Linking Grape Yield Performance to Remote Sensing Vegetation Indices: A Fusion of Sentinel Radar and Optical Imagery with Machine Learning Models

ماشین های کشاورزی

Articles in Press, Accepted Manuscript, Available Online from 28 July 2025   

Document Type: Research Article

DOI: 10.22067/jam.2025.91357.1325

Authors

H. R. Maleki¹; M. Khodamorad Pour^{* 2}; H. Mohammadi Monavar¹

¹Department of Bio-System Engineering, Faculty of Agriculture, Bu-Ali Sina University, Hamedan, Iran

²Department of Water Science and Engineering, Faculty of Agriculture, Bu-Ali Sina University, Hamedan, Iran

Abstract

Introduction
Remote sensing is considered a key management tool in precision agriculture, particularly for monitoring and identifying plant coverage. Grapes are among the most valuable horticultural crops, with Hamedan province accounting for approximately 7.3 % of Iran's total vineyard area. This study evaluates the accuracy of vineyard identification in Hamedan province using machine learning algorithms including support vector machine (SVM), minimum distance (MD), and random forest (RF) models along with normalized difference vegetation index (NDVI) and normalized difference water index (NDWI) estimated from combined optical and radar images of Sentinel (Sentinel-1 and Sentinel-2). Based on the most accurate vineyard identification map, the maximum time series of NDVI and NDWI of the MODIS satellite in vineyards was estimated between 2007 and 2020, and their correlation with the actual yield of the grape crop was examined.
Materials and Methods
In this research, we first extracted images of pivotal remote sensing vegetation indicators, including NDVI and NDWI, from Sentinel-2 images in Hamedan province in 2020. The approach of addressing speckle noise through median pixels allowed for the acquisition of median radar images from Sentinel-1 over the designated study area. To create high-accurate images, spectral composition was used to combine these images with the NDVI and NDWI from Sentinel-2 images. Using these images, vineyard identification maps were generated through classification algorithms, including support vector machine, random forest, and minimum distance models. Training samples were used to train these algorithms. Samples from six land coverage classes involving vineyards, were collected using a combination of field observations and Google Earth imagery. Of these, 70% were used for training and 30% for testing the classification models. In order to assess the accuracy of the vineyard identification maps, indicators including overall accuracy and kappa coefficient were examined. Subsequently, the vineyard map with the highest assessment indicator was selected. Finally, using this accurate vineyard identification map, the maximum monthly NDVI and NDWI indices estimated from MODIS sensor images in the vineyards were calculated from 2007 to 2020, and their correlation with yields of the grape crop was computed using Pearson correlation.
Results and Discussion
Based on the comparison of different classification algorithms for distinguishing vineyards, random forest model along with NDVI and NDWI indices outperformed support vector machine and minimum distance models. With regard to accuracy, however, the random forest along with the NDWI has the best overall accuracy (95%) and kappa coefficient (0.95). The superior performance of NDWI is attributed to the high moisture levels in vineyards resulting from irrigation, as NDWI is particularly sensitive to variations in vegetation water content. The lower accuracy of vineyard identification using SVM and MD models can be linked to shadow effects caused by the canopy structure of grapevines, as well as imbalanced training data used for the support vector machine model. Correlation analysis of real grape yields with NDVI and NDWI of MODIS extracted from the highest accuracy vineyards map indicates NDVI (correlation coefficient 0.81) has a stronger linear relationship with yield than NDWI (correlation coefficient 0.75). This can be explained by NDVI's sensitivity to leaf chlorophyll changes, which results in a strong correlation with yield.
Conclusion
Vineyards can be accurately identified using machine learning algorithms and remote sensing vegetation indices derived from combined radar and optical satellite images. Furthermore, the strong correlation between NDVI and grape yield enable reliable yield prediction based on NDVI time series analysis. The outcomes of this study facilitate the identification of grape cultivation areas, improved water resourse management, the development of optimized irrigation strategies, pre-harvest yield estimation, and the exploration of export options.

Keywords

Minimum Distance model; NDVI index; NDWI index; Random Forest model; Support Vector Machine model

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