NDVI prediction over Mongolian grassland using GSMaP precipitation data and JRA-25/JCDAS temperature data |
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| Authors: | H Iwasaki |
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| Institution: | 1. State Key Laboratory of Environmental Criteria and Risk Assessment, Chinese Research Academy of Environmental Sciences, Beijing 100012, China;2. State Key Laboratory of Environmental Protection for Regional Eco-Process and Function Assessment, Chinese Research Academy of Environmental Sciences, Beijing 100012, China;3. The State Key Laboratory of Resources and Environmental Information System, Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing 100101, China;1. Department of Evolutionary Biology and Environmental Studies, University of Zurich, Winterthurerstrasse 190, 8057 Zurich, Switzerland;2. Swiss Federal Research Institute WSL, Zürcherstrasse 111, 8903 Birmensdorf, Switzerland;3. Bern University of Applied Sciences, School of Agricultural, Forest and Food Sciences HAFL, Länggasse 85, 3052 Zollikofen, Switzerland;1. College of Resource Environment and Tourism, Capital Normal University, Beijing, PR China;2. Key Laboratory of 3D Information Acquisition and Application of Ministry of Education, Beijing, PR China;3. Key Laboratory of Resources Environment and GIS of Beijing Municipal, Beijing, PR China;4. Base of the State Laboratory of Urban Environmental Processes and Digital Modeling, Beijing 100048, PR China;5. School of Geosciences, University of South Florida, Tampa, USA;1. University of Chinese Academy of Sciences, Beijing, China;2. Key Laboratory of Digital Earth Science, Institute of Remote Sensing and Digital Earth (RADI), Chinese Academy of Sciences (CAS), Dengzhuang South Road, Haidian District, Beijing, 100094, China;3. National Remote Sensing Center, Information and Research Institute of Meteorology, Hydrology and Environment (IRIMHE), Ulaanbaatar, 15160, Mongolia;4. National Authority for Remote Sensing & Space Sciences (NARSS), Egypt |
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| Abstract: | An algorithm to predict the NDVI (Normalized Difference Vegetation Index) distribution over Mongolia, which is based on a stepwise multiple linear regression analysis, has been developed using global precipitation data obtained from satellites and global surface air temperature data obtained from the reanalysis data during the period 1998–2005. This algorithm can predict the NDVI value up to 1–3 months in advance for a grid with a spatial resolution of 0.25° × 0.25°.In order to validate the algorithm, the NDVI distribution was predicted for the period from May to November 2006 using 1 to 3-month prediction algorithms. The distributions of the predicted normalized anomalies agreed well with those of the observed normalized anomalies. It was found that these algorithms were effective for arid and semi-arid regions, despite its low accuracy for August and regions with high vegetation activity. |
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