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Multiple crop yield estimation and forecasting have become essential because climate change and land degradation are deteriorating production of crops. The present study presents multiple crop yield estimation and forecasting efficiently and precisely using MERRA-2 model, satellite-gauge and MODIS-Terra satellite data by regression and time series models. Rice, wheat, maize, rapeseed &amp; mustard, and millet crops yield data of India was used for crop yield estimation and forecasting using Multiple Linear Regression (MLR), Decomposition, Holt, Winters and Autoregressive Integrated Moving Average (ARIMA) models. Models were developed using crops yield historical data, weather variables (temperature, precipitation, surface radiation) and pollutants (O 3 , CO) data of 1982–83 to 2014–15. Overall best correlation ( R  = 0.96) and poor correlation ( R  = 0.56) was found between observed and estimated rice and rapeseed &amp; mustard crops using Decomposition and MLR models, respectively. After rigorous skill assessment and out of more than 20 combinations of ARIMA models, the ARIMA (0, 1, 1) time series model performed the best with Root Mean Square Error (RMSE = 4.59%) for wheat crop estimation. Forecasted crop yields were successfully cross-validated using four years’ data (2015–16, 2016–17, 2017–18 and 2018–19) for regression and time series models by comparing Relative Error (RE%) between observed and forecasted crops yields. Results were statistically analyzed using correlation coefficient ( R ), Bayesian Information Criteria (BIC), RMSE%, Mean Absolute Percentage Error (MAPE%),  t -value and  p -value. The crop yield estimation and forecasting results showed reasonably quantifiable information with the actual datasets. The outcomes of the present study have policy level implications for compensation to farming communities for the loss of crops.

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Multiple crop yield estimation and forecasting using MERRA-2 model, satellite-gauge and MODIS satellite data by time series and regression modelling approach