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

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.

[1]  Fulu Tao,et al.  Changes in agricultural water demands and soil moisture in China over the last half-century and their effects on agricultural production , 2003 .

[2]  G. B Samarasinghe,et al.  Growth and yields of Sri Lanka's major crops interpreted from public domain satellites , 2003 .

[3]  S. M. E. Groten,et al.  NDVI—crop monitoring and early yield assessment of Burkina Faso , 1993 .

[4]  Warren B. Cohen,et al.  Effects of spatial variability in light use efficiency on satellite-based NPP monitoring , 2002 .

[5]  S. Prince.  A model of regional primary production for use with coarse resolution satellite data , 1991 .

[6]  K. Eckhardt,et al.  Plant parameter values for models in temperate climates , 2003 .

[7]  Bhaskar J. Choudhury,et al.  Relationships between vegetation indices, radiation absorption, and net photosynthesis evaluated by a sensitivity analysis , 1987 .

[8]  T. D. Mitchell,et al.  A comprehensive set of high-resolution grids of monthly climate for Europe and the globe: the observed record (1901-2000) and 16 scenarios (2001-2100). , 2004 .

[9]  Scott J. Goetz,et al.  Interannual variability of global terrestrial primary production: Results of a model driven with satellite observations , 2000 .

[10]  Craig S. T. Daughtry,et al.  Errors in measuring absorbed radiation and computing crop radiation use efficiency , 1993 .

[11]  Jonathan D. Haskett,et al.  NET PRIMARY PRODUCTION OF U.S. MIDWEST CROPLANDS FROM AGRICULTURAL HARVEST YIELD DATA , 2001 .

[12]  B. Choudhury.  A biophysical process based approach for estimating net primary production using satellite and ground observations , 2002 .

[13]  J. Randerson,et al.  Terrestrial ecosystem production: A process model based on global satellite and surface data , 1993 .

[14]  J. Monteith.  SOLAR RADIATION AND PRODUCTIVITY IN TROPICAL ECOSYSTEMS , 1972 .

[15]  B. Choudhury,et al.  Spatial heterogeneity in vegetation canopies and remote sensing of absorbed photosynthetically active radiation: A modeling study , 1992 .

[16]  R. Reynolds,et al.  The NCEP/NCAR 40-Year Reanalysis Project , 1996, Renewable Energy.

[17]  Canada..  Joint United States Canadian tables of feed composition. , 1964 .

[18]  Ranganath R. Navalgund,et al.  Procedures for wheat yield prediction using Landsat MSS and IRS-1 A data , 1993 .

[19]  David B. Lobell,et al.  Remote sensing of regional crop production in the Yaqui Valley, Mexico: estimates and uncertainties , 2003 .

[20]  J. Monteith.  Climate and the efficiency of crop production in Britain , 1977 .

[21]  C. Tucker,et al.  A Global 9-yr Biophysical Land Surface Dataset from NOAA AVHRR Data , 2000 .

[22]  Tom Lyche,et al.  Fortran subroutines for computing smoothing and interpolating natural splines , 1983 .

[23]  S. Goward,et al.  Global Primary Production: A Remote Sensing Approach , 1995 .

[24]  S. Goward,et al.  Vegetation canopy PAR absorptance and the normalized difference vegetation index - An assessment using the SAIL model , 1992 .

[25]  C. Tucker,et al.  Satellite estimates of productivity and light use efficiency in United States agriculture, 1982–98 , 2002 .

[26]  R. Hay,et al.  Harvest index: a review of its use in plant breeding and crop physiology , 1995 .

[27]  Brent N. Holben,et al.  Accuracy of the AVHRR vegetation index as a predictor of biomass, primary productivity and net CO2 flux , 1989, Vegetatio.

[28]  W. Bastiaanssen,et al.  A new crop yield forecasting model based on satellite measurements applied across the Indus Basin, Pakistan , 2003 .

[29]  E. L. Anderson.  Tillage and N fertilization effects on maize root growth and root:shoot ratio , 1988, Plant and Soil.

[30]  Akihiko Ito,et al.  A simulation model of the carbon cycle in land ecosystems (Sim-CYCLE) : A description based on dry-matter production theory and plot-scale validation , 2002 .

[31]  Bunkei Matsushita,et al.  Estimation of regional net primary productivity (NPP) using a process-based ecosystem model: How important is the accuracy of climate data? , 2004 .

[32]  Alfredo Huete,et al.  Estimating crop yields and production by integrating the FAO Crop Specific Water Balance model with real-time satellite data and ground-based ancillary data , 2000 .

[33]  J. Hunt,et al.  Relationship between woody biomass and PAR conversion efficiency for estimating net primary production from NDVI , 1994 .

[34]  S. Goetz,et al.  Satellite remote sensing of primary production : an improved production efficiency modeling approach , 1999 .

[35]  D. Randall,et al.  A Revised Land Surface Parameterization (SiB2) for Atmospheric GCMS. Part I: Model Formulation , 1996 .

[36]  Gérard Dedieu,et al.  TURC: A diagnostic model of continental gross primary productivity and net primary productivity , 1996 .

[37]  James R. Kercher,et al.  Parameter estimation for a global model of terrestrial biogeochemical cycling by an iterative method , 2001 .

[38]  Jingli Yang.  A theoretical assessment of the relation between woody canopy cover and red reflectance , 1997 .

[39]  A. Bondeau,et al.  Comparing global models of terrestrial net primary productivity (NPP): overview and key results , 1999 .

[40]  A. Moore.  Encyclopaedia of plant physiology - new series: Volume 3 Edited by C. R. Stocking and U. Heber Springer; Berlin, 1976 xvii + 517 pages. DM 145, $59.50 , 1978 .

[41]  J. Randerson,et al.  Global net primary production: Combining ecology and remote sensing , 1995 .

[42]  P. G. Jarvis,et al.  Productivity of temperate de-ciduous and evergreen forests , 1983 .

[43]  Gérard Dedieu,et al.  Methodology for the estimation of terrestrial net primary production from remotely sensed data , 1994 .