The use of satellite data for crop yield gap analysis

被引:251
作者
Lobell, David B. [1 ,2 ]
机构
[1] Stanford Univ, Dept Environm Earth Syst Sci, Stanford, CA 94305 USA
[2] Stanford Univ, Ctr Food Secur & Environm, Stanford, CA 94305 USA
关键词
Remote sensing; GIS; Agronomy; REMOTE-SENSING DATA; LEAF-AREA; TIME-SERIES; MODEL; REFLECTANCE; FIELD; LANDSAT; PRODUCTIVITY; PERFORMANCE; MANAGEMENT;
D O I
10.1016/j.fcr.2012.08.008
中图分类号
S3 [农学(农艺学)];
学科分类号
0901 ;
摘要
Field experiments and simulation models are useful tools for understanding crop yield gaps, but scaling up these approaches to understand entire regions over time has remained a considerable challenge. Satellite data have repeatedly been shown to provide information that, by themselves or in combination with other data and models, can accurately measure crop yields in farmers' fields. The resulting yield maps provide a unique opportunity to overcome both spatial and temporal scaling challenges and thus improve understanding of crop yield gaps. This review discusses the use of remote sensing to measure the magnitude and causes of yield gaps. Examples from previous work demonstrate the utility of remote sensing, but many areas of possible application remain unexplored. Two simple yet useful approaches are presented that measure the persistence of yield differences between fields, which in combination with maps of average yields can be used to direct further study of specific factors. Whereas the use of remote sensing may have historically been restricted by the cost and availability of fine resolution data, this impediment is rapidly receding. (C) 2012 Elsevier B.V. All rights reserved.
引用
收藏
页码:56 / 64
页数:9
相关论文
共 50 条
[1]  
Allen R., 2002, Limited use of remotely sensed data for crop condition monitoring and crop yield forecasting in NASS
[2]  
Baruth Bettina., 2008, Proceedings of the 21st Congress of the International Society for Photogrammetry and Remote Sensing, V37, P935
[3]   A new crop yield forecasting model based on satellite measurements applied across the Indus Basin, Pakistan [J].
Bastiaanssen, WGM ;
Ali, S .
AGRICULTURE ECOSYSTEMS & ENVIRONMENT, 2003, 94 (03) :321-340
[4]   A generalized regression-based model for forecasting winter wheat yields in Kansas and Ukraine using MODIS data [J].
Becker-Reshef, I. ;
Vermote, E. ;
Lindeman, M. ;
Justice, C. .
REMOTE SENSING OF ENVIRONMENT, 2010, 114 (06) :1312-1323
[5]   RESOURCE LIMITATION IN PLANTS - AN ECONOMIC ANALOGY [J].
BLOOM, AJ ;
CHAPIN, FS ;
MOONEY, HA .
ANNUAL REVIEW OF ECOLOGY AND SYSTEMATICS, 1985, 16 :363-392
[6]   A simplified approach for yield prediction of sugar beet based on optical remote sensing data [J].
Clevers, JGPW .
REMOTE SENSING OF ENVIRONMENT, 1997, 61 (02) :221-228
[7]   Assimilation of leaf area index derived from ASAR and MERIS data into CERES-Wheat model to map wheat yield [J].
Dente, Laura ;
Satalino, Giuseppe ;
Mattia, Francesco ;
Rinaldi, Michele .
REMOTE SENSING OF ENVIRONMENT, 2008, 112 (04) :1395-1407
[8]   Application of MODIS derived parameters for regional crop yield assessment [J].
Doraiswamy, PC ;
Sinclair, TR ;
Hollinger, S ;
Akhmedov, B ;
Stern, A ;
Prueger, J .
REMOTE SENSING OF ENVIRONMENT, 2005, 97 (02) :192-202
[9]   Crop condition and yield simulations using Landsat and MODIS [J].
Doraiswamy, PC ;
Hatfield, JL ;
Jackson, TJ ;
Akhmedov, B ;
Prueger, J ;
Stern, A .
REMOTE SENSING OF ENVIRONMENT, 2004, 92 (04) :548-559
[10]  
Gallego J., 2010, Agricultural Survey Methods, P193, DOI [10.1002/9780470665480.ch12, DOI 10.1002/9780470665480.CH12]