期刊
2014 IEEE INTERNATIONAL GEOSCIENCE AND REMOTE SENSING SYMPOSIUM (IGARSS)
卷 -, 期 -, 页码 5060-5063出版社
IEEE
DOI: 10.1109/IGARSS.2014.6947634
关键词
Grassland; MODIS time series; ANN; ANFIS; biomass prediction
More than 80% of agricultural land in Ireland is grassland, providing a major feed source for the pasture based dairy farming and livestock industry. Intensive grass based systems demand high levels of intervention by the farmer, with estimation of pasture cover (biomass) being the most important variable in land use management decisions, as well as playing a vital role in paddock and herd management. Many studies have been undertaken to estimate grassland biomass using satellite remote sensing data, but rarely in systems like Irelands intensively managed, small scale pastures, where grass is grazed as well as harvested for winter fodder. The objective of this study is to estimate grassland yield (kgDM/ha) from MODIS derived vegetation indices on a near weekly basis across the entire 300+ day growing season using three different methods (Multiple Linear Regression (MLR), Artificial Neural Networks (ANN) and Adaptive Neuro-Fuzzy Inference Systems (ANFIS)). The results show that ANFIS model produced best result (R-2 = 0.86) as compare to the ANN (R-2 = 0.57) and MLR (R-2 = 0.31).
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