2021, and 0.319% in 2022), interplanting (0.246% in 2021 and 2022), and wheat (0.259% in 2021, and 0.248% in 2022). As a result, crop patterns interacted with soil salinity in space, jointly determining the sustainable development of agriculture in the irrigation area. In 2021 and 2022, the coupling coordination degree between them was 0.784 and 0.787 in the study area, respectively, which reached a high level. It could be concluded that the development between crop patterns and soil salinity was balanced and coordinated with each other during the observation period. The results would provide some references for optimizing crop planting patterns and improving soil environment in large irrigation district to some extent.
