Daily and ten-day Normalized Difference Vegetation Index( NDVI) of crops were retrieved from meteorological satellite NOAA AVHRR images. The temporal variations of the NDVI were analyzed during the whole growing seaso...Daily and ten-day Normalized Difference Vegetation Index( NDVI) of crops were retrieved from meteorological satellite NOAA AVHRR images. The temporal variations of the NDVI were analyzed during the whole growing season, and thus the principle of the interaction between NDVI profile and the growing status of crops was discussed. As a case in point, the relationship between integral NDVI and winter wheat yield of Henan Province in 1999 had been analyzed. By putting integral NDVI values of 60 sample counties into the winter wheat yield-integral NDVI coordination, scattering map was plotted. It demonstrated that integral NDVI had a close relation with winter wheat yield. These relation could be described with linear, cubic polynomial, and exponential regression, and the cubic polynomial regression was the best way. In general, NDVI reflects growing status of green vegetation, so crop monitoring and crop yield estimation could be realized by using remote sensing technique on the basis of time serial NDVI data together with agriculture calendars.展开更多
基金Under the auspices of Beijing Precision Agriculture Project of the State Development Planning Commission(A00300100584-RS02).
文摘Daily and ten-day Normalized Difference Vegetation Index( NDVI) of crops were retrieved from meteorological satellite NOAA AVHRR images. The temporal variations of the NDVI were analyzed during the whole growing season, and thus the principle of the interaction between NDVI profile and the growing status of crops was discussed. As a case in point, the relationship between integral NDVI and winter wheat yield of Henan Province in 1999 had been analyzed. By putting integral NDVI values of 60 sample counties into the winter wheat yield-integral NDVI coordination, scattering map was plotted. It demonstrated that integral NDVI had a close relation with winter wheat yield. These relation could be described with linear, cubic polynomial, and exponential regression, and the cubic polynomial regression was the best way. In general, NDVI reflects growing status of green vegetation, so crop monitoring and crop yield estimation could be realized by using remote sensing technique on the basis of time serial NDVI data together with agriculture calendars.
