NDVI estimation using Sentinel-1 data over wheat fields in a semiarid Mediterranean region

Abstract

Annual crop monitoring is a key parameter for managing agricultural strategies. Several studies have relied on remote sensing products such as the normalized difference vegetation index (NDVI) as a vegetation dynamic metric. However, the dependence of optical data on weather conditions limits its availability. In this study, we reconstruct the NDVI time series of wheat fields using the moving averages of the Sentinel-1 normalized VH/VV cross-polarization ratio (IN) and the interferometric coherence in VV polarization over wheat selected fields in a semiarid site in Tunisia during two seasons, from 2018 to 2020. The crop cycle is divided into two periods: before and after the heading phase, which occurs in approximately the middle of March. Due to the volume-scattering impact, the second phase is divided into the ripening and maturation phase (NDVI ≥0.4) and senescence phase (NDVI <0.4). To estimate the NDVI values, different methods are used: curve-fitting equations and machine learning regressors such as the random forest (RF) and the support vector regressor (SVR). Low root mean square error (RMSE) values characterize NDVI estimation during the first period. In the second period, the RMSE values reach 0.06 when the NDVI is lower than 0.4. When the NDVI values exceed 0.4 in the second period, lower accuracy marks the NDVI estimation using the curve-fitting equations as a function of IN or coherence. Relative low accuracy characterizes the regression algorithms’ estimations when NDVI ≥ 0.4 compared to their performance during the aforementioned periods. The proposed approach was tested on different wheat fields. The NDVI estimations are characterized by RMSE values varying between 0.12 and 0.19. The use of RF and SVR outperformed the curve-fitting methods with an RMSE equal to 0.12. The present findings revealed the high accuracy of the proposed approach to estimate the missing values of wheat fields NDVI values during the vegetation development period until heading and the senescence phase. The presence of the mutual effect of the vegetation water content and its volume complicated the NDVI estimation using the C-band data.

Keywords

• Synthetic aperture radar
• normalized difference vegetation index
• Sentinel-1
• vegetation
• VH/VV cross-polarization ratio
• interferometric coherence