Main Article Content


Wheat is a major food grain crop of main agricultural region i.e. northern plain of India. Haryana state holds a premium position in wheat production (Rabi Season) in the country. Pre-harvest yield estimation of wheat has key role in policy framing. In Haryana, Agriculture is a big support to its economy which continues to occupy a prominent position in State GDP. In present research, Agromet-Spectrals models have been developed for this purpose i.e.yield estimation in Haryana with the help of input data such as meteorological indices and satellite based NDVI(NASA’s-MODIS) from 2000-2017. Empirical models were developed for predicting wheat yield for Hisar and Karnal districts representation the two agro-climatic zone of state in Haryana, India.The models were developed used weather variable (Temperature (Minimum and Maximum), Relative Humidity (Morning and Evening) and Rainfall) and spectral indices Normalized Difference Vegetative Index viz. Agromet- model(weather model) and Agromet-spectral model (MODIS-NDVI). Weather or Agromet model was integrated with NDVI values for both location to enhanced the accuracy of models. Regression models were developed using significant weather variables and NDVI data for wheat yield prediction at both location.  The result revealed that the models when integrated with remote sensing data (NDVI) gave better prediction as compared to agromet model that depends only on weather variables.  Agromet-models (adjusted R2 = 0.38 to 0.78) whereas satellite data based NDVI i.e. MODIS-NDVI for both stationgave best result (Adjusted R2 = 0.61-0.86) as compared to weather models. MODIS-NDVI pixel based values observed to be more effective for wheat yield predication in integrated with weather parameters.This study could help the provincial government of Haryana as well as in northern plains in estimation of yield prior harvest at first week of April by using weather spectral (NDVI-MODIS) models.


Agromet-Spectral model MODIS NDVI Remote sensing Weather parameter

Article Details

How to Cite
jeet, M., Anurag, Niwas , R. ., & Tomar, D. . . . (2022). Integrating weather model & Remote sensing indices for wheat yield prediction in Haryana, India. Environment Conservation Journal, 23(1&2), 124–130.


  1. Agrawal, R. and Mehta, S.C.(2007). Weather based forecasting of crop yields, pests and diseases-IASRI Models. Journal of Indian Society Agriculture statistics., 61(2): 255-263.
  2. Anonymous, (2019), news article dt. February 28, 2019.
  3. Babar, M.A., Reynolds, M.P., Van Ginkel, M., Klatt, A.R., Raun, W.R. and Stone, M.L., (2006). Spectral reflectance indices as a potential indirect selection criteria for wheat yield under irrigation. Crop Science, 46(2): 578-588.
  4. Bognar, P., Kern, A., Pásztor, S., Lichtenberger, J., Koronczay, D. and Ferencz, C., (2017).Yield estimation and forecasting for winter wheat in Hungary using time series of MODIS data. International journal of remote sensing, 38(11): 3394-3414.
  5. Fisher, R.A., (1924): On a Distribution Yielding the Error Functions of Several Well Known Statistics.
  6. Jin, X., Kumar, L., Li, Z., Feng, H., Xu, X., Yang, G. and Wang, J., (2018). A review of data assimilation of remote sensing and crop models.European Journal of Agronomy, 92:141-152.
  7. Lopresti, M. F., Di Bella, C. M. and Degioanni, A. J. (2015) ‘Relationship between MODIS-NDVI data and wheat yield: A case study in Northern Buenos Aires province, Argentina’, Information Processing in Agriculture. 2(2), pp. 73–84.
  8. Nagy, A., Fehér, J. and Tamás, J., (2018). Wheat and maize yield forecasting for the Tisza river catchment using MODIS NDVI time series and reported crop statistics. Computers and Electronics in Agriculture, 151: 41-49.
  9. Parida, B.R. and Ranjan, A.K., (2019). Wheat Acreage Mapping and Yield Prediction Using Landsat-8 OLI Satellite Data: a Case Study in Sahibganj Province, Jharkhand (India). Remote Sensing in Earth Systems Sciences, 2(2-3): .96-107.
  10. Ranjan, R., Nain, A.S. and Panwar, R.(2012), Predicting yield of wheat with remote sensing and weather data. Journal of Agrometeorology.9(2): 158-166.
  11. Rouse Jr, J.,Haas, R. H., Schell, J. A., & Deering, D. W. , (1974).‘Monitoring vegetation systems in the Great Plains with ERTS’ NASA. Goddard Space Flight Center 3d ERTS-1 Symposium1: 309–317.
  12. Saeed, U.,Dempewolf, J., Becker-Reshef, I., Khan, A., Ahmad, A., & Wajid, S. A. (2017), ‘Forecasting wheat yield from weather data and MODIS NDVI using Random Forests for Punjab province, Pakistan’, International journal of remote sensing. 38(17): 4831–4854.
  13. Singh, R.A.N.D.H.I.R., Semwal, D.P., Rai, A. and Chhikara, R.S., (2002). Small area estimation of crop yield using remote sensing satellite data. International Journal of Remote Sensing, 23(1), :49-56.
  14. Sisodia, B. V. S., Yadav, R. R., Kumar, S., & Sharma, M. K (2014).‘Forecasting of pre-harvest crop yield using discriminant function analysis of meteorological parameters’, Journal of Agrometeorology. 16(1): 121-125.
  15. Wang, Y., Xu, X., Huang, L., Yang, G., Fan, L., Wei, P. and Chen, G., (2019). An Improved CASA Model for Estimating Winter Wheat Yield from Remote Sensing Images. Remote Sensing, 11(9): 1-19.