Investigating Sentinel-1 and Sentinel-2 Data Efficiency in Studying the Temporal Behavior of Wheat Phenological Stages Using Google Earth Engine

Hajar Saad El Imanni (), Abderrazak El Harti and Jonathan Panimboza
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Hajar Saad El Imanni: Team of Remote Sensing and GIS Applied to the Geosciences and the Environment, Faculty of Sciences and Techniques, Beni Mellal 23000, Morocco
Abderrazak El Harti: Team of Remote Sensing and GIS Applied to the Geosciences and the Environment, Faculty of Sciences and Techniques, Beni Mellal 23000, Morocco
Jonathan Panimboza: Department of Earth and Building Sciences, University of the Armed Forces, ESPE, Sangolqui 171103, Ecuador

Agriculture, 2022, vol. 12, issue 10, 1-27

Abstract: Crop monitoring is critical for sustaining agriculture, preserving natural resources, and dealing with the effects of population growth and climate change. The Sentinel missions, Sentinel-1 and Sentinel-2, provide open imagery at a high spatial and temporal resolution. This research aimed (1) to evaluate the temporal profiles derived from Sentinel-1 and Sentinel-2 time series data in deducing the dates of the phenological stages of wheat from germination to the fully mature plant using the Google Earth Engine (GEE) JavaScript interface and (2) to assess the relationship between phenological stages and optical/ SAR remote sensing indices for developing an accurate phenology estimation model of wheat and extrapolate it to the regional scale. Firstly, the temporal profiles derived from Sentinel-1 and Sentinel-2 remote sensing indices were evaluated in terms of deducing the dates of the phenological stages of wheat. Secondly, the remote sensing indices were used to assess their relationship with phenological stages using the linear regression (LR) technique. Thirdly, the best performing optical and radar remote sensing indices were selected for phenological stage prediction. Fourthly, the spatial distribution of wheat in the TIP region was mapped by performing a Random Forest (RF) classification of the fusion of Sentinel-1 and Sentinel 2 images, with an overall accuracy of 95.02%. These results were used to characterize the growth of wheat on the TIP regional scale using the Temporal Normalized Phenology Index (TNPI) and the predicted models. The obtained results revealed that (1) the temporal profiles of the dense time series of Sentinel-1 and Sentinel-2 indices allowed the dates of the germination, tillering, jointing heading, maturity, and harvesting stages to be determined with the support of the crop calendar. (2) The TNPI increase and TNPI decrease revealed that the declining part of the NDVI profile from NDVI Max , to NDVI Min2 revealed higher TNPI values (from 0.58 to 1) than the rising part (from 0.08 to 0.58). (3) The most accurate models for predicting phenological stages were generated from the WDVI and VH–VV remote sensing indices, having an R 2 equal to 0.70 from germination to jointing and an R 2 equal to 0.84 from heading to maturity.

Keywords: crop monitoring; Sentinel-1; Sentinel-2; time-series; Google Earth Engine; Temporal Normalized Phenology Index (TNPI) (search for similar items in EconPapers)
JEL-codes: Q1 Q10 Q11 Q12 Q13 Q14 Q15 Q16 Q17 Q18 (search for similar items in EconPapers)
Date: 2022
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