Wheat Yield Forecasting for the Tisza River Catchment Using Landsat 8 NDVI and SAVI Time Series and Reported Crop Statistics

Abstract

Due to the increasing global demand of food grain, early and reliable information on crop production is important in decision making in agricultural production. Remote sensing (RS)-based forecast models developed from vegetation indices have the potential to give quantitative and timely information on crops for larger regions or even at farm scale. Different vegetation indices are being used for this purpose, however, their efficiency in estimating crop yield certainly needs to be tested. In this study, wheat yield was derived by linear regressing reported yield values against a time series of six different peak-seasons (2013–2018) using the Landsat 8-derived Normalized Difference Vegetation Index (NDVI) and Soil Adjusted Vegetation Index (SAVI). NDVI- and SAVI-based forecasting models were validated based on 2018–2019 datasets and compared to evaluate the most appropriate index that performs better in forecasting wheat production in the Tisza river basin. Nash-Sutcliffe efficiency index was positive with E1 = 0.716 for the model from NDVI and for SAVI E1 = 0.909, which means that the forecasting method developed and performed good forecast efficiency. The best time for wheat yield prediction with Landsat 8-SAVI and NDVI was found to be the beginning of full biomass period from the 138th to 167th day of the year (18 May to 16 June; BBCH scale: 41–71) with high regression coefficients between the vegetation indices and the wheat yield. The RMSE of the NDVI-based prediction model was 0.357 t/ha (NRMSE: 7.33%). The RMSE of the SAVI-based prediction model was 0.191 t/ha (NRMSE 3.86%). The validation of the results revealed that the SAVI-based model provided more accurate forecasts compared to NDVI. Overall, probable yield amount is possible to predict far before harvest (six weeks earlier) based on Landsat 8 NDVI and SAVI and generating simple thresholds for yield forecasting, and a potential loss of wheat yield can be mapped.

Monitoring Onion Crop “Cipolla Rossa di Tropea Calabria IGP” Growth and Yield Response to Varying Nitrogen Fertilizer Application Rates Using UAV Imagery

“…Finally, according to Al-Gaadi et al [97], Venancio et al [98], and Nagy et al [99], who found positive relations between SAVI and plant growth in potato, corn, and wheat, respectively, our study confirms the reliability of the SAVI index to give helpful information regarding plant development [6,27,28] and yield expectation also for onion crop typically characterized by sparse vegetation and a low level of soil covering.…”

Wheat Yield Prediction with Machine Learning based on MODIS and Landsat NDVI Data at Field Scale

“…. In the study conducted in the Tisza basin in Hungary (Nagy et al, 2021), the strongest relationship (R 2 : 0.64) was observed between the NDVI data obtained from Landsat 8 images and the yield for the May-early June period when the wheat reached its peak level.…”

Evaluating how lodging affects maize yield estimation based on UAV observations

“…Remote sensing technology can quickly and accurately obtain wall-to-wall information on the land surface and has been widely used to estimate the yield of various food crops ( Ju et al., 2021 ; Maimaitijiang et al., 2021 ; Nagy et al., 2021 ). However, satellite remote sensing is often limited by low spatial resolution, long revisit period, and cloudy weather, which prevents image acquisition at certain time points ( Xu et al., 2021a ).…”