A comprehensive assessment of the correlations between field crop yields and commonly used MODIS products

Abstract An exploratory assessment was undertaken to determine the correlation strength and optimal timing of several commonly used Moderate Resolution Imaging Spectroradiometer (MODIS) composited imagery products against crop yields for 10 globally significant agricultural commodities. The crops analyzed included barley, canola, corn, cotton, potatoes, rice, sorghum, soybeans, sugarbeets, and wheat. The MODIS data investigated included the Normalized Difference Vegetation Index (NDVI), Fraction of Photosynthetically Active Radiation (FPAR), Leaf Area Index (LAI), and Gross Primary Production (GPP), in addition to daytime Land Surface Temperature (DLST) and nighttime LST (NLST). The imagery utilized all had 8-day time intervals, but NDVI had a 250 m spatial resolution while the other products were 1000 m. These MODIS datasets were also assessed from both the Terra and Aqua satellites, with their differing overpass times, to document any differences. A follow-on analysis, using the Terra 250 m NDVI data as a benchmark, looked at the yield prediction utility of NDVI at two spatial scales (250 m vs. 1000 m), two time precisions (8-day vs. 16-day), and also assessed the Enhanced Vegetation Index (EVI, at 250 m, 16-day). The analyses spanned the major farming areas of the United States (US) from the summers of 2008–2013 and used annual county-level average crop yield data from the US Department of Agriculture as a basis. All crops, except rice, showed at least some positive correlations to each of the vegetation related indices in the middle of the growing season, with NDVI performing slightly better than FPAR. LAI was somewhat less strongly correlated and GPP weak overall. Conversely, some of the crops, particularly canola, corn, and soybeans, also showed negative correlations to DLST mid-summer. NLST, however, was never correlated to crop yield, regardless of the crop or seasonal timing. Differences between the Terra and Aqua results were found to be minimal. The 1000 m resolution NDVI showed somewhat poorer performance than the 250 m and suggests spatial resolution is helpful but not a necessity. The 8-day versus 16-day NDVI relationships to yields were very similar other than for the temporal precision. Finally, the EVI often showed the very best performance of all the variables, all things considered.

[1]  Z. Wan Collection-5 MODIS Land Surface Temperature Products Users' Guide , 2006 .

[2]  Douglas K. Bolton,et al.  Forecasting crop yield using remotely sensed vegetation indices and crop phenology metrics , 2013 .

[3]  Chris Funk,et al.  Phenologically-tuned MODIS NDVI-based production anomaly estimates for Zimbabwe , 2009 .

[4]  V. Murty,et al.  Estimation of cropped area and grain yield of rice using remote sensing data , 1992 .

[5]  J. Bruinsma World Agriculture: Towards 2015/2030: An Fao Perspective , 2002 .

[6]  Wenjiang Huang,et al.  Modelling paddy rice yield using MODIS data , 2014 .

[7]  David M. Johnson An assessment of pre- and within-season remotely sensed variables for forecasting corn and soybean yields in the United States , 2014 .

[8]  J. Gallego,et al.  Accuracy, Objectivity and Efficiency of Remote Sensing for Agricultural Statistics , 2010 .

[9]  Roberto Benedetti,et al.  On the use of NDVI profiles as a tool for agricultural statistics: The case study of wheat yield estimate and forecast in Emilia Romagna , 1993 .

[10]  D. Roy,et al.  An overview of MODIS Land data processing and product status , 2002 .

[11]  Louis Kouadio,et al.  Evaluation of the integrated Canadian crop yield forecaster (ICCYF) model for in-season prediction of crop yield across the Canadian agricultural landscape , 2015 .

[12]  Nguyen Hieu Trung,et al.  A comparative analysis of multitemporal MODIS EVI and NDVI data for large-scale rice yield estimation , 2014 .

[13]  A. Huete,et al.  Overview of the radiometric and biophysical performance of the MODIS vegetation indices , 2002 .

[14]  A. J. Stern,et al.  Application of MODIS derived parameters for regional crop yield assessment , 2005 .

[15]  R. Mueller,et al.  The 2009 Cropland Data Layer. , 2010 .

[16]  C. Domenikiotis,et al.  Early cotton yield assessment by the use of the NOAA/AVHRR derived Vegetation Condition Index (VCI) in Greece , 2004 .

[17]  N. Silleos,et al.  The use of multi-temporal NDVI measurements from AVHRR data for crop yield estimation and prediction , 1993 .

[18]  Zhengwei Yang,et al.  Monitoring US agriculture: the US Department of Agriculture, National Agricultural Statistics Service, Cropland Data Layer Program , 2011 .

[19]  C. Justice,et al.  A generalized regression-based model for forecasting winter wheat yields in Kansas and Ukraine using MODIS data , 2010 .

[20]  James E. McMurtrey,et al.  Relationship of spectral data to grain yield variation , 1980 .

[21]  A. Gitelson,et al.  Relationships between gross primary production, green LAI, and canopy chlorophyll content in maize: Implications for remote sensing of primary production , 2014 .

[22]  Z. Wan,et al.  Using MODIS Land Surface Temperature and Normalized Difference Vegetation Index products for monitoring drought in the southern Great Plains, USA , 2004 .

[23]  Shusen Wang,et al.  Crop yield forecasting on the Canadian Prairies using MODIS NDVI data , 2011 .

[24]  G. Timár,et al.  Crop yield estimation by satellite remote sensing , 2004 .

[25]  F. Kessler Projections , 2020, International Encyclopedia of Human Geography.

[26]  Felix Rembold,et al.  Analysis of GAC NDVI Data for Cropland Identification and Yield Forecasting in Mediterranean African Countries , 2001 .

[27]  Yuan Shen,et al.  Large-area rice yield forecasting using satellite imageries , 2010, Int. J. Appl. Earth Obs. Geoinformation.

[28]  Anatoly A. Gitelson,et al.  Monitoring Maize (Zea mays L.) Phenology with Remote Sensing , 2004 .

[29]  T. L. Barnett,et al.  The use of large-area spectral data in wheat yield estimation , 1982 .

[30]  Steven W. Running,et al.  Usefulness and limits of MODIS GPP for estimating wheat yield , 2005 .

[31]  Zhengwei Yang,et al.  CropScape: A Web service based application for exploring and disseminating US conterminous geospatial cropland data products for decision support , 2012 .

[32]  N. K. Patel,et al.  Estimation of rice yield using IRS-1A digital data in coastal tract of Orissa , 1991 .

[33]  Anatoly A. Gitelson,et al.  MODIS-based corn grain yield estimation model incorporating crop phenology information , 2013 .

[34]  M. S. Rasmussen Operational yield forecast using AVHRR NDVI data: reduction of environmental and inter-annual variability , 1997 .

[35]  Yan Li,et al.  Towards an operational system for regional-scale rice yield estimation using a time-series of Radarsat ScanSAR images , 2003 .

[36]  Johannes J. Feddema,et al.  MODIS land surface temperature composite data and their relationships with climatic water budget factors in the central Great Plains , 2005 .

[37]  Sujit Kumar Bala,et al.  Correlation between potato yield and MODIS‐derived vegetation indices , 2009 .

[38]  Terry L. Kastens,et al.  Image masking for crop yield forecasting using AVHRR NDVI time series imagery , 2005 .

[39]  A. Janvry,et al.  World development report 2008 : agriculture for development , 2008 .

[40]  J. Hatfield,et al.  Remote sensing estimators of potential and actual crop yield , 1983 .

[41]  Yang Shao,et al.  An analysis of cropland mask choice and ancillary data for annual corn yield forecasting using MODIS data , 2015, Int. J. Appl. Earth Obs. Geoinformation.

[42]  Andrew Davidson,et al.  Assessing the Performance of MODIS NDVI and EVI for Seasonal Crop Yield Forecasting at the Ecodistrict Scale , 2014, Remote. Sens..

[43]  Jing Huang,et al.  Meta-analysis of influential factors on crop yield estimation by remote sensing , 2014 .

[44]  M. Hayes,et al.  Using NOAA AVHRR data to estimate maize production in the United States Corn Belt , 1996 .

[45]  B. Brisco,et al.  Rice monitoring and production estimation using multitemporal RADARSAT , 2001 .

[46]  Jan G. P. W. Clevers,et al.  A simplified approach for yield prediction of sugar beet based on optical remote sensing data , 1997 .

[47]  Ramesh P. Singh,et al.  Crop yield estimation model for Iowa using remote sensing and surface parameters , 2006 .

[48]  Rick L. Lawrence,et al.  Wheat yield estimates using multi-temporal NDVI satellite imagery , 2002 .