TY - JOUR
T1 - Irrigation mapping using Sentinel-1 time series at field scale
AU - Gao, Qi
AU - Zribi, Mehrez
AU - Escorihuela, Maria Jose
AU - Baghdadi, Nicolas
AU - Segui, Pere Quintana
N1 - Funding Information:
The authors wish to thank the technical teams for their support and the SIGPAC team, which provides plenty of ground information. Qi Gao received grant DI-15-08105 from the Spanish Education Ministry (MICINN) and DI-2016-078 from the Catalan Agency of Research (AGAUR). The study was partially funded by the REC project funded by the European Commission Horizon 2020 Programme for Research and Innovation (H2020) in the context of the Marie Sklodowska-Curie Research and Innovation Staff Exchange (RISE) action under grant agreement No. 645642.
Publisher Copyright:
© 2018 by the authors.
PY - 2018/9/1
Y1 - 2018/9/1
N2 - The recently launched Sentinel-1 satellite with a Synthetic Aperture Radar (SAR) sensor onboard offers a powerful tool for irrigation monitoring under various weather conditions, with high spatial and temporal resolution. This research discusses the potential of different metrics calculated from the Sentinel-1 time series for mapping irrigated fields. A methodology for irrigation mapping using SAR data is proposed. The study is performed using VV (vertical-vertical) and VH (vertical-horizontal) polarizations over an agricultural site in Urgell, Catalunya (Spain). With field segmentation information from SIGPAC (the Geographic Information System for Agricultural Parcels), the backscatter intensities are averaged within each field. From the Sentinel-1 time series for each field, the statistics and metrics, including the mean value, the variance of the signal, the correlation length, and the fractal dimension, are analyzed. With the Support Vector Machine (SVM), the classification of irrigated crops, irrigated trees, and non-irrigated fields is performed with the metrics vector. The results derived from the SVM are validated with ground truthing from SIGPAC over the whole study area, with a good overall accuracy of 81.08%. Random Forest (RF) machine classification is also tested in this study, which gives an accuracy of around 82.2% when setting the tree depth at three. The methodology is based only on SAR data, which makes it applicable to all areas, even with frequent cloud cover, but this method may be less robust when irrigation is less dominated to soil moisture change.
AB - The recently launched Sentinel-1 satellite with a Synthetic Aperture Radar (SAR) sensor onboard offers a powerful tool for irrigation monitoring under various weather conditions, with high spatial and temporal resolution. This research discusses the potential of different metrics calculated from the Sentinel-1 time series for mapping irrigated fields. A methodology for irrigation mapping using SAR data is proposed. The study is performed using VV (vertical-vertical) and VH (vertical-horizontal) polarizations over an agricultural site in Urgell, Catalunya (Spain). With field segmentation information from SIGPAC (the Geographic Information System for Agricultural Parcels), the backscatter intensities are averaged within each field. From the Sentinel-1 time series for each field, the statistics and metrics, including the mean value, the variance of the signal, the correlation length, and the fractal dimension, are analyzed. With the Support Vector Machine (SVM), the classification of irrigated crops, irrigated trees, and non-irrigated fields is performed with the metrics vector. The results derived from the SVM are validated with ground truthing from SIGPAC over the whole study area, with a good overall accuracy of 81.08%. Random Forest (RF) machine classification is also tested in this study, which gives an accuracy of around 82.2% when setting the tree depth at three. The methodology is based only on SAR data, which makes it applicable to all areas, even with frequent cloud cover, but this method may be less robust when irrigation is less dominated to soil moisture change.
KW - Classification
KW - Irrigation
KW - SAR
KW - Sentinel-1
KW - Soil moisture
UR - http://www.scopus.com/inward/record.url?scp=85053622301&partnerID=8YFLogxK
U2 - 10.3390/rs10091495
DO - 10.3390/rs10091495
M3 - Article
AN - SCOPUS:85053622301
SN - 2072-4292
VL - 10
JO - Remote Sensing
JF - Remote Sensing
IS - 9
M1 - 1495
ER -