不同尺度的土壤含水量主被动微波联合反演方法研究

刘奇鑫; 顾行发; 王春梅; 杨健; 占玉林

doi:10.13745/j.esf.sf.2023.12.32

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地学前缘 ›› 2024, Vol. 31 ›› Issue (2) : 42-53. DOI: 10.13745/j.esf.sf.2023.12.32

场地土壤污染机制与风险管控

不同尺度的土壤含水量主被动微波联合反演方法研究

刘奇鑫 ¹^,² ,
顾行发 ¹^,²^,³ ,
王春梅 ¹ ,
杨健 ¹ ,
占玉林 ¹^,^*

作者信息 +

Soil moisture retrieval on both active and passive microwave data scales

Qixin LIU ¹^,² ,
Xingfa GU ¹^,²^,³ ,
Chunmei WANG ¹ ,
Jian YANG ¹ ,
Yulin ZHAN ¹^,^*

Author information +

History +

摘要

土壤含水量是水文、农业和气象等领域的关键参数,而微波遥感是目前监测土壤含水量最有效的手段之一。本文利用主动微波与被动微波数据,结合其他多源遥感数据,运用随机森林算法分别在主动微波数据分辨率尺度和被动微波数据分辨率尺度下完成主被动微波数据的土壤含水量联合反演。首先对被动微波尺度的地表覆盖类型与归一化植被指数(NDVI)参数进行空间分辨率优化,再利用回归ReliefF方法对两种尺度所用的输入变量的重要性进行评估,并对输入变量进行优选,最后对比主被动微波数据土壤含水量联合反演和单独利用主动/被动微波数据进行反演的精度,分析主被动微波联合反演方法的有效性。结果表明:在主动微波尺度,主被动微波联合反演的精度相比单独利用主动微波数据反演的精度有所提升,相关系数r由0.691升至0.744,RMSE由0.084 8 cm³/cm³降至0.079 6 cm³/cm³;在被动微波尺度,主被动微波联合反演的精度反而比单独利用被动微波数据反演的精度更低,相关系数r由0.944变为0.939,RMSE由0.043 5 cm³/cm³变为0.045 1 cm³/cm³。因此在主动微波尺度更适合进行主被动微波的联合反演。

Abstract

Soil moisture is a critical parameter in the field of hydrology, agriculture and meteorology, and microwave remote sensing is one of the most effective methods for soil moisture detection. This study uses active and passive microwave data and other multi-source remote sensing data and applies Random forest algorithm to perform soil moisture retrieval on both active and passive microwave data scales. First, on passive microwave data scale, the parameters for land cover and normalized difference vegetation index (NDVI) are spatially optimized. Second, ReliefF method is used for evaluating the importance of input parameters and parameter selection. Last, results by using active and passive microwave data jointly or separately are compared to assess the retrial accuracy and effectiveness of the former method. It was found that on the scale of active microwave data, the joint retrieval method yielded results with higher accuracy (r=0.691, RMSE=0.0796) compared to using only active microwave data (r=0.744, RMSE=0.0848); however, on the scale of passive microwave data, the opposite was true (r=0.939, RMSE=0.0451 using joint data; r=0.944, RMSE=0.0435 using only passive microwave data). The joint retrieval method proved to be applicable on the scale of active microwave data.

关键词

土壤含水量 / 微波遥感 / 联合反演 / 随机森林 / 空间优化 / 重要性评估

Key words

soil moisture / microwave remote sensing / joint retrieval / random forest / spatial optimization / importance evaluation

中图分类号

P642.115;P627;X87

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导出引用

刘奇鑫 , 顾行发 , 王春梅 , 等. 不同尺度的土壤含水量主被动微波联合反演方法研究. 地学前缘. 2024, 31(2): 42-53 https://doi.org/10.13745/j.esf.sf.2023.12.32

Qixin LIU, Xingfa GU, Chunmei WANG, et al. Soil moisture retrieval on both active and passive microwave data scales[J]. Earth Science Frontiers. 2024, 31(2): 42-53 https://doi.org/10.13745/j.esf.sf.2023.12.32

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脚注

基金

国家重点研发计划项目(2021YFE0117300)

国家民用空间基础设施陆地观测卫星共性应用支撑平台项目(2017-000052-73-01-001735)

国家国防科技工业局高分辨率对地观测系统重大专项(30-Y60B01-9003-22/23)

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Received	Revised	Published
2023-12-13	2024-01-02	2024-03-25
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2025-03-24

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