Using QuickBird imagery and a production efficiency model to improve crop yield estimation in the semi-arid hilly Loess Plateau,China |
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| Authors: | Gang Pan Guo-Jun Sun Feng-Min Li |
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| Affiliation: | 1. CAS Key Laboratory of Computational Biology, Partner Institute for Computational Biology, Shanghai Institutes for Biological Sciences, Chinese Academy of Sciences, Shanghai 200031, China;2. Institute of Genetics and Developmental Biology, Chinese Academy of Sciences, Beijing 100101, China;3. Department of Biochemistry, Department of Plant Biology, and Center of Biophysics and Quantitative Biology, University of Illinois at Urbana Champaign, Urbana, IL 61801, USA;1. Faculty of Life and Environmental Science, Shimane Univ., 1060 Nisikawatu-cho, Matsue 690-8504, Japan;2. The UWA Institute of Agriculture, The University of Western Australia, 35 Stirling Highway, Crawley, WA 6009, Australia;3. Graduate School of Agriculture, Kyoto University, Kitashirakawa Oiwake-cho, Sakyo-ku, Kyoto 606-8502, Japan;4. Ministry of Education, Culture, Sports, Science and Technology (MEXT), 3-2-2 Kasumigaseki, Chiyoda-ku, Tokyo 100-8959, Japan;5. Faculty of Agriculture, Çukurova Unviersity, 01330, Balcali, Adana, Turkey;1. Geospatial Sciences Center of Excellence (GSCE), Department of Geography, South Dakota State University, 1021 Medary Ave., Wecota Hall 506B, Brookings, SD 57007-3510, USA;2. Universities Space Research Association, Columbia, MD 21044, USA;3. Biospheric Sciences Laboratory, National Aeronautics and Space Administration/Goddard Space Flight Center, Greenbelt, MD 20771, USA;1. State Key Laboratory of Earth Surface Processes and Resources Ecology, Key Laboratory of Environmental Change and Natural Disaster MOE, Faculty of Geographical Science, Beijing Normal University, Beijing, China;2. Key Laboratory of Land Surface Pattern and Simulation, Institute of Geographical Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing, 100101, China;3. College of Resources and Environment, University of Chinese Academy of Sciences, Beijing, 100049, China;1. Chalmers University of Technology, Gothenburg, Sweden;2. Smart Lighting Engineering Research Center, Rensselaer Polytechnic Institute, Troy, NY, USA;3. Heliospectra AB, Gothenburg, Sweden |
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| Abstract: | Crop yield is a key element in rural development and an indicator of national food security. A method that could estimate crop yield over large hilly areas would be highly desirable. Methods including high spatial resolution satellite imagery have the potential to achieve this objective. This paper describes a method of integrating QuickBird imagery with a production efficiency model (PEM) to estimate crop yield in Zhonglianchuan, a hilly area on Loess Plateau, China. In the PEM model, crop yield is a function of the photosynthetic active radiation (PAR), fraction of absorbed photosynthetically active radiation (fAPAR) and light-use efficiency (LUE). Based on the high spatial resolution QuickBird imagery, a land cover classification is used to attribute a class-specific LUE. The fAPAR is related to spectral vegetation indices (SVI), which can be derived from the satellite images. The LUE, fAPAR and incident PAR data were combined to estimate the crop yield. Farmer-reported crop yield data in 80 representative plots were used to validate the model output. The results indicated QuickBird imagery can improve the accuracy of predicted results relative to the Landsat TM image. The predicted yield approximated well with the data reported by the farmers (r2 = 0.86; n = 80). The spatial distributions of crop yield derived here also offers valuable information to manage agricultural production and understand ecosystem functioning. |
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