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The North China Plain (NCP) was selected as the study area and the effects of water and heat were analysed to determine the dominant factor affecting winter wheat growth. The mean, minimum and maximum temperatures, precipitation and soil moisture data were selected to analyse the correlations between the leaf area index (the growth indicator) and these factors using long time series half-monthly data (2–5 months) (from 1982 to 2010). The results showed that temperature was the main factor affecting the growth of winter wheat in the NCP. The growth of winter wheat had weak correlations with precipitation and soil moisture and the influence of water on winter wheat growth was smaller than the influence of heat. In the northern part of the NCP, mainly including the north-west region of Shandong Province and the southern region of Hebei Province, irrigation was necessary in late February and early March. [ABSTRACT FROM AUTHOR]

Crop classification is a key issue for agricultural monitoring using remote-sensing techniques. Synthetic aperture radar (SAR) data are attractive for crop classification because of their all-weather, all-day imaging capability. The objective of this study is to investigate the capability of SAR data for crop classification in the North China Plain. Multi-temporal Envisat advanced synthetic aperture radar (ASAR) and TerraSAR data were acquired. A support vector machine (SVM) classifier was selected for the classification using different combinations of these SAR data and texture features. The results indicated that multi-configuration SAR data achieved satisfactory classification accuracy (best overall accuracy of 91.83%) in the North China Plain. ASAR performed slightly better than TerraSAR data acquired in the same time span for crop classification, while the combination of two frequencies of SAR data (C- and X-band) was better than the multi-temporal C-band data. Two temporal ASAR data acquired in late jointing and flowering periods achieved sufficient classification accuracy, and adding data to the early jointing period had little effect on improving classification accuracy. In addition, texture features of SAR data were also useful for improving classification accuracy. SAR data have considerable potential for agricultural monitoring and can become a suitable complementary data source to optical data. [ABSTRACT FROM AUTHOR]