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.
Highlights
Since global trading prices of agricultural commodities depends largely on their seasonal production levels, the total size of cropping area and crop yields are important for export-import activities, agricultural agencies at national and international levels, government agencies, and other crop marketing agencies
The validation of the results revealed that the Soil Adjusted Vegetation Index (SAVI)-based model provided more accurate forecasts compared to Normalized Difference Vegetation Index (NDVI)
This study focused on developing and testing remote sensing-based technology for early season prediction of wheat yield in the Northern Great Plain region as part of a transboundary catchment area, the Tisza river basin, using multiple linear regression model and combining with Landsat 8-derived vegetation indices (NDVI, SAVI) and crop statistics data
Summary
Since global trading prices of agricultural commodities depends largely on their seasonal production levels, the total size of cropping area and crop yields are important for export-import activities, agricultural agencies at national and international levels, government agencies, and other crop marketing agencies. Remote sensing became a widespread technique used in agriculture and agronomy Atzberger [2] and the interest in using satellite remote-sensed data for crop monitoring and crop production forecasting has increased as it produces uniform data at the global scale, and modelling results can potentially be utilized in large regions. Remote sensing is capable of providing temporal (and potentially real-time) and objective data on crop vigor, density, health and productivity because remote-sensing data is in close relation to the canopy Leaf Area Index (LAI) and fAPAR (fraction of Absorbed Photosynthetically Active Radiation) [3]. In minimizing the distribution of the effects on the relationships between vegetation spectral reflectance and crop yield, some researchers refer to distance-based vegetation indices such as Soil Adjusted Vegetation Index (SAVI). SAVI is applied in correcting Normalized Difference Vegetation Index (NDVI) for influencing of soil brightness in areas where vegetative cover is low [7,8,9]
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