Monitoring crop yield using NOAA/AVHRR-based vegetation indices

Satellite remote sensing is the most effective means to monitor crop production on a regional scale. When figuring the vegetation growth condition and distribution on a large scale, vegetation indices derived from NOAA polar orbiting satellite work better than meteorological data derived from weather station. In this paper, vegetation indices including VCI (Vegetation Condition Index), TCI (Temperature Condition Index) and VHI (Vegetation Health Index) are extracted from 16 km seven-day composite NOAA AVHRR/NDVI time series images. Then, the values of VCI, TCI and VHI distribution on arable land were calculated. Based on the calculated vegetation indices and the crop yield statistical data, the linear regression models and the non-linear regression models were established, respectively, to express the relationships between the vegetation indices and crop yield. The major conclusions in this study are (1) the vegetation indices and crop yield have good correlation in a certain week of the crop growth season, (2) the fitting accuracies of the non-linear regression models are much higher than those of the linear regression models, namely, the results obtained from the non-linear regression models are more accordant with the agricultural statistic data in comparison with those from the linear regression models. Those methods can be used in the operational system of crop yield estimation on a national scale.