| Sentinel-2多光谱卫星遥感反演植被覆盖下的土壤盐分变化 |
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| 引用本文： | 杜瑞麒,陈俊英,张智韬,徐洋洋,张兴,殷皓原,杨宁.Sentinel-2多光谱卫星遥感反演植被覆盖下的土壤盐分变化[J].农业工程学报,2021,37(17):107-115. |
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| 作者姓名： | 杜瑞麒 陈俊英 张智韬 徐洋洋 张兴 殷皓原 杨宁 |
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| 作者单位： | 1. 西北农林科技大学水利与建筑工程学院，杨凌 712100; 2. 西北农林科技大学旱区农业水土工程教育部重点实验室，杨凌 712100; |
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| 基金项目： | 国家重点研发计划项目（2017YFC0403302，2016YFD0200700）；杨凌示范区科技计划项目（2018GY-03） |
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| 摘 要： | 为克服植被覆盖条件下土壤盐分含量与光谱反射率之间相关性较差所带来反演精度较低的问题，该研究以内蒙古河套灌区沙壕渠灌域为研究区域，利用Sentinel-2卫星同步获取光谱数据，通过构建以归一化植被指数（Normalized Difference Vegetation Index，NDVI）为分支标准的盐分深度决策树确定反演土壤盐分含量的最佳深度，然后构建以NDVI和表层土壤含水率为分支标准的类别决策树，将土壤样本划分为不同类别，以此分别构建土壤盐分反演模型，并评估反演效果。研究结果表明，决策树能增强光谱反射率对土壤盐分含量的敏感性，光谱反射率与土壤盐分含量的相关系数达0.66以上。基于随机森林（Random Forest，RF）的盐分反演模型可取得理想的反演效果，决定系数为0.77，均方根误差为0.27%，相对分布误差为2.65，相对分析误差为8.99。土壤盐分含量反演模型能较好地反演表层（<20 cm）和深层（>40～60 cm）土壤盐分含量，在反演中层（20～40 cm）土壤盐分含量上存在一定局限。当地表有植被覆盖时，利用决策树可有效地提高土壤盐分含量的反演精度（与未考虑决策树相比，决定系数和相对分布误差分别提高0.34和0.67）。研究结果可为监测灌区内作物生育期间土壤盐分含量的动态变化提供方法参考。
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| 关 键 词： | 土壤 盐分 遥感 Sentinel-2卫星 土壤水分 河套灌区 反演 |
| 收稿时间： | 2020/8/17 0:00:00 |
| 修稿时间： | 2021/7/7 0:00:00 |
Inversing soil salinity under vegetation cover using Sentinel-2 multispectral satellite remote sensing |
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| Du Ruiqi,Chen Junying,Zhang Zhitao,Xu Yangyang,Zhang Xing,Yin Haoyuan,Yang Ning.Inversing soil salinity under vegetation cover using Sentinel-2 multispectral satellite remote sensing[J].Transactions of the Chinese Society of Agricultural Engineering,2021,37(17):107-115. |
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| Authors: | Du Ruiqi Chen Junying Zhang Zhitao Xu Yangyang Zhang Xing Yin Haoyuan Yang Ning |
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| Institution: | 1. College of Water Resources and Architectural Engineering, Northwest A&F University, Yangling 712100, China; 2. Key Laboratory of Agricultural Soil and Water Engineering in Arid and Semiarid Areas, Ministry of Education, Northwest A&F University, Yangling 712100, China; |
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| Abstract: | Dynamic change of Soil Salinity Content (SSC) is widely used to control soil salinization for high production efficiency in modern agriculture. This study aimed to enhance the correlation between SSC and spectral reflectance under vegetation coverage using Sentinel-2 multispectral remote sensing. Soil samples of salinity and moisture were collected at 100 sampling sites with different depth ranges, including <20, 20-40, and >40-60 cm, in Shahaoqu Irrigation Area, Inner Mongolia, China, from June to August in 2019. The multispectral data of Sentinel-2 satellite was acquired synchronously according to the sample time and location. The specific procedure was as follows. Firstly, a depth decision tree was constructed with the normalized difference vegetation index as a branching criterion, where the best one was then selected to determine the optimal depth range for the SSC retrieval of soil samples. Secondly, a classification decision tree was used to divide the soil samples into different categories, according to the normalized vegetation index and Soil Moisture Content (SMC). As such, the category of each soil sample was determined using the classification decision tree. Thirdly, the optimal spectral combination for each category was calculated to serve as an input variable into the SSC inversion model. Several machine learning models were adopted for the SSC inversion models to monitor the SSC at the optimal depth range from the salinity depth decision tree, including Adaptive boosting algorithm (Adaboost), Partial Least Squares Regression (PLSR), Support Vector Machines (SVM), Gradient Boosting Decision Tree (GBDT), and Random Forest (RF). The results showed that the correlation coefficient between SSC and spectral reflectance was above 0.66, considering the decision tree. In terms of soil depth range, the optimal inversion depth for the SSC under vegetation cover was >40-60 cm, followed by <20 cm, but the SSC inversion model presented some limitations in the middle layer (20-40 cm). Furthermore, the inversion accuracy was ranked in the descending order of RF, GBDT, Adaboost, PLSR, and SVM, where the RF, GBDT, and Adaboost presented similar inversion. Correspondingly, the SSC inversion model using ensemble learning demonstrated a strong generalization ability to achieve the ideal and stable inversion under different application scenarios, compared with the other machine learning. Specifically, the SSC inversion model performed the best using RF, where the coefficient of determination, the root mean square error, the residual predictive interquartile range, and the residual predictive deviations were 0.77, 0.27%, 2.65, and 8.99, respectively. The correlation between SSC and spectral reflectance was 0.38 without considering the decision tree, indicating there was no significance in the SSC inversion model. Considering the decision tree, the correlation coefficient between soil salinity and spectral reflectance was above 0.66, and the coefficient of determination of the SSC inversion model was 0.72, indicating that the decision tree effectively enhanced the sensitivity of spectral reflectance to SSC for the high accuracy, particularly when the vegetation on the surface of the soil. Consequently, this finding can provide a promising potential way to monitor the soil salinization in the irrigated areas during crop growth using multi-spectral satellites in modern agriculture. |
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| Keywords: | soils salinity remote sensing Sentinel-2 satellite soil moisture Hetao Irrigation District inversion |
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