Research On The Productivity Of Farm Land Based On Remote Sensing

Land is the basis for human survival, the arable land produces food for human survival and development. In order to grasp the potential of natural land vegetation production capacity and output of agricultural land, the predecessors have done a lot of useful researches on that. On the basis of previous research, research methods of the net primary productivity of vegetation and conclusions of research on production capacity of the arable land are both used to explore a new way to calculate the production capacity of the arable land by remote sensing methods. This research are supported by the following conclusions:According to the features of the production capacity of the arable land and the nature of the primary net productivity of vegetation which is the weight of plant without water accumulated in a certain space within a certain period of time, the weight of corn without water which comes from photosynthesis is calculated from the net primary productivity by the corn harvest index, and the other part of dry weight of the corn is mineral, whose content of dry plant is10%, then true weight of corn harvested can be get from adding moisture and mineral content of corn, after that, standard crop coefficient is used to convert the true weight of corn harvested into yield of standard crop in order to compare with production capacity of the arable land. According to previous studies and farmers’ actual experience, the corn harvest index is0.5, the moisture content is40%.Based on remote sensing and CASA model, total productivity of crop could reflect production capacity of the arable land, but the data does not completely agree.The model of CASA get crop yield without including the effects of human activities on crop yield increase, and throughput capacity includes policy, investment, human activities. Crop yield various levels of percentage based on remote sensing and CASA model can accurately reflect the production capacity of the arable land by villages and towns, and the natural quality index and the land utilization index of various levels of capacity percentage.Orrelation analysis showed that there is statistically significant between the total output of crops based on remote sensing and CASA model and the production capacity of the arable land research in these three levels which are villages and towns, the natural quality index and the land utilization index. The reflection degree of accuracy of the percentage of yield in Baoxing County from high to low are production by natural quality, production by land use, potential capacity, theoretical capacity by towns, potential capacity by towns and the actual capacity by towns.There is on statistically significant between the yield data in each level obtained in this study and theoretical capacity, potential capacity and the actual capacity by towns, theoretical capacity by natural quality and potential capacity by land use.