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基于Sentinel-1/2遥感数据的冬小麦覆盖地表土壤水分协同反演

赵建辉 张蓓 李宁 郭拯危

赵建辉, 张蓓, 李宁, 郭拯危. 基于Sentinel-1/2遥感数据的冬小麦覆盖地表土壤水分协同反演[J]. 电子与信息学报, 2021, 43(3): 692-699. doi: 10.11999/JEIT200416
引用本文: 赵建辉, 张蓓, 李宁, 郭拯危. 基于Sentinel-1/2遥感数据的冬小麦覆盖地表土壤水分协同反演[J]. 电子与信息学报, 2021, 43(3): 692-699. doi: 10.11999/JEIT200416
Jianhui ZHAO, Bei ZHANG, Ning LI, Zhengwei GUO. Cooperative Inversion of Winter Wheat Covered Surface Soil Moisture Based on Sentinel-1/2 Remote Sensing Data[J]. Journal of Electronics and Information Technology, 2021, 43(3): 692-699. doi: 10.11999/JEIT200416
Citation: Jianhui ZHAO, Bei ZHANG, Ning LI, Zhengwei GUO. Cooperative Inversion of Winter Wheat Covered Surface Soil Moisture Based on Sentinel-1/2 Remote Sensing Data[J]. Journal of Electronics and Information Technology, 2021, 43(3): 692-699. doi: 10.11999/JEIT200416

基于Sentinel-1/2遥感数据的冬小麦覆盖地表土壤水分协同反演

doi: 10.11999/JEIT200416
基金项目: 国家自然科学基金(61871175),河南省科技攻关计划项目(182102210233, 192102210082),河南省青年人才托举工程(2019HYTP006),河南省高等学校重点科研项目(19A420005)
详细信息
    作者简介:

    赵建辉:男,1980年生,副教授,研究方向为SAR图像处理

    张蓓:女,1994年生,硕士生,研究方向为SAR图像处理

    李宁:男,1987年生,教授,研究方向为多模式合成孔径雷达成像及其应用研究

    郭拯危:女,1963年生,教授,研究方向为SAR图像处理

    通讯作者:

    郭拯危 gzw@henu.edu.cn

  • 中图分类号: TN958

Cooperative Inversion of Winter Wheat Covered Surface Soil Moisture Based on Sentinel-1/2 Remote Sensing Data

Funds: The National Natural Science Foundation of China (61871175), The Plan of Science and Technology of Henan Province (182102210233, 192102210082), The Youth Talent Lifting Project of Henan Province (2019HYTP006), The College Key Research Project of Henan Province (19A420005)
  • 摘要: 冬小麦是我国重要粮食作物之一,对冬小麦覆盖地表土壤水分进行监测有助于解决因土壤供水导致的冬小麦歉收和农业用水浪费等问题。为了降低冬小麦覆盖地表土壤水分微波遥感反演过程中冬小麦对雷达后向散射系数的影响,该文基于Sentinel-1携带的合成孔径雷达(SAR)数据和Sentinel-2携带的多光谱成像仪(MSI)数据,结合水云模型,开展冬小麦覆盖地表土壤水分协同反演研究。首先,基于MSI数据,该文定义了一种新的植被指数,即融合植被指数(FVI),用于冬小麦含水量反演;然后,该文发展了一种基于主被动遥感数据的冬小麦覆盖地表土壤水分反演半经验模型,校正冬小麦在土壤水分反演过程中对雷达后向散射系数的影响;最后,以河南省某地冬小麦农田为研究区域,开展归一化水体指数(NDWI)和FVI两种指数与VV, VH, VV/VH 3种极化组合而成的6种反演方式下的土壤水分反演对比实验。结果表明:以FVI为植被指数,能够更好地去除冬小麦在土壤水分反演过程中对雷达后向散射系数的影响;6种反演方式中,FVI与VV/VH组合下的反演效果最优,其决定系数为0.7642,均方根误差为0.0209 cm3/cm3,平均绝对误差为0.0174 cm3/cm3,展示了该文所提土壤水分反演模型的研究价值和应用潜力。
  • 图  1  研究区与土壤水分采样点分布

    图  2  土壤水分反演流程

    图  3  研究区土壤水分反演值与采样点实测值的空间分布与频率分布(2019年12月29日)

    表  1  基于水云模型和本文所发展模型的土壤水分反演精度对比结果

    反演模型反演组合方式R2RMSE
    水云模型VV-NDWI0.69150.0245
    VV-FVI0.72120.0243
    本文所发展模型VV/VH-NDWI0.72660.0240
    VV/VH-FVI0.76420.0209
    下载: 导出CSV

    表  2  本文所发展模型的6种组合反演方式下土壤水分反演精度对比结果

    反演组合方式R2RMSEMAE
    VH-NDWI0.47270.03260.0263
    VV-NDWI0.67330.02530.0202
    VV/VH-NDWI0.72660.02400.0202
    VH-FVI0.51510.02890.0246
    VV-FVI0.67910.02490.0219
    VV/VH-FVI0.76420.02090.0174
    下载: 导出CSV
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出版历程
  • 收稿日期:  2020-05-29
  • 修回日期:  2020-12-06
  • 网络出版日期:  2020-12-18
  • 刊出日期:  2021-03-22

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