Next Article in Journal
Mapping Paddy Fields in Japan by Using a Sentinel-1 SAR Time Series Supplemented by Sentinel-2 Images on Google Earth Engine
Previous Article in Journal
Mapping Rice Paddy Based on Machine Learning with Sentinel-2 Multi-Temporal Data: Model Comparison and Transferability
Previous Article in Special Issue
Near Real-Time Irrigation Detection at Plot Scale Using Sentinel-1 Data
Open AccessArticle

Potential for the Detection of Irrigation Events on Maize Plots Using Sentinel-1 Soil Moisture Products

1
CESBIO, Université de Toulouse, CNRS/UPS/IRD/CNES/INRAE, 18 Avenue Edouard Belin, bpi 2801, 31401 Toulouse, CEDEX 9, France
2
ITK, Cap alpha, Avenue de l’Europe, 34830 Clapiers, France
3
INRAE, TETIS, University of Montpellier, 500 rue François Breton, 34093 Montpellier, CEDEX 5, France
4
CNRM, Université de Toulouse, Meteo-France, CNRS, 31057 Toulouse, France
*
Author to whom correspondence should be addressed.
Remote Sens. 2020, 12(10), 1621; https://doi.org/10.3390/rs12101621
Received: 2 April 2020 / Revised: 13 May 2020 / Accepted: 14 May 2020 / Published: 19 May 2020
(This article belongs to the Special Issue Irrigation Mapping Using Satellite Remote Sensing)
Although the real timing and flow rates used for crop irrigation are controlled at the scale of individual plots by the irrigator, they are not generally known by the farm upper management. This information is nevertheless essential, not only to compute the water balance of irrigated plots and to schedule irrigation, but also for the management of water resources at regional scales. The aim of the present study was to detect irrigation timing using time series of surface soil moisture (SSM) derived from Sentinel-1 radar observations. The method consisted of assessing the direction of change of surface soil moisture (SSM) between observations and a water balance model, and to use thresholds to be calibrated. The performance of the approach was assessed on the F-score quantifying the accuracy of the irrigation event detections and ranging from 0 (none of the irrigation timing is correct) to 100 (perfect irrigation detection). The study focused on five irrigated and one rainfed plot of maize in South-West France, where the approach was tested using in situ measurements and surface soil moisture (SSM) maps derived from Sentinel-1 radar data. The use of in situ data showed that (1) irrigation timing was detected with a good accuracy (F-score in the range (80–83) for all plots) and (2) the optimal revisit time between two SSM observations was 2–4 days. The higher uncertainties of microwave SSM products, especially when the crop is well developed (normalized difference of vegetation index (NDVI) > 0.7), degraded the score (F-score = 69), but various possibilities of improvement were discussed. This paper opens perspectives for the irrigation detection at the plot scale over large areas and thus for the improvement of irrigation water management. View Full-Text
Keywords: sprinkler; corn; France; irrigation timing; FAO-56; surface soil moisture; SAR sprinkler; corn; France; irrigation timing; FAO-56; surface soil moisture; SAR
Show Figures

Figure 1

MDPI and ACS Style

Le Page, M.; Jarlan, L.; El Hajj, M.M.; Zribi, M.; Baghdadi, N.; Boone, A. Potential for the Detection of Irrigation Events on Maize Plots Using Sentinel-1 Soil Moisture Products. Remote Sens. 2020, 12, 1621.

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.

Article Access Map by Country/Region

1
Search more from Scilit
 
Search
Back to TopTop