| 基于热红外的四种土壤含水量估算方法对比研究 |
| Received:July 27, 2017 Revised:January 16, 2018 点此下载全文 |
| 引用本文：杨永民,邱建秀,苏红波,田静,张仁华.基于热红外的四种土壤含水量估算方法对比研究[J].Journal of Infrared and Millimeter Waves,2018,37(4):459~467 |
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| 基金项目:中国水科院陆地水循环及地表过程重点实验室开放基金(项目号：2015A006)、国家自然科学基金青年基金(项目号：41501415、41501450)、中国水利水电科学研究院科研专项(项目号：JZ0145B032017)、广东省自然科学基金(项目号：2016A030310154) 和中央高校基本科研业务费专项资金(项目号：16lgpy06)联合资助 |
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| 中文摘要:基于遥感的区域土壤水分反演是流域水资源规划管理、农作物灌溉制度制定、区域旱情监测、农作物估产等方面的基础。本文对四种可见光/热红外土壤水分反演方法进行评估对比，四种土壤水分估算方法包括基于TVDI的土壤水分估算方法和三种基于蒸散比/潜在蒸散比的土壤水分估算方法(EFM1, EFM2和EFM3)。基于四种土壤水分估算方法，本文使用ASTER数据估算了黑河流域中游地区的土壤水分状况，使用研究区生态水文无线传感器网络和流域水文气象观测站点的土壤水分观测数据对四个模型方法进行了验证评估。结果表明，TVDI方法因其干、湿边确定的经验性，会导致土壤水分估算的误差。而基于蒸散比/潜在蒸散比的土壤水分估算方法会在一定程度改善TVDI方法估算的经验性。对蒸散比/潜在蒸散比的三种方法对比显示基于EFM1和EFM3方法优于EFM2方法。此外，基于热红外的土壤水分估算方法都需要土壤参数信息，土壤参数的不确定性会导致土壤水分估算的误差，高精度的土壤参数会改善基于热红外的土壤水分估算方法的精度。 |
| 中文关键词:热红外 土壤水分 遥感 |
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| Estimation of surface soil moisture based on thermal remote sensing: Intercomparison of four methods |
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| Abstract:Remote sensing-based estimation of soil moisture is crucial in many aspects including basin-scale water resource management, irrigation scheduling, regional scale drought monitoring and crop yield forecasting. In this study, we evaluate the potential of visible/thermal-infrared remote sensing in soil moisture estimation, by assessing the TVDI-based method and three categories methods based on evaporation fraction/potential evaporation ratio (EFM1, EFM2 and EFM3). In combination with ASTER data set, the soil moisture in middle reach of the Heihe River Basin are predicted by the above-mentioned four methods and validated by the ground-based measurements from eco-hydrological wireless sensor network and hydro meteorological observation network in the middle reach of Heihe river basin. Results indicate that uncertainties arise from the empiricism of the TVDI-based method in the process of determining dry and wet edges. On the other hand, the evaporation fraction/potential evaporation ratio methods can to some degree reduce the uncertainties, and among the three methods, EFM1 and EFM3 outperform EFM2. In addition, the thermal-infrared based methods require accurate soil parameters to reproduce the variation of soil moisture. |
| keywords:thermal remote sensing soil moisture remote sensing |
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