基于遥感和作物生长模型的作物产量差估测

传统的作物生长模型很难模拟大田的实际产量，因为大量的数据、复杂的数学运算以及误差传递限制了作物生长模拟模型的运用。目前为止实际产量仅能通过观测和实地调查获得。该文将NOAA-14 AVHRR遥感获取的冠层温度信息引入作物生长模型，利用冠气温差计算作物水分胁迫系数，可以近似地估计区域作物实际生长速率和产量，进而建立了遥感-作物模拟复合模型PS-X，提出了估算区域作物实际产量的方法。PS-X模型可在不同层次模拟作物的生长和产量，在PS-1、PS-2、PS-X水平计算的分别是作物的光温生产潜力、水分限制下的生产力和实际产量。利用该模型，论文分别模拟了邯郸地区1998年夏玉米的光温生产潜力、水分限制下的生产力和实际产量，并通过比较不同模拟水平下产量和农户调查产量进行区域产量差分析。结果表明：PS-1和PS-2水平之间的产量差主要由水分和土壤质地差异造成；PS-2与PS-X水平间的平均产量差异较大，占总产量差（PS-1与PS-X水平之差）的81.4%，主要由田间管理差异造成；对于平原地区，夏玉米产量估测精度可达90%以上；砂质土壤区估算冠层温度和水分胁迫系数比壤质、粘质土壤区要高，因此砂质土壤区模拟作物产量较低，这与PS-2计算结果、农户调查数据一致。研究证实，区域上应用遥感瞬时温度信息建立遥感-作物模拟复合模型进行估产是可行的。

Yield gap estimation by combining remote sensing and crop growth model

Conventional analytical crop growth models cannot handle actual land use systems because of massive data needs, algorithm complexity and prohibitive error propagation. So far, actual production could only be determined through field measurements. A methodology was presented for estimating regional levels of actual crop production. The difference between remotely sensed canopy temperature and ambient air temperature was used to estimate the degree of stomata closure of the crop. Introducing this remote sensing based degree of stomata closure in calculations of assimilatory activity permits to calculate the actual rate of crop growth over regions. An integrated model (the PS-X model) introduces NOAA-14 AVHRR remote sensing data into crop growth modeling. The PS-X model simulates crop growth and production at several levels of abstraction: it calculates the bio-physical production potential (PS-1), the water-limited production potential (PS-2) and the actual production (PS-X). It was run using data on summer maize collected in Handan, North China Plain (NCP) in 1998. Regionally differentiated yield gaps were analyzed by combining production calculations at PS-1, PS-2 and PS-X levels with farmer's household survey data. The yield gap between the PS-1 and PS-2 levels was largely explained by differences in soil type and rainfall. However the larger part of the yield gap between the bio-physical production potential and actual production (81.4%) exists between the PS-2 and PS-X levels and is largely caused by management factors. The estimation accuracy of summer maize production exceeded 90% for counties in the plain. Canopy temperature and crop water stress factor estimates were higher on sandy soil than those on loam or clay soils. Hence the modeled yield was lower for plots on sandy soil than that on other soils. This result was consistent with results obtained with analyses at the PS-2 level and with farmers interviews. This study confirms that it is feasible to estimate regional crop production by coupling instantaneous remotely sensed temperature information and crop growth simulation.