Next Article in Journal
Detecting Building Edges from High Spatial Resolution Remote Sensing Imagery Using Richer Convolution Features Network
Next Article in Special Issue
Improving the Seasonal Representation of ASCAT Soil Moisture and Vegetation Dynamics in a Temperate Climate
Previous Article in Journal
Tracking Fine-Scale Structural Changes in Coastal Dune Morphology Using Kite Aerial Photography and Uncertainty-Assessed Structure-from-Motion Photogrammetry
Article Menu
Issue 9 (September) cover image

Export Article

Open AccessFeature PaperArticle

Irrigation Mapping Using Sentinel-1 Time Series at Field Scale

1
isardSAT, Parc Tecnològic Barcelona Activa, Carrer de Marie Curie, 8, 08042 Barcelona, Catalunya, Spain
2
CESBIO (CNRS/CNES/UPS/IRD), CEDEX 9, 31401 Toulouse, France
3
Observatori de l’Ebre (OE), Ramon Llull University, 43520 Roquetes, Spain
4
IRSTEA, University of Montpellier, UMR TETIS, CEDEX 5, 34093 Montpellier, France
*
Authors to whom correspondence should be addressed.
Remote Sens. 2018, 10(9), 1495; https://doi.org/10.3390/rs10091495
Received: 6 August 2018 / Revised: 7 September 2018 / Accepted: 15 September 2018 / Published: 18 September 2018
(This article belongs to the Special Issue Soil Moisture Retrieval using Radar Remote Sensing Sensors)
  |  
PDF [11333 KB, uploaded 18 September 2018]
  |  

Abstract

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. View Full-Text
Keywords: soil moisture; SAR; Sentinel-1; irrigation; classification soil moisture; SAR; Sentinel-1; irrigation; classification
Figures

Graphical abstract

This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited (CC BY 4.0).
SciFeed

Share & Cite This Article

MDPI and ACS Style

Gao, Q.; Zribi, M.; Escorihuela, M.J.; Baghdadi, N.; Segui, P.Q. Irrigation Mapping Using Sentinel-1 Time Series at Field Scale. Remote Sens. 2018, 10, 1495.

Show more citation formats Show less citations formats

Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.

Related Articles

Article Metrics

Article Access Statistics

1

Comments

[Return to top]
Remote Sens. EISSN 2072-4292 Published by MDPI AG, Basel, Switzerland RSS E-Mail Table of Contents Alert
Back to Top