Abstract
NDVI (Normalized Difference of Vegetation Index) was employed to estimate the carbon budget remotely with satellite data. However, NDVI has some difficulties in application to agricultural crops, boreal forest, and tundra ecosystems. We proposed a new vegetation index GR (greenery ratio) to detect the vegetation change remotely, and we applied it to estimate CO2 budget of Japanese rice paddy with MODIS satellite data. GR was ratio of green (G) to the trichromatic visible bands (R+G+B) of MODIS, and an empirical GR-CO2 budget model was developed as functions of MODIS-data and observed micrometeorology and fluxes at the rice paddy of Mase-site. The daily PAR (photosynthetically active radiation) was also estimated by MODIS. The parameterized model provided good performance to estimate daily magnitudes and seasonal trends of GPP (gross primary productivity), however, RE (ecosystem respiration) showed a little under-estimation, especially differed in late growing season. In contradiction to the observed CO2 budget, the estimated budgets were 2% greater of GPP and 5% less of RE and 9% greater of NEE (net ecosystem exchange). The large discrepancy in NEE was owing to the poor estimation of RE after drainage. Further study to improve RE estimation was needed.
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