Ecological Modelling 183 (2005) 385–396 Remote sensing of crop production in China by production efficiency models: models comparisons, estimates and uncertainties Fulu Tao a,b,∗ , Masayuki Yokozawa b , Zhao Zhang c , Yinlong Xu a , Yousay Hayashi c a Chinese Academy of Agricultural Sciences, Institute of Agricultural Environment and Sustainable Development, Beijing 100081, China b National Institute for Agro-Environmental Sciences, 3-1-3 Kannondai, Tsukuba, Ibaraki 305-8604, Japan c Institute of Geoscience, University of Tsukuba, Tsukuba, Ibaraki 305-8571, Japan Received 11 February 2004; received in revised form 10 August 2004; accepted 25 August 2004 Abstract 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- Stanford-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. © 2004 Elsevier B.V. All rights reserved. Keywords: Crop yield; Global biogeochemical model; Food security warning system; Light use efficiency; NPP ∗ Corresponding author. Tel.: +81 29 838 8202; fax: +81 29 838 8199. E-mail address: taofl@niaes.affrc.go.jp (F. Tao). 1. Introduction Regional to global scale crop production monitor- ing and forecasting is important for agricultural man- 0304-3800/$ – see front matter © 2004 Elsevier B.V. All rights reserved. doi:10.1016/j.ecolmodel.2004.08.023