Dense time sampling increases the sensitivity to dynamic phenomena and provides a better description of them. With the increased revisit period of the complete S-2 constellation (return frequency up to 5 days), it is possible to perform accurate seasonal trend analysis. In this framework, specific applications regarding low cost precision agriculture is of great interest given that S-2 has specific bands in the Red-Edge spectral range dedicated to the study of vegetation that were not available in previous multispectral sensors (e.g., Landsat, SPOT). In the time series methodological area, the focus is set on the monitoring of irrigation of agricultural areas.
Satellite Image Time Series (SITS), such as the ones acquired by Sentinel-2, combine a large amount of information, compared to previous satellite generations, since a better trade-off in terms of spatial/spectral/temporal resolutions is guaranteed. These type of information becomes relevant in the agricultural analysis, where availability of dense SITS is required to map and analyze fast changing crop behaviors. The high spatial resolution offered by Sentinel-2 allows to separate and analyze single crop fields, even when their size is relatively small (lower than 9.4ha, as it frequently happens in Europe w.r.t. 19.6ha in US), with a high temporal resolution. In the literature, several methods exist that analyze the evolution of crop fields, by aggregating them, but none is fully able to work: i) at single field level; and ii) with irregularly sampled data. In this project, we developed an approach for the analysis of spatio-temporal evolution of crop fields in SITS that is able to deal with the spatial/spectral/temporal characteristics of S2 data. Experiments were carried out from S2-SITS acquired over Barrax, Spain, in the period July 2015 – November 2016, where a total of 76 images were available, but only 49 were used for the analysis, given the non-presence of clouds.
Sentinel-2 images distribution for the period July 2015 – November 2016 in Barrax, Spain.
To this aim, four steps are followed: i) pre-processing of the S2-SITS, ii) spatio-temporal fusion, iii) spatio-temporal evolution analysis; and iv) spatio-temporal information extraction. For the spatio-temporal fusion, the system identifies and separates all the crop fields cultivated at least once over a given area based on their vegetation spatio-temporal evolution.
Normalized Difference Vegetation Index (NDVI)
Multitemporal crop field map
Barrax (Spain) agricultural area over the period from July 6th 2015 to November 20th 2016.
Example of the original and continuously reconstructed mean NDVI for a single crop field.
For the spatio-temporal evolution, the system reconstructs continuous and regular sampled S2-SITS at single crop field level by means of an adaptive non-parametric regression model. For the spatio-temporal information extraction, two types of information are considered: i) extraction of phenological parameters and ii) creation of cumulative indices maps. For the phenological parameters extraction, the trend of the mean NDVI, at single crop field level, is used to derive their phenological behavior and create maps related to beginning, middle and end of season. This same information is then used to create maps indicating information such winter/summer cultivation period.
Beginning of season
Middle of season
End of season
Phenology estimation maps over a 12 months period.
Phenology estimation for three sample crop fields: ♢ beginning of the season, ☆ middle of the season. ⚬ end of the season.
Mean cumulative NDVI and NDWI with a value higher than 0.5.
For the creation of cumulative indices maps, well-known thresholds from the literature are used as starting point over given radiometric indices in order to generate a map offering critical or relevant information at single crop field level. Such are the cases of the NDVI and NDWI indices, where a threshold over 0.5 can provide information about how many times a field has been cultivated over a given period, and how many times there was presence of water on that field.