Estimating crop parameters using Sentinel-1 and 2 datasets and geospatial field data

Area of Science:

• Agricultural Science
• Remote Sensing
• Geospatial Analysis

Background:

• Global food security is challenged by population growth and climate change, necessitating advanced crop monitoring techniques.
• Satellite remote sensing, particularly Sentinel-1 (SAR) and Sentinel-2 (optical) data, offers high spatial and temporal resolution for large-scale crop parameter estimation.

Purpose of the Study:

• To present a comprehensive dataset of wheat and rapeseed crop parameters collected during the 2017 growing season in France.
• To facilitate improved crop monitoring, yield prediction, and agricultural modeling through the provision of field-sampled data linked to satellite observations.

Main Methods:

• Field surveys were conducted from January to July 2017, sampling phenological stages, Leaf Area Index (LAI), biomass (wet and dry), and water content in wheat and rapeseed fields.
• Data from Sentinel-2 (10 spectral bands, 12 vegetation indices) and Sentinel-1 (2 backscattering coefficients, 1 ratio, 4 polarimetric indicators) were assigned to each field sample.

Main Results:

• Phenological stages were documented for 83 wheat and 32 rapeseed fields across Brittany and Picardy.
• Detailed biophysical parameters (LAI, biomass, water content) were collected for selected wheat and rapeseed fields.

Conclusions:

• The generated dataset offers a valuable resource for validating and developing remote sensing-based crop monitoring models.
• This data can be applied to crop monitoring and modeling efforts in diverse agricultural regions beyond the study area.