Maize acreage estimation using ENVISAT MERIS and CBERS-02B CCD data in the North China Plain

Abstract: Crop acreage estimation is a key aspect to forecast crop production. Maize acreage estimation becomes more and more important because the fast production changes every year due to the dynamics of the prices. This paper focuses on maize acreage estimation in the North China Plain using ENVISAT MERIS and CBERS-02B CCD data of 2008. Firstly, adaptive maximum likelihood classification of CBERS-02B CCD images based on ground survey provided reliable maize area fraction image (AFI). CBERS derived AFIs (as reference AFI) were used to train a 3-layer back-propagation neural network, this was then used to the whole MERIS data to generate MERIS AFIs (AFIe). To estimate maize acreage, the maize AFI from MERIS was masked with cropland dataset and maize acreages were estimated by zonal statistic of maize AFI at district level. The statistical results were also modified using a non-arable coefficient to remove the effects of non-arable factors. The results showed a close relationship between estimated and statistical maize acreage (R 2 ≈0.88). At province level, the estimation error is approximately 8%. This method is valuable for wide-scale, regional crop acreage estimation at the early stage of growing season. The study gives suggestions about high resolution image acquisition, spatial distribution and cropland datasets. [Copyright &y& Elsevier]