Satellite Image Time Series (SITS), such as the ones acquired by the new Sentinel-2 (S2), 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. The characteristics of S2 SITS become more relevant in the agricultural analysis, where availability of continuous and/or dense SITS is important to map and analyze the crops dynamics. So far, agriculture applications have been limited by the spatial and temporal resolutions to monthly or yearly analysis over large regions and homogeneous crops. Whereas, the high spatial resolution offered by S2 allows to separate and analyze single crop fields, even when their size is relatively small (few hectares), with a high temporal resolution. In the literature only few techniques exist that deal with the detection and analysis of crop fields at single field level. Thus, this paper proposes an approach to dynamic crop field mapping in S2 SITS. To this aim, spectral, spatial and temporal information are used. The high variability of the crop cycle is analyzed in time to extract spectral and spatial information for crop-field detection. Connected-component labeling is used to label and analyze each crop field independently. Experimental results obtained for S2 SITS acquired over Barrax, Spain, confirm the effectiveness of the proposed approach.
Spatio-temporal evolution of crop fields in Sentinel-2 Satellite Image Time Series
Solano Correa, Yady Tatiana;Bovolo, Francesca;
2017-01-01
Abstract
Satellite Image Time Series (SITS), such as the ones acquired by the new Sentinel-2 (S2), 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. The characteristics of S2 SITS become more relevant in the agricultural analysis, where availability of continuous and/or dense SITS is important to map and analyze the crops dynamics. So far, agriculture applications have been limited by the spatial and temporal resolutions to monthly or yearly analysis over large regions and homogeneous crops. Whereas, the high spatial resolution offered by S2 allows to separate and analyze single crop fields, even when their size is relatively small (few hectares), with a high temporal resolution. In the literature only few techniques exist that deal with the detection and analysis of crop fields at single field level. Thus, this paper proposes an approach to dynamic crop field mapping in S2 SITS. To this aim, spectral, spatial and temporal information are used. The high variability of the crop cycle is analyzed in time to extract spectral and spatial information for crop-field detection. Connected-component labeling is used to label and analyze each crop field independently. Experimental results obtained for S2 SITS acquired over Barrax, Spain, confirm the effectiveness of the proposed approach.I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.
