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FEGAFondo Español de Garantía Agraria
An overview of the use of Sentinel 2 in the CwRS Spanish
program – LPIS update
martes, 6 de junio de 2017
FEGA
Index
I. Current use of Sentinel - 2 in the CwRS in Spain.
II. Use of Sentinel - 2 for classification of land cover uses and crop
types. Case study in Zaragoza (Northwest of Spain)
2016. Classification of arable crops in irrigation.
2017. Evaluation of Sentinel 2 potential capabilities in the
control of diversification (greening payment)
III. Use of Sentinel 2 for updating LPIS land cover uses. Case study
in Murcia (Southeast of Spain)
FEGA
I. Current use of Sentinel - 2 in the CwRS in Spain
FEGA
Control with Remote Sensing in Spain
10 11 12 1 2 3 4 5 6 7 8 9
10
XS1 XS2 XS3
Performance of winter crops
Performance of summer crops
XS0
XS0 XS1 XS3XS2_VHR
XSa XSa
Satellite image selection’s dates are scheduled
according to the phenologic cycle of the crops
•Sowing of winter crops (XS0)
•Beginning of the growth of winter crops
(XS1)
•Maximum development of winter crops
(XS2)
•Maximum development of summer
crops (XS3)
•Since 2016, an additional image has
been included in order to control
diversification (XSa).XSa XSa
2016 CwRS zones
FEGA
CwRS 2016 – Images selected in one of the control zones (Zaragoza) and derived products used in photointerpretation
XS0 DMC 21/01/2016XS1 Spot 6
13/03/201613 XS2 WV2 29/04/2016XS3 Spot 6
02/07/2016
NDVI composite
2F (X1, X2, X3)Crop group
classification
Crop type
classification
Derived RS
products
FEGA
Photo interpretation processThe photo interpretation process comprises all
the activities carried out until the right
assignment of areas and uses to the declared
parcels.Aid application
Declared land use
Imagery
Photo interpretation procedure
Photo interpretation
of the Parcel
Assignment of
Photo interpretation
codes
Photo interpreted area
LPIS data
TR
TR
IM
Remote sensing products used for CAPI
FEGA
Integration of Sentinel 2 in the 2017 CwRS (Spain)
• Autumn imagery (XS0) acquired for all the 23 zones have been Sentinel
2A images with a scheduled window of acquisition from 15/11/2016 to
31/01/2017 65% success rate.
• However, because of the 10 day revisit period of Sentinel 2A, a window
extension was needed for some sites and the autumn images were
actually taken in the period 03/11/2016 - 19/02/2017 Period of
extension: 2-5 days, with a maximum of 12 days, in one case.
• Validated images (full cover & minimum cloud cover): 23 (100%). In one
site was necessary to make a compound of two different dates in order
to have a free cloud image over the site.
FEGA
II. Use of Sentinel 2 for classification of land cover
uses and crop types. Case study in Zaragoza
(Northwest of Spain):
- 2016. Classification of arable crops in irrigation.
- 2017. Evaluation of Sentinel 2 potential
capabilities in the control of diversification
(greening payment)
This work is supported by proDataMarket project (H2020 644497).
FEGA
2016. Case study in Zaragoza (Northwest of Spain)
Use of Sentinel 2 for classification of arable
crops in irrigation
This work is supported by proDataMarket project (H2020 644497).
FEGA
2016 - Case study in Zaragoza. Use of Sentinel 2 for identification of arable crops in irrigation
• Case study area TAEJ, 2016
CwRS site (1.600 Km2), province
of Zaragoza.
• It is located in the River Ebro
basin, one of the most important
irrigated areas in Spain.
• The main crops are wheat, barley
and alfalfa.
• Evaluation/ Validation using
ground truth survey.
Goal: Generation of an irrigated crop layer based on Sentinel 2 temporal series of
images and reference ancillary data from the Ministry (National Irrigation Plan)
and FEGA (LPIS).
FEGA
LPIS
Ground
Truth survey
Herb. Spring 81.75 %
Herb. Summer 89.57 %
Herb. Spring-Summer 90.69 %
Monthly NDVI
series
11
Validation
User Accuracy
HA= High Activity
LA = Low Activity
Automated
Classfication
based on
Decision
Tree
Geoprocessing
Geoprocessing
2016 - Case study in Zaragoza. Use of Sentinel 2 for identification of arable crops in irrigation
FEGA
2017. Evaluation of Sentinel 2 potential capabilities in the
control of diversification (greening payment)
This work is supported by proDataMarket project (H2020 644497).
FEGA
Campaign Number of
zones
Declared
reference
parcels
Number of
Rapid Field
Visits
Percentage
of Rapid
Field Visits
2011 18 401.735 31.903 7.94%
2012 22 400.266 42.516 10.62%
2013 17 357.006 40.287 11.28%
2014 18 359.462 36.312 10.10%
2015 18 325.043 51.846 15.95%
2016 18 336.128 60.069 17.87%
2017. Evaluation of Sentinel 2 potential capabilities in the control of diversification (greening payment)
CwRS - Increase of RFV due to the diversification control
Goal: Evaluate how Sentinel 2 spectral bands can contribute to improve
photointerpretation´s products and vegetation´s indices in the control of diversification
and, consequently reduce the number of necessary Rapid Field Visits
FEGA
Scenario 1: The same number of
images used in the 2016 CwRS are
kept, but:
• All the HR and HHR images are
replaced by Sentinel images,
maintaining only the VHR image.
• New band combinations for
photointerpretation are considered,
testing Se2 high spectral resolution
for crop classification.
Sentinel2 TAEJ (Zaragoza) 04/05/2016 RGB composites:
RGB: 11,5,2
SWIR, RE1, B
RGB: 8,4,3
NIR,R,G
(CwRS PHI)
RGB: 8a,6,5
NIR,RE2,RE1
RGB: 4,3,2
(Real color)
2016 CwRS TEST
XS0 (HR) Sentinel 2
XS1 (HHR) Sentinel 2
XS2 (VHR) XS2 (VHR)
Xsa (HHR)
XS3 (HHR)
Sentinel 2
Sentinel 2
2017. Evaluation of Sentinel 2 potential capabilities in the control of diversification (greening payment)
Aids
applications
FEGA
Scenario 2:
The discrimination of some crops, such as wheat and
corn, is evaluated by testing different spectral bands of
Sentinel 2.
More Sentinel 2 dates and bands are added to the
classification to analyze the time series capabilities for
crop diversification control.
In particular, the spectral evolution of wheat and barley
for several band combinations within a Se2A time series
is tested in the site of TAEJ – Zaragoza (CwRS2 2016)
2017. Evaluation of Sentinel 2 potential capabilities in the control of diversification (greening payment)
FEGA
2017. Evaluation of Sentinel 2 potential capabilities in the control of diversification (greening payment)
This graph shows the NDVI evolution of the two crops (index
produced using the Red (4) and NIR (8) bands, which are
common in several earth observation satellites.
Notice that both crops follow a similar pattern, showing a very
similar spectral response with not significant differences.
FEGA
.
2017. Evaluation of Sentinel 2 potential capabilities in the control of diversification (greening payment)
This chart shows the reflectivity values of wheat and barley in the
Red Edge Se2 band 6 (exclusive band of Se2).
A certain spectral reflectivity difference is appreciated, especially in
the first half of May.
FEGA
2017. Evaluation of Sentinel 2 potential capabilities in the control of diversification (greening payment)
This chart depicts the evolution of the two crops, wheat and barley,
in the NIR Edge Se2 band 7 (exclusive band of Se2).
Reflectivity values for wheat remain high between April and late
May, while barley values begin to fall from early April.
FEGA
III. Use of Sentinel 2 for updating LPIS land cover uses.
Case study in Murcia (Southeast of Spain)
FEGA
2016. Potential of Sentinel2 for identification of LPIS land cover uses & change detection layer: Case study in Murcia
LPISLPIS group of
uses
Surface
(ha)%
Woody crops 172,046 29
Herbaceous crops 98,753 17
Forest 81,660 14
Grassland 178,573 30
Non-productive 51,415 9
Water 8,698 1
Total 591,147
Goal: Generation of a layer with the main LPIS uses (land cover), using machine learning
classification, and producing a LPIS changing layer.
Location. Sentinel tile (100 km x 100 km) in Murcia.
Murcia has a Mediterranean climate of semi-arid type. The average annual
temperature is 18ºC and the precipitation is abut 300-350 mm per year.
FEGA
Methodology for LPIS Main Uses Layer and LPIS Change Map
LPIS data
Sentinel2 8-band composites
from 6 dates of 2016
Vegetation Height Map from
LiDAR
Slope map (from DEM)
Agroclimatic classification map
Main LPIS uses layer
(2016)
Farmers aid applications
National Forest Map
Sample data
Classification data
LPIS Change Map
(2015-2016)
LPIS before updating
(2015)
Decision
Tree Algorithm
Classification
model
Criteria for class
identification:Global Accy: >80%
Kappa>75%
Machine Learning
Classification Process
Change 7%
Uncertainty 13%
Not change 80%
Geoprocessing
Support decision making
FEGA
Validation of LPIS Change MapLPIS 2015
Main LPIS uses
layer (2016)
Difference LPIS
2015-2016
(Validation Layer)
Ge
op
rocessin
g
Geoprocessing
LP = low probability
LPIS change
map
2015-2016
Layer of discrepancies
Available
Ortophoto
Based on
2016 fligh
Changes between
groups of uses
Omission Err : 0,1 %
Commission Err: 7%
FEGA
Detailed agricultural land use classification map
Agricultural area
in the Main LPIS
uses layer (2016)
User Accuracy:
Decision
Tree Algorithm
Classification
model
Criteria for class
identification:Global Accy: >80%
Kappa>70%
Detailed land use
classification map (2016)
Machine Learning
Classification ProcessCereal 85.5%
Citrus 94.7%
Forest 89.3.%
Fruit trees 91.6%
Grassland 86.2%
Nuts 82.6%
Other woody crops 76.9%
Urban 94.9%
Water 100%
FEGA
STRENGTHS
• Higher spatial, spectral and radiometric resolution than other HR/MR imagery
• Open (free) data policy
• Useful as back-up imagery 2017 RS zone in PADU (Granada) as XS1
WEAKNESSES
• Less spatial resolution than other HR imagery (Spot 6/7)
• Revisit time of 10 days is insufficient for the requirements of the CwRS. Once
Sentinel 2B is operative, it will be reduced to 5 days.
• Orthorectification of imagery, though valid, in some areas has worse accuracy
than desired. The situation is being improved by ESA.
• The growing number of images available makes the processing and
exploitation of the data for photointerpretation more complex.
Strengths and weaknesses of Se2 for the CwRS
FEGA
Conclusions
The potential use of Se2 images in relation to CwRS and LPIS updating
has been studied through the development of three case studies. They
must only be considered as pre-opeartional technical assessments. More
studies are necessary prior to operational deployment.
As a general outcome, Se2 images can be usefull for the identification and
mapping of land use covers and crop types.
The degree of contribution of SE2 images at improving crop diversification
control (greening) is still under evaluation, although the first impressions
are promising.
Sentinel2 provides a big amount of high quality RS data with a great
potential and many different applications in IACS. However, it is necessary
to do further computing developments and carry out some more pilot
projects.
More powerful computing resources are required to process and manage
the large volume of data involved in the Sentinel2 time series.
FEGAFondo Español de Garantía Agraria
THANK YOU!!