Journal
AGRICULTURE-BASEL
Volume 12, Issue 10, Pages -Publisher
MDPI
DOI: 10.3390/agriculture12101605
Keywords
crop monitoring; Sentinel-1; Sentinel-2; time-series; Google Earth Engine; Temporal Normalized Phenology Index (TNPI)
Categories
Ask authors/readers for more resources
Crop monitoring is crucial for sustainable agriculture, and the Sentinel-1 and Sentinel-2 missions provide open imagery that can be used to deduce the phenological stages of crops such as wheat. This research evaluated the temporal profiles derived from Sentinel-1 and Sentinel-2 data to determine the dates of wheat's phenological stages and assessed the relationship between these stages and remote sensing indices. The study also successfully mapped the spatial distribution of wheat in a specific region using a fusion of Sentinel-1 and Sentinel-2 images.
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 TNPIincrease and TNPIdecrease revealed that the declining part of the NDVI profile from NDVIMax, to NDVIMin2 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.
Authors
I am an author on this paper
Click your name to claim this paper and add it to your profile.
Reviews
Recommended
No Data Available