Remote sensing of crop production in China by production efficiency models: models comparisons, estimates and uncertainties

文献类型: 外文期刊

第一作者: Tao, FL

作者: Tao, FL;Yokozawa, M;Zhang, Z;Xu, YL;Hayashi, Y

作者机构:

关键词: crop yield;global biogeochemical model;food security warning system;light use efficiency;NPP;NET PRIMARY PRODUCTION;LIGHT USE EFFICIENCY;VEGETATION INDEX;SATELLITE DATA;RADIATION;NPP;VARIABILITY;ECOSYSTEMS;ACCURACY;CLIMATE

期刊名称:ECOLOGICAL MODELLING ( 影响因子:2.974; 五年影响因子:3.264 )

ISSN:

年卷期:

页码:

收录情况: SCI

摘要: Regional estimates or prediction of crop production is critical for many applications such as agricultural lands management, food security warning system, food trade policy and carbon cycle research. Remote sensing offers great potential for regional production monitoring and estimates, yet uncertainties associated with are rarely addressed. Moreover, although crops are one of critical biomes in global carbon cycle research, few evidences are available on the performance of global models of terrestrial net primary productivity (NPP) in estimating regional crop NPP. In this study, we use high quality weather and crop data to calibrate model parameter. validate and compare two kinds of remote sensing based production efficiency models, i.e. the Carnegie-Ames-Stan ford-Approach (CASA) and Global Production Efficiency Model Version 2.0 (GLO-PEM2), in estimating maize production across China. Results show that both models intend to underestimate maize yields, although they also overestimate maize yields much at some regions. There are no significant differences between the results from CASA and GLO-PEM2 models in terms of both estimated production and spatial pattern. CASA model simulates better in the areas with dense crop and weather data for calibration. Otherwise GLO-PEM2 model does better. Whether the water soil-moisture down-regulator is used or not should depend on the percent of irrigation lands at the regions. The improved and validated models can be used for many applications. Further improvement can be expected by increasing remote sensing image resolution and the number of surface data stations. (c) 2004 Elsevier B.V. All rights reserved.

分类号: X17

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