Estimation Method of Soil Salinity Based on Remote Sensing Data Assimilation

被引：0
|
作者：
Zhang Z. [1 ,2 ]
Huang X. [1 ,2 ]
Chen Q. [1 ,2 ]
Zhang J. [1 ,2 ]
Tai X. [1 ,2 ]
Han J. [1 ,2 ]
机构：
[1] Key Laboratory of Agricultural Soil and Water Engineering in Arid and Semiarid Areas, Ministry of Education, Northwest A&F University, Shaanxi, Yangling
[2] College of Water Resources and Architectural Engineering, Northwest A&F University, Shaanxi, Yangling
来源：
Nongye Jixie Xuebao/Transactions of the Chinese Society for Agricultural Machinery | 2022年 / 53卷 / 07期
关键词：
data assimilation; ensemble Kalman filter; HYDRUS-ID model; remote sensing; soil salinity;
D O I：
10.6041/j.issn.1000-1298.2022.07.020
中图分类号：
学科分类号：
摘要：
Soil salinization seriously restricts sustainable agricultural development, and it is a main environmental problem in arid and semiarid regions. Therefore, the method of assimilating remote sensing data is used to monitor spatial and temporal information of soil salinity in a regional scale, which is of great significance to management of soil salinization. The feasibility of soil salinity estimation to assimilate HYDRUS-ID model and remote sensing data was explored by using ensemble KaLman filter. The study area was located in Shahaoqu Irrigation District of Hetao Irrigation District. The remote sensing data was obtained by GF-1 satellite. Spectral indexes were screened by gray correlation method, and inversion models of soil salinity at different depths were constructed by ridge regression models. Then remote sensing data was applied to HYDRUS-ID model by using ensemble Kalman filter to carry out assimilation study of soil salinity of different depths in a regional scale. The main conclusions were as follows:based on ridge regression models of soil salinity at different depths, R2 were above 0. 64 and RE were 0. 14 〜0. 22. Inversion accuracies were relatively good and inversion values were relatively accurate. In a single point scale, compared with inversion values and simulation values, assimilation values were closer to measured values. EFF of assimilation values were 0. 84〜0. 93 and their NER were 0. 61〜0. 73. They were all positive values. And their RMSE were reduced to 0. 006% 〜0.011%. These results showed the scheme of data assimilation improved simulation accuracies of HYDRUS-ID model. In a regional scale, r of assimilation values were above 0. 94 and their NER were above 0. 61. And they were better than r and NER of inversion values and simulation values. Meanwhile, with increase of depth, the accuracy of assimilation was decreased. The results indicated that data assimilation greatly improved simulation accuracies of soil salinity at different depths by using ensemble K aim an filter. The research result can provide certain reference value for improving monitoring accuracy of soil salinity in a regional scale. © 2022 Chinese Society of Agricultural Machinery. All rights reserved.
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页码：197 / 207
页数：10
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