Determination of Appropriate Remote Sensing Indices for Spring Wheat Yield Estimation in Mongolia

In Mongolia, the monitoring and estimation of spring wheat yield at the regional and national levels are key issues for the agricultural policy and food management as well as for the economy and society as a whole. The remote sensing data and technique have been widely used for the estimation of crop yield and production in the world. For the current research, nine remote sensing indices were tested that include normalized difference drought index (NDDI), normalized difference water index (NDWI), vegetation condition index (VCI), temperature condition index (TCI), vegetation health index (VHI), normalized multi-band drought index (NMDI), visible and shortwave infrared drought index (VSDI), and vegetation supply water index (VSWI). These nine indices derived from MODIS/Terra satellite have so far not been used for crop yield prediction in Mongolia. The primary objective of this study was to determine the best remote sensing indices in order to develop an estimation model for spring wheat yield using correlation and regression method. The spring wheat yield data from the ground measurements of eight meteorological stations in Darkhan and Selenge provinces from 2000 to 2017 have been used. The data were collected during the period of the growing season (June–August). Based on the analysis, we constructed six models for spring wheat yield estimation. The results showed that the range of the root-mean-square error (RMSE) values of estimated spring wheat yield was between 4.1 (100 kg ha−1) to 4.8 (100 kg ha−1), respectively. The range of the mean absolute error (MAE) values was between 3.3 to 3.8 and the index of agreement (d) values was between 0.74 to 0.84, respectively. The conclusion was that the best model would be (R2 = 0.55) based on NDWI, VSDI, and NDVI out of the nine indices and could serve as the most effective predictor and reliable remote sensing indices for monitoring the spring wheat yield in the northern part of Mongolia. Our results showed that the best timing of yield prediction for spring wheat was around the end of June and the beginning of July, which is the flowering stage of spring wheat in this study area. This means an accurate yield prediction for spring wheat can be achieved two months before the harvest time using the regression model.

[1]  L. Mundy,et al.  THE COORDINATED RADIO AND INFRARED SURVEY FOR HIGH-MASS STAR FORMATION. II. SOURCE CATALOG , 2012, 1211.7116.

[2]  Anatoly A. Gitelson,et al.  An evaluation of MODIS 8- and 16-day composite products for monitoring maize green leaf area index , 2012 .

[3]  M. Bindi,et al.  A simple model of regional wheat yield based on NDVI data , 2007 .

[4]  Junho Yeom,et al.  Comparison of Vegetation Indices Derived from UAV Data for Differentiation of Tillage Effects in Agriculture , 2019, Remote. Sens..

[5]  Ahmad Al Bitar,et al.  Estimating maize biomass and yield over large areas using high spatial and temporal resolution Sentinel-2 like remote sensing data , 2016 .

[6]  M. Rosegrant,et al.  Global Food Security: Challenges and Policies , 2003, Science.

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

[8]  Wang Jianbo,et al.  Vegetation supply water index based on MODIS data Analysis of the in Yunnan in spring of 2012 , 2014, 2014 The Third International Conference on Agro-Geoinformatics.

[9]  Sheng Chang,et al.  Suitability Assessment of Satellite-Derived Drought Indices for Mongolian Grassland , 2017, Remote. Sens..

[10]  Compton J. Tucker,et al.  Satellite remote sensing of total herbaceous biomass production in the Senegalese Sahel - 1980-1984 , 1985 .

[11]  Lillian Kay Petersen,et al.  Real-Time Prediction of Crop Yields From MODIS Relative Vegetation Health: A Continent-Wide Analysis of Africa , 2018, Remote. Sens..

[12]  M. Shinoda,et al.  Phenology of Mongolian Grasslands and Moisture Conditions , 2007 .

[13]  C. Atzberger,et al.  Land Suitability Evaluation for Agricultural Cropland in Mongolia Using the Spatial MCDM Method and AHP Based GIS , 2017 .

[14]  V. Shokri,et al.  Effects of Climate Change and Drought-Stress on Plant Physiology , 2015 .

[15]  F. Kogan,et al.  World droughts in the new millennium from AVHRR‐based vegetation health indices , 2002 .

[16]  Gaohuan Liu,et al.  Evaluation and Comparison of Random Forest and A-LSTM Networks for Large-scale Winter Wheat Identification , 2019, Remote. Sens..

[17]  Sha Zhang,et al.  NDVI anomaly for drought monitoring and its correlation with climate factors over Mongolia from 2000 to 2016 , 2019, Journal of Arid Environments.

[18]  C. Tucker.  Red and photographic infrared linear combinations for monitoring vegetation , 1979 .

[19]  P. de Maeyer,et al.  Long-term soil moisture content estimation using satellite and climate data in agricultural area of Mongolia , 2019 .

[20]  James P. Verdin,et al.  A five‐year analysis of MODIS NDVI and NDWI for grassland drought assessment over the central Great Plains of the United States , 2007 .

[21]  C. D. Bella,et al.  Relationship between MODIS-NDVI data and wheat yield: A case study in Northern Buenos Aires province, Argentina , 2015 .

[22]  F. Kogan,et al.  Drought Monitoring and Corn Yield Estimation in Southern Africa from AVHRR Data , 1998 .

[23]  B. Gao.  NDWI—A normalized difference water index for remote sensing of vegetation liquid water from space , 1996 .

[24]  T. Carlson,et al.  A method to make use of thermal infrared temperature and NDVI measurements to infer surface soil water content and fractional vegetation cover , 1994 .

[25]  F. Kogan.  Application of vegetation index and brightness temperature for drought detection , 1995 .

[26]  A. Gitelson,et al.  AVHRR-Based Spectral Vegetation Index for Quantitative Assessment of Vegetation State and Productivity: Calibration and Validation , 2003 .

[27]  Herman Eerens,et al.  Empirical regression models using NDVI, rainfall and temperature data for the early prediction of wheat grain yields in Morocco , 2008, Int. J. Appl. Earth Obs. Geoinformation.

[28]  Catherine Champagne,et al.  Field-Scale Crop Seeding Date Estimation from MODIS Data and Growing Degree Days in Manitoba, Canada , 2019, Remote. Sens..

[29]  M. Shinoda,et al.  Seasonal change of soil moisture in Mongolia: its climatology and modelling , 2011 .

[30]  Wenjiang Huang,et al.  Evaluation of MODIS and Landsat multiband vegetation indices used for wheat yield estimation in irrigated Indus Basin , 2017, Comput. Electron. Agric..

[31]  Clement Atzberger,et al.  Advances in Remote Sensing of Agriculture: Context Description, Existing Operational Monitoring Systems and Major Information Needs , 2013, Remote. Sens..

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

[33]  Ramesh P. Singh,et al.  Vegetation and temperature condition indices from NOAA AVHRR data for drought monitoring over India , 2003 .

[34]  J. Malingreau.  Global vegetation dynamics - Satellite observations over Asia , 1986 .

[35]  D. Fumagalli,et al.  Improving WOFOST model to simulate winter wheat phenology in Europe: Evaluation and effects on yield , 2019, Agricultural Systems.

[36]  B. Holben.  Characteristics of maximum-value composite images from temporal AVHRR data , 1986 .

[38]  Q. Qin,et al.  Evaluation of the visible and shortwave infrared drought index in China , 2013, International Journal of Disaster Risk Science.

[39]  D. Loka.  Effect of water-deficit stress on cotton during reproductive development , 2012 .

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

[41]  Yuei-An Liou,et al.  Time Series MODIS and in Situ Data Analysis for Mongolia Drought , 2016, Remote. Sens..

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

[43]  W. Marandu,et al.  COUNTRY REPORT TO THE FAO INTERNATIONAL TECHNICAL CONFERENCE ON PLANT GENETIC RESOURCES , 1996 .

[44]  Huazhong Ren,et al.  Winter Wheat Production Estimation Based on Environmental Stress Factors from Satellite Observations , 2018, Remote. Sens..

[45]  A. Gitelson,et al.  Near real-time prediction of U.S. corn yields based on time-series MODIS data , 2014 .

[46]  K. Moffett,et al.  Remote Sens , 2015 .

[47]  Thomas J. Schmugge,et al.  Remote estimation of soil moisture availability and fractional vegetation cover for agricultural fields , 1990 .

[48]  Gang Bao,et al.  NDVI-Based Long-Term Vegetation Dynamics and Its Response to Climatic Change in the Mongolian Plateau , 2014, Remote. Sens..

[49]  Philip Lewis,et al.  Evaluation of regional estimates of winter wheat yield by assimilating three remotely sensed reflectance datasets into the coupled WOFOST–PROSAIL model , 2019, European Journal of Agronomy.

[50]  Huajun Tang,et al.  Regional yield estimation for winter wheat with MODIS-NDVI data in Shandong, China , 2008, Int. J. Appl. Earth Obs. Geoinformation.

[51]  S. Vicente‐Serrano,et al.  Early prediction of crop production using drought indices at different time‐scales and remote sensing data: application in the Ebro Valley (north‐east Spain) , 2006 .

[52]  James W. Jones,et al.  The DSSAT cropping system model , 2003 .

[53]  S. Khudulmur,et al.  Desertification atlas of Mongolia , 2014 .

[54]  J. Qu,et al.  NMDI: A normalized multi‐band drought index for monitoring soil and vegetation moisture with satellite remote sensing , 2007 .

[55]  Dehai Zhu,et al.  Assimilating SAR and Optical Remote Sensing Data into WOFOST Model for Improving Winter Wheat Yield Estimation , 2018, 2018 7th International Conference on Agro-geoinformatics (Agro-geoinformatics).

[56]  F. Kogan.  Droughts of the Late 1980s in the United States as Derived from NOAA Polar-Orbiting Satellite Data , 1995 .

[57]  James Rowland,et al.  Estimating maize production in Kenya using NDVI: Some statistical considerations , 1998 .

[58]  Ya Baasandorj,et al.  BIOLOGICAL REHABILITATION IN THE DEGRADED LAND, A CASE STUDY OF SHARIINGOL SOUM OF SELENGE AIMAG IN MONGOLIA , 2015 .

[59]  A. Suleiman,et al.  Application and evaluation of the DSSAT-wheat in the Tiaret region of Algeria , 2008 .

[60]  R. Confalonieri,et al.  WOFOST-GTC: A new model for the simulation of winter rapeseed production and oil quality , 2016 .

[61]  Ahmad Khan,et al.  Wheat Yield Forecasting for Punjab Province from Vegetation Index Time Series and Historic Crop Statistics , 2014, Remote. Sens..

[62]  M. Mikami,et al.  Topographical and hydrological effects on meso-scale vegetation in desert steppe, Mongolia , 2017, Journal of Arid Land.

[63]  A. Gitelson,et al.  Use of a green channel in remote sensing of global vegetation from EOS- MODIS , 1996 .

[64]  J. L. Araus,et al.  Usefulness of spectral reflectance indices as durum wheat yield predictors under contrasting Mediterranean conditions , 2003 .

[65]  F. Kogan.  Operational Space Technology for Global Vegetation Assessment , 2001 .

[66]  F. Loreto,et al.  Drought-stress effects on physiology, growth and biomass production of rainfed and irrigated bell pepper plants in the Mediterranean region , 2001 .

[67]  Clement Atzberger,et al.  Using Low Resolution Satellite Imagery for Yield Prediction and Yield Anomaly Detection , 2013, Remote. Sens..