| MODIS数据估算区域蒸散量的空间尺度误差纠正方法研究 |
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| 引用本文： | 辛晓洲,刘雅妮,柳钦火,唐勇.MODIS数据估算区域蒸散量的空间尺度误差纠正方法研究[J].遥感学报,2012,16(2):207-231. |
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| 作者姓名： | 辛晓洲 刘雅妮 柳钦火 唐勇 |
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| 作者单位： | 遥感科学国家重点实验室 中国科学院遥感应用研究所, 北京 100101;遥感科学国家重点实验室 中国科学院遥感应用研究所, 北京 100101;遥感科学国家重点实验室 中国科学院遥感应用研究所, 北京 100101;遥感科学国家重点实验室 中国科学院遥感应用研究所, 北京 100101 |
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| 基金项目： | 国家重点基础研究发展计划(973计划)(编号:2007CB714400);国家自然科学基金项目(编号:40971204,40730525,40601067);中国科学院知识创新工程重要方向性项目(编号:KZCX2-YW-313) |
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| 摘 要： | 探讨了使用中高分辨率卫星数据提供的地表分类以及植被指数信息与中低分辨率卫星数据相结合,在混合像元内部进行亚像元处理,以纠正混合像元造成的通量估算误差的方法。其意义在于利用中低分辨率卫星数据进行长期大面积蒸散监测时,只需要少量的中高分辨率数据支持,就可以在一定程度上改善监测结果,具有很好的可操作性。
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| 关 键 词： | 蒸散 卫星遥感 混合像元 空间尺度误差 亚像元处理 |
| 收稿时间： | 3/3/2010 12:00:00 AM |
| 修稿时间： | 2011/9/15 0:00:00 |
Spatial-scale error correction methods for regional fluxes retrievalusing MODIS data |
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| XIN Xiaozhou,LIU Ya''ni,LIU Qinhuo and TANG Yong.Spatial-scale error correction methods for regional fluxes retrievalusing MODIS data[J].Journal of Remote Sensing,2012,16(2):207-231. |
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| Authors: | XIN Xiaozhou LIU Ya'ni LIU Qinhuo and TANG Yong |
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| Institution: | State Key Laboratory of Remote Sensing Science, Jointly Sponsored by the Institute of Remote Sensing Applications of ChineseAcademy of Sciences and Beijing Normal University, Beijing 100101, China;State Key Laboratory of Remote Sensing Science, Jointly Sponsored by the Institute of Remote Sensing Applications of ChineseAcademy of Sciences and Beijing Normal University, Beijing 100101, China;State Key Laboratory of Remote Sensing Science, Jointly Sponsored by the Institute of Remote Sensing Applications of ChineseAcademy of Sciences and Beijing Normal University, Beijing 100101, China;State Key Laboratory of Remote Sensing Science, Jointly Sponsored by the Institute of Remote Sensing Applications of ChineseAcademy of Sciences and Beijing Normal University, Beijing 100101, China |
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| Abstract: | Large numbers of important researches have been done to estimate regional surface heat fluxes using remote sensingdata over the past few decades. Due to the spatial heterogeneity of the land surface on a regional scale, many problems stillneed to be explored. Clearly, for landscapes with significant variability in vegetation cover, type, architecture, and moisture, dueto the large contrasts in surface temperature, vegetation cover, surface roughness length and zero plane displacement height, theapplication of a land surface model to a mixed pixel causes significant errors. In this paper, we discussed the method of combiningthe land cover information and remotely sensed vegetation index provided by Landsat data and Moderate Resolution ImagingSpectroradiometer (MODIS) data to correct spatial-scale errors. It makes full use of the advantages of the temporal resolutions ofMODIS data and spatial resolutions of Landsat data to construct a regional evapotranspiration model, which meets the requirementsof spatial heterogeneity scale and makes the higher frequency of large area flux monitoring more operational. |
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| Keywords: | evapotranspiration remote sensing mixed pixels spatial-scale error sub-pixel analysis |
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