Evaluation of arable land yield potential through remote sensing monitoring

Recently, raising yield per unit of available arable land becomes necessary to combat the challenge due to increasing population with decreasing arable land. Therefore, it is imperative to evaluate the yield potential for the cropland. The objective of this study was to evaluate the winter wheat yield potential for cropland of Beijing suburb. Three years´ continuous remote sensing yield monitoring experiments were carried out from 2007 to 2009. Multitemporal remote sensing images of Landsat5 TM and BJ-1 were collected at the winter wheat growth season. The winter wheat yield monitoring models were established based on the remote sensing vegetation index and the actual yield data for each year. Then, relationship between three years winter wheat monitoring yield and habitat factors, including climatic factors, soil factors and terrain factors, were analyzed. Principal Component Analysis (PCA) and Multi-linear regression (MLR) analysis method are combined to construct the wheat yield potential assessment model, to comprehensively evaluate the wheat-growing areas and classify medium- and low-yield fields in Beijing suburb. The information from this study allow us to systematically understand the wheat medium- and low-yield fields of Beijing area and their spatial distribution features, identify key potential barrier factors, and establish reference for medium- and low-yield farmland in transformation, crop distribution management and rational fertilization in Beijing area.