Learning from Research

Map of irrigated and non-irrigated areas

The SENSAGRI irrigated and non-irrigated areas product consists of maps that derive the spatio-temporal distribution of irrigation events and irrigated and non-irrigated fields.

Page contents

Output Description

Earth observation (EO) data can effectively map irrigated croplands, offering a synoptic overview of broad areas and their extent. The product developed and implemented an automatic methodology that provides irrigated/non-irrigated maps from high-resolution Surface Soil Moisture (SSM) maps, computed from Sentinel-1 and Sentinel-2 images. The underlying assumption is that an irrigated field shows an SSM level higher than a non-irrigated field, at least for a specific period, which may range from a few days to hours. The classified product is a series of binary pixel-wise maps containing a flag class (i.e. irrigated/non-irrigated) at different points in time. The maps have a geographic latitude and longitude projection and a World Geodetic System 1984 (WGS84) datum. Irrigation detection using high-resolution SSM maps could be advantageous for early detection of irrigated areas and provide valuable information for water management associations, especially during periods of water shortage. 

The study area of the tool is the irrigation district of Riaza, Castilla y León, Spain. In this region, both winter and summer crops are irrigated with different schedules and variables for the same crop and within the same field. For example, irrigation for winter wheat, in some cases, is supplemental and applied during some phenological stages (e.g. heading), while in other cases, irrigation is reasonably continuous. In addition, there often is an essential temporal/spatial variability of soil moisture level within the single field because the irrigation pattern may not be uniform in time and space. However, the tool’s accuracy is higher than 80%. 

Relevance for monitoring and evaluation of the CAP

The proposed methodology does not depend on optical data and, therefore, can create irrigation maps irrespective of cloud cover. As such, the proposed method can complement other existing detection schemes based on optical data. The tool can monitor the progress of measures that support water savings through no water use at all. In other words, the tool can point to previously irrigated parcels that became non-irrigated because of policy support to activities that do not use irrigation, such as conversion to rain-fed cultivations, adoption of cultivars with earlier planting, setting the land aside and others. The tool can also support controls and inspections since the irrigation/non-irrigation maps provide very early detection of irrigation at the plot level for assessing farm compliance against obligations or conditionalities. 

The same maps can support an evaluation in three ways. First, they can be used, together with other data sources and other EO tools, in estimating irrigation water needs which is a proxy for the ‘water use in agriculture’ impact indicator. Second, when combined with IACS/LPIS information, they can support the evaluation of agricultural policy measures to reduce water use. Third, the irrigation/non-irrigation map can cross-validate information related to policy effects on crop irrigation and its consequent impacts on water use in agriculture. 

The tool is at the proof-of-concept stage of development and was successfully validated at the study region of Riaza in Castilla y León, Spain. A critical step of the tool’s application is the training stage, when the AI algorithms ‘learn’ to recognise an irrigated field for various crops and periods of the year. Learning depends on in situ reference data from field observations, irrigation registries or cadastral maintained by irrigation water associations. Therefore, transferring and validating the tool to other regions and Member States is feasible. 

Relevance of the output per CAP Objectives

  • Specific Objective 4 - Climate change action
  • Specific Objective 5 - Environmental care

Additional output information

Data collection systems used:

  • IACS/LPIS
  • Copernicus

Type of output:

  • New / improved data for M&E
  • Visualisation tools

Associated evaluation approaches:

  • Desk research
  • Data analysis
  • Impact evaluation ongoing

Spatial scale:

  • Parcel
  • Farm holding
  • Sub-regional / local
  • Regional

Project information

Sensangri Logo

Sentinels Synergy for Agriculture 

SENSAGRI aims to exploit the unprecedented capacity of Sentinel-1 and Sentinel-2 satellites and develop an innovative portfolio of prototype agricultural monitoring services. 

SENSAGRI’s goals are: 

  • To combine the Copernicus Sentinel-1 radar with Sentinel-2 optical measurements and in-situ data to develop new applications and market opportunities for the European agricultural sector. 
  • Develop prototype Copernicus services of Surface Soil Moisture (SSM), green and brown Leaf Area Indices (LAI) and seasonal crop type mapping and use those for proof-of-concept services of advanced agricultural monitoring products. 
  • Validate delivered services and establish service demonstration cases to show the large application potential of the new upstream data products. 
  • Disseminate prototype and proof-of-concept services and interact services with the agricultural sector. 

Project’s timeframe: 2016 – 2019

Contacts of project holder: Antonio Ruiz-Verdú, Laboratory for Earth Observation – Image Processing Laboratory, University of Valencia (Spain) antonio.ruiz@uv.es  

CORDIS database: https://cordis.europa.eu/project/id/730074  

Territorial coverage: France, Italy, Poland, Spain

Resources