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18 September 2009 Assessment of nitrate leaching on agriculture region using remote sensing and model
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Overuse of chemical fertilizers raises the risk of nitrate pollution of groundwater in the North China Plain. To preserve the groundwater and reduce the economic losses, an efficiently and quickly assessment of nitrate leaching risk on regional farmland is crucial. In this research we developed a GIS-based model named 'Arc-NLEAP' based on NLEAP model, combined the statistical and Remote Sensing data, to estimate applied fertilizer rates and crop yields, which are two key variables indicating amount of input and output nitrogen in crop land, since crop greenness derived by MODIS may reflect the content of chlorophyll of canopy which is closely related to nitrogen content, and NDVI values of crop crucial growing periods determine crop production. The simulated results showed that the value for parameter NAL (Nitrate Available for Leaching) was between 8 kg / ha and 474 kg / ha and the average was 117 kg / ha, for NL (amount of Nitrate Leached) 18kg / ha (Low) , 59 kg / ha (Average) and 222 kg / ha(High).Percentages of parameter MRI(Movement Risk Index) accounted for 8%,77% and 15% for low risk, medium risk and high risk respectively. Taking water leaching index, nitrogen available for leaching, amount of Nitrate Leached, ammonia volatilization and denitrification into consideration, we defined the N hazard class to evaluate the nitrogen leaching risk and the result indicated that lager 74% of the study area was labeled as low N hazard class. Despite the spatial patterns for parameters NAL and NL were similar, the values for MRI was determined by site-specific soil type and the capacity of water movement principally, demonstrating that measures of controlling nitrate leaching should be based on the spatial pattern of MRI, along with decreasing the amount of application rate simultaneity.
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Yuping Lei, Zhen Wang, Hongjun Li, Li Zheng, and Shengwei Zhang "Assessment of nitrate leaching on agriculture region using remote sensing and model", Proc. SPIE 7472, Remote Sensing for Agriculture, Ecosystems, and Hydrology XI, 74722A (18 September 2009);

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