Remote sensing based yield monitoring: Application to winter wheat in United States and Ukraine

Abstract Accurate and timely crop yield forecasts are critical for making informed agricultural policies and investments, as well as increasing market efficiency and stability. Earth observation data from space can contribute to agricultural monitoring, including crop yield assessment and forecasting. In this study, we present a new crop yield model based on the Difference Vegetation Index (DVI) extracted from Moderate Resolution Imaging Spectroradiometer (MODIS) data at 1 km resolution and the un-mixing of DVI at coarse resolution to a pure wheat signal (100% of wheat within the pixel). The model was applied to estimate the national and subnational winter wheat yield in the United States and Ukraine from 2001 to 2017. The model at the subnational level shows very good performance for both countries with a coefficient of determination higher than 0.7 and a root mean square error (RMSE) of lower than 0.6 t/ha (15–18%). At the national level for the United States (US) and Ukraine the model provides a strong coefficient of determination of 0.81 and 0.86, respectively, which demonstrates good performance at this scale. The model was also able to capture low winter wheat yields during years with extreme weather events, for example 2002 in US and 2003 in Ukraine. The RMSE of the model for the US at the national scale is 0.11 t/ha (3.7%) while for Ukraine it is 0.27 t/ha (8.4%).

[1]  R. D. Jackson,et al.  Multidate spectral reflectance as predictors of yield in water stressed wheat and barley , 1981 .

[2]  R. Macdonald.  A summary of the history of the development of automated remote sensing for agricultural applications , 1984, IEEE Transactions on Geoscience and Remote Sensing.

[3]  R. Motzo,et al.  Effect of drought on yield and yield components of durum wheat and triticale in a Mediterranean environment , 1993 .

[4]  Giovanni Ravazzani,et al.  Modified Hargreaves-Samani Equation for the Assessment of Reference Evapotranspiration in Alpine River Basins , 2012 .

[5]  C. Justice,et al.  Atmospheric correction of MODIS data in the visible to middle infrared: first results , 2002 .

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

[7]  José A. Sobrino,et al.  Retrieval of Surface Albedo on a Daily Basis: Application to MODIS Data , 2014, IEEE Transactions on Geoscience and Remote Sensing.

[8]  Frank Veroustraete,et al.  Estimating evapotranspiration of European forests from NOAA-imagery at satellite overpass time: Towards an operational processing chain for integrated optical and thermal sensor data products , 2005 .

[9]  P. Rollin,et al.  AAFC annual crop inventory , 2013, 2013 Second International Conference on Agro-Geoinformatics (Agro-Geoinformatics).

[10]  A. Holtslag,et al.  A remote sensing surface energy balance algorithm for land (SEBAL)-1. Formulation , 1998 .

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

[12]  W. Lucht.  Expected retrieval accuracies of bidirectional reflectance and albedo from EOS-MODIS and MISR angular sampling , 1998 .

[13]  J. Roujean,et al.  A bidirectional reflectance model of the Earth's surface for the correction of remote sensing data , 1992 .

[14]  Wim G.M. Bastiaanssen,et al.  Linear relationships between surface reflectance and temperature and their application to map actual evaporation of groundwater , 1989 .

[15]  Jianxi Huang,et al.  Improving the timeliness of winter wheat production forecast in the United States of America, Ukraine and China using MODIS data and NCAR Growing Degree Day information , 2015 .

[16]  A. J. Richardsons,et al.  DISTINGUISHING VEGETATION FROM SOIL BACKGROUND INFORMATION , 1977 .

[17]  R. Fischer,et al.  Drought resistance in spring wheat cultivars, 1. Grain yield responses. , 1978 .

[18]  Jeff Dozier,et al.  A generalized split-window algorithm for retrieving land-surface temperature from space , 1996, IEEE Trans. Geosci. Remote. Sens..

[19]  Nataliia Kussul,et al.  Winter wheat yield forecasting in Ukraine based on Earth observation, meteorological data and biophysical models , 2013, Int. J. Appl. Earth Obs. Geoinformation.

[20]  Alan H. Strahler,et al.  Geometric-optical bidirectional reflectance modeling of the discrete crown vegetation canopy: effect of crown shape and mutual shadowing , 1992, IEEE Trans. Geosci. Remote. Sens..

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

[22]  F. Baret,et al.  Potentials and limits of vegetation indices for LAI and APAR assessment , 1991 .

[23]  Massimo Menenti,et al.  S-SEBI: A simple remote sensing algorithm to estimate the surface energy balance , 2000 .

[24]  E. Vermote,et al.  Combined use of Landsat-8 and Sentinel-2A images for winter crop mapping and winter wheat yield assessment at regional scale. , 2017, AIMS geosciences.

[25]  Massimo Menenti,et al.  Aggregation effects of surface heterogeneity in land surface processes , 1999 .

[26]  Claus Buschmann,et al.  In vivo spectroscopy and internal optics of leaves as basis for remote sensing of vegetation , 1993 .

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

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

[29]  Michele Meroni,et al.  Yield estimation using SPOT-VEGETATION products: A case study of wheat in European countries , 2014, Int. J. Appl. Earth Obs. Geoinformation.

[30]  S. Goward,et al.  Evaluating North American net primary productivity with satellite observations , 1987 .

[31]  Z. Su.  The Surface Energy Balance System (SEBS) for estimation of turbulent heat fluxes , 2002 .

[32]  Albert Olioso,et al.  Retrieval of evapotranspiration over the Alpilles/ReSeDA experimental site using airborne POLDER sensor and a thermal camera , 2005 .

[33]  C. Justice,et al.  The Harmonized Landsat and Sentinel-2 surface reflectance data set , 2018, Remote Sensing of Environment.

[34]  Michele Meroni,et al.  Remote Sensing Based Yield Estimation in a Stochastic Framework - Case Study of Durum Wheat in Tunisia , 2013, Remote. Sens..

[35]  A. Gitelson,et al.  Novel algorithms for remote estimation of vegetation fraction , 2002 .

[36]  Michele Meroni,et al.  Towards regional grain yield forecasting with 1km-resolution EO biophysical products: Strengths and limitations at pan-European level , 2015 .

[37]  Martha C. Anderson,et al.  Evaluation of Drought Indices Based on Thermal Remote Sensing of Evapotranspiration over the Continental United States , 2011 .

[38]  Nataliia Kussul,et al.  Regional retrospective high resolution land cover for Ukraine: Methodology and results , 2015, 2015 IEEE International Geoscience and Remote Sensing Symposium (IGARSS).

[39]  P. Vellinga,et al.  Climate Change and Extreme Weather Events , 2000 .

[40]  Wolfgang Lucht,et al.  Theoretical noise sensitivity of BRDF and albedo retrieval from the EOS-MODIS and MISR sensors with respect to angular sampling , 2000 .

[41]  Richard G. Allen,et al.  Satellite-Based Energy Balance for Mapping Evapotranspiration with Internalized Calibration (METRIC)—Model , 2007 .

[42]  N. C. Strugnell,et al.  First operational BRDF, albedo nadir reflectance products from MODIS , 2002 .

[43]  A. Gitelson,et al.  Vegetation and soil lines in visible spectral space: A concept and technique for remote estimation of vegetation fraction , 2002 .

[44]  Feng Gao,et al.  The Evaporative Stress Index as an indicator of agricultural drought in Brazil: An assessment based on crop yield impacts , 2016 .

[45]  E. Vermote,et al.  Measuring the Directional Variations of Land Surface Reflectance From MODIS , 2015, IEEE Transactions on Geoscience and Remote Sensing.

[46]  Nataliia Kussul,et al.  Early Season Large-Area Winter Crop Mapping Using MODIS NDVI Data, Growing Degree Days Information and a Gaussian Mixture Model , 2017 .

[47]  C. Tucker,et al.  Satellite remote sensing of total dry matter production in the Senegalese Sahel , 1983 .

[48]  P. Sellers.  Remote sensing of the land surface for studies of global change , 1993 .

[49]  M. Fuller,et al.  The freezing characteristics of wheat at ear emergence , 2007 .

[50]  Mykola Lavreniuk,et al.  Large-Scale Classification of Land Cover Using Retrospective Satellite Data , 2016 .

[51]  Serhiy Skakun,et al.  Prior Season Crop Type Masks for Winter Wheat Yield Forecasting: A US Case Study , 2018, Remote. Sens..

[52]  A. Gitelson.  Wide Dynamic Range Vegetation Index for remote quantification of biophysical characteristics of vegetation. , 2004, Journal of plant physiology.

[53]  F. Maignan,et al.  Bidirectional reflectance of Earth targets: evaluation of analytical models using a large set of spaceborne measurements with emphasis on the Hot Spot , 2004 .

[54]  Christopher Justice,et al.  Towards a Generalized Approach for Correction of the BRDF Effect in MODIS Directional Reflectances , 2009, IEEE Transactions on Geoscience and Remote Sensing.

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

[56]  Robert E. Wolfe,et al.  A 30+ year AVHRR Land Surface Reflectance Climate Data Record and its application to wheat yield monitoring , 2017, Remote. Sens..