Multitemporal fusion of Landsat/TM and ENVISAT/MERIS for crop monitoring |
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| Affiliation: | 1. State Key Laboratory of Earth Surface Processes and Resource Ecology, Institute of Remote Sensing Science and Engineering, Faculty of Geographical Science, Beijing Normal University, Beijing 100875, China;2. Center for Environmental Remote Sensing, Chiba University, Chiba 263-8522, Japan;3. Department of Land Surveying and Geo-Informatics, The Hong Kong Polytechnic University, Hong Kong;4. State Key Laboratory of Remote Sensing Science, Institute of Remote Sensing Science and Engineering, Faculty of Geographical Science, Beijing Normal University, Beijing 100875, China;5. Department of Ecosystem Science and Management, Texas A&M University, College Station, TX 77843, USA;6. International Institute of Tropical Forestry, USDA Forest Service, Río Piedras, PR, 00926, USA;1. The State Key Laboratory of Remote Sensing Science, Institute of Remote Sensing and Digital Earth, Chinese Academy of Sciences, P.O.Box 9718, Datun Road, Chaoyang, Beijing 100101, China;2. Key laboratory of Digital Earth Science, Institute of Remote Sensing and Digital Earth, Chinese Academy of Sciences, P.O.Box 9718, Datun Road, Chaoyang, Beijing 100101, China;1. State Key Laboratory of Resources and Environmental Information System, Institute of Geographic Sciences and Natural Resources Research, CAS, Beijing 100101, China;2. College of Resources and Environment, University of Chinese Academy of Sciences, Beijing 100190, China;3. Jiangsu Provincial Key Laboratory of Geographic Information Science and Technology, International Institute for Earth System Science, Nanjing University, Nanjing, Jiangsu 210046, China;4. National Satellite Meteorological Center, Beijing 100081, China;5. Key Laboratory of Space Utilization, Technology and Engineering Center for space Utilization, CAS, Beijing 100094, China;1. Department of Land Surveying and Geo-Informatics, The Hong Kong Polytechnic University, Kowloon, Hong Kong;2. Faculty of Science and Technology, Engineering Building, Lancaster University, Lancaster LA1 4YR, UK;3. Faculty of Geosciences, University of Utrecht, Heidelberglaan 2, 3584 CS Utrecht, The Netherlands;4. School of Geography, Archaeology and Palaeoecology, Queen''s University Belfast, BT7 1NN Northern Ireland, UK;5. School of Remote Sensing and Information Engineering, Wuhan University, Wuhan 430079, China;1. Department of Geography and Resource Management, The Chinese University of Hong Kong, Hong Kong, China;2. Institute of Space and Earth Information Science, The Chinese University of Hong Kong, Hong Kong, China;3. Jiangsu Key Laboratory of Big Data Analysis Technolog, Nanjing University of Information Science and Technology, Nanjing 210044, China |
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| Abstract: | Monitoring Earth dynamics using current and future satellites is one of the most important objectives of the remote sensing community. The exploitation of image time series from sensors with different characteristics provides new opportunities to increase the knowledge about environmental changes and to support many operational applications. This paper presents an image fusion approach based on multiresolution and multisensor regularized spatial unmixing. The approach yields a composite image with the spatial resolution of the high spatial resolution image while retaining the spectral and temporal characteristics of the medium spatial resolution image. The approach is tested using images from Landsat/TM and ENVISAT/MERIS instruments, but is general enough to be applied to other sensor pairs. The potential of the proposed spatial unmixing approach is illustrated in an agricultural monitoring application where Landsat temporal profiles from images acquired over Albacete, Spain, in 2004 and 2009 are complemented with MERIS fused images. The resulting spatial resolution from Landsat allows monitoring small and medium size crops at the required scale while the fine spectral and temporal resolution from MERIS allow a more accurate determination of the crop type and phenology as well as capturing rapidly varying land-cover changes. |
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| Keywords: | Image fusion Regularized spatial unmixing Point-spread function Multi-temporal NDVI Crop monitoring |
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