Zhang Jinming, Ding Jianli, Wang Jinjie, Zhang Zihan, Tan Jiao, Ge Xiangyu
College of Geography and Remote Sensing Sciences, Xinjiang University, Urumqi, China.
Xinjiang Key Laboratory of Oasis Ecology, Xinjiang University, Urumqi, China.
Front Plant Sci. 2024 Nov 12;15:1437390. doi: 10.3389/fpls.2024.1437390. eCollection 2024.
Soil salinization represents a significant challenge to the ecological environment in arid areas, and digital mapping of soil salinization as well as exploration of its spatial heterogeneity with crop growth have important implications for national food security and salinization management. However, the machine learning models currently used are deficient in mining local information on salinity and do not explore the spatial heterogeneity of salinity impacts on crops. This study developed soil salinization inversion models using CNN (Convolutional Neural Network), LSTM (Long Short-Term Memory Network), and RF (Random Forest) models based on 97 field samples and feature variables extracted from Landsat-8 imagery. By evaluating the accuracy, the best-performing model was selected to map soil salinity at a 30m resolution for the years 2013 and 2022, and to explore the relationship between soil electrical conductivity (EC) values and the expansion of cotton fields as well as their spatial correlation. The results indicate that:(1) The CNN performs best in prediction, with an R of 0.84 for the training set and 0.73 for the test set, capable of capturing more local salinity information. (2) The expansion of cotton fields has reduced the level of soil salinization, with the area of severely salinized and saline soils in newly added cotton fields decreasing from 177.91 km and 381.46 km to 19.49 km and 1.12 km, respectively. (3) Regions with long-term cotton cultivation and newly reclaimed cotton fields exhibit high sensitivity and vulnerability to soil salinity. This study explores the excellent performance of deep learning in salinity mapping and visualizes the spatial distribution of cotton fields that are highly sensitive to soil salinity, providing a scientific theoretical basis for accurate salinity management.
土壤盐渍化对干旱地区的生态环境构成重大挑战，土壤盐渍化的数字制图以及探索其与作物生长的空间异质性对国家粮食安全和盐渍化管理具有重要意义。然而，目前使用的机器学习模型在挖掘盐分局部信息方面存在不足，且未探究盐分对作物影响的空间异质性。本研究基于97个野外样本和从Landsat-8影像中提取的特征变量，利用卷积神经网络（CNN）、长短期记忆网络（LSTM）和随机森林（RF）模型开发了土壤盐渍化反演模型。通过评估精度，选择性能最佳的模型以30米分辨率绘制2013年和2022年的土壤盐分图，并探究土壤电导率（EC）值与棉田扩张之间的关系及其空间相关性。结果表明：（1）CNN在预测方面表现最佳，训练集的R值为0.84，测试集的R值为0.73，能够捕获更多局部盐分信息。（2）棉田的扩张降低了土壤盐渍化程度，新增棉田中重度盐渍化和盐土面积分别从177.91平方千米和381.46平方千米减少至19.49平方千米和1.12平方千米。（3）长期种植棉花的地区和新垦棉田对土壤盐分表现出高敏感性和脆弱性。本研究探究了深度学习在盐分制图方面的优异性能，并可视化了对土壤盐分高度敏感的棉田空间分布，为精准盐分管理提供了科学理论依据。
