welcome to the 5th webinar - esa-sen2agri.org...cosmin cara, cosmin udroiu from cs-romania welcome...

5th Sen2-Agri Webinar, 19 July 2018

The webinar will last around 1h

The slides will be available on the Sen2-Agri website in the coming 24hrs(http://www.esa-sen2agri.org/)

Moderator:

Pierre Defourny from UCLouvain

Presenters:

Sophie Bontemps and Nicolas Bellemans from UCLouvain

Sen2Agri users

Members of the consortium available to answer your questions:

Cosmin Cara, Cosmin Udroiu from Cs-Romania

Welcome to the

5th webinar

http://www.esa-sen2agri.org/


1. Welcome

2. Information about the new version 1.8.2 of the system

3. In situ data collection: experience sharing within Sen2-Agri community

4. Summary of the different data collection techniques and important challenges

5. Next events

Webinar outline


Sen2-Agri: open source system to deliver

4 Sen2-Agri products along the season

EARLY AREA INDICATOR

EARLY AREA INDICATOR

Binary map identifying annually cultivated land at 10m updated every month

Crop type map at 10 m for the main regional

crops including irrigated/rainfed

discrimination

Vegetation status map at 10 m delivered every

week (NDVI, LAI, pheno index)

Monthly cloud free surface reflectance composite at 10-20 m

in line with the GEOGLAM core products


Sen2-Agri system

A system designed to run in an automated near real time (and off line) mode to deliver agric. products as satellite images are ingested

=> Orchestrator concept


2 operating modes

Automated mode

a) based on the Orchestrator with by-default parmeterization, automatic data download and processing until the end of the season, on-time delivery => operational scenarios

b) Processor execution on user request, with by-default parameterization

Manual mode: manual processing, independently for any processor, without installing and configuring the whole system

System can be operated through GUI, SNAP, terminal window / command line


• 716 system downloads on www.esa-sen2agri.org/

Sen2-Agri users

Ongoing supports

30 downloads

1 download

Map of Sen2-Agri system download

http://www.esa-sen2agri.org/


Sen2-Agri support

http://www.esa-sen2agri.org/resources/faq/

http://forum.esa-sen2agri.org/

> 70 active users (without staff)

http://www.esa-sen2agri.org/resources/faq/

http://forum.esa-sen2agri.org/


1. Welcome



4. Next events

Webinar outline


Easy and transparent update through an update script

Sen2-Agri: a continuously improving system !

Version 1.8.2

released on

16/07/2018

Version Release date Purpose

1.0 July 2016 First beta-version release to project demo users

1.6 June 2017 Pre-release of the system to project champion-users

->1.6.1 July 2017 First public release

1.7 November 2017 New User interface functionalities Handling of S2B together with S2A and L8Able to use local repository of L1C data (IPT, EODC)

1.8 April 2018 Pre-release of the v1.8- New User interface functionalities - New downloader (sen2agri-services application)- Pre-release of a new Vegetation indices processor (LAI-FAPAR-FCOVER-NDVI)

-> 1.8.1 May 2018 Stable release of the v1.8

-> 1.8.2 July 2018 Evolutions of the downloader and of the parameters of L2A processor


Version 1.8.2 new features

Version 1.8.2 includes two important evolutions:

1. New features introduced in the downloader (sen2agri-services application)– handling of multi-polygons site extent

– handling of long decimal precision in the geometry coordinates of the site extent

– handling of the time lag between the S2 images availability on the ESA-Scientific Data Hub and on the AWS store

2. Update of the Ground Image Processing Parameters (GIPP) files used by the atmospheric correctionprocessor (L2A)


Different scenarios

to update your system

Standard installation

Standard update+ GIPP update

Standard update! GIPP update only before new site definitionCheck http://www.esa-sen2agri.org/release-of-the-version-1-8-2/ for details

http://www.esa-sen2agri.org/release-of-the-version-1-8-2/


1. Welcome



4. Next events

Webinar outline


Sen2-Agri system : parameters settings

Area of Interest Shapefile to be uploaded

Monitoring period Start and end dates to be defined

S2 or S2 + L8 To be selected

Other parameters …

Field data as a key but challenging activity

SoS

Monitoring periodBefore the monitoring period

System initialization

EoS

Sen2-Agri system : field campaigns

Sampling design Stratification and sampling

Field visit In situ data collection – early survey

In situ data collection – mid-season survey

Data upload Field data quality control


• World Food Programme – South Sudan

• ESA PECS Project – Bulgaria

• CIMMYT – Mexico

• ICRISAT / IER – Mali

Users Experience

Sentinel-2:

WFP Approaches for In Situ Data Collection

WFP Existing System for Crop Type Mapping

Crop Type Ground Data Collection

Sentinel-2 Data Assimilation

Cloud Storage and Processing Centre

Crop Type Map

ONA Platform

Assimilation, Quality Control, Conversion

on

Crop type data collection:Uses JECAM (Joint Experiment in Crop Area Measurement) compatible guidelines complemented by UCL Geomatics (Belgium).

Basic minimum information set required:

IDCrop / No CropCrop TypeCrop/LC ClassIrrigation

Stratification: Farming Systems / Sectors, Agro-climatic.

30-50 samples per strata and crop type.

Non cropland data also collected, mostly by photo-interpretation.

Additional data collected to ease interpretation and quality control:

Development stageCrop planted previous seasonPlanting and Harvest dateFree comments (crop status, flood, weeds, trees)

Crop Type Data

Data Collection Tool: GeoODK

Widespread use of ODK in household survey in WFP, guaranteeing a shallow learning curve and instant familiarity

Simple to set up field forms, very simple to use and teach

Very few fancy features / very robust

Data Collection Tool

GeoODK-Collect selected for in-situ data collection

Used in 2 small pilots and one large pilot.

Now in use on two large scale operational deployments

Starting off…

Tap the form that you want to use.

Survey date: there is no need to edit the date. The default date is today’s.

Field Type Sequence

Field Type: Choose one of Cultivated or Fallow.

If Cultivated, you progress to the choice of crop types, both mono or mixed cropping.

For mixed cropping, the first crop should be the taller and denser crop.

Next: GPS Work

GeoLocation Save Coordinate

GeoTrace Capture

Single Point Capture Watch out for Accuracy!

Click Play

Select ModeWalk around, check your path

Save your work Add a Comment

Types of Samples

Field Work Requirements

Given the areas we operate in, the preferred option is to collect geo-referenced perimeters.

Crop Type Data


Where large fields are present and well defined, the preferred option may change to collect transect lines or single points. This requires back-office work to derive polygons from the lines or points

Crop Type Data

Captured Office Completion


Where large fields are present and well defined, the preferred option may change to collect transect lines or single points. This requires back-office work to derive polygons from the lines or points

Crop Type Data


Points and lines make for very rapid data capture, particularly in areas with contiguous large fields

Crop Type Data

Drive along track

Collect a Single Point inside each of the 4 fieldsMove to the nextIntersection and repeat

Stop at corner of 4 fields

1 2

34

5

Start

Corner

End

ONA Platform

Captured data uploaded to on-line platform (real time if feasible). Quality control and basic display and summaries)

ONA Platform

Capacity Development

Training Enumerators in Crop Type Data Capture


What is a sample?


Good Work: Footpaths do not matter, several fields included in a single sample.

Sample <> Fields

Mixed Cropping!


Good Work: Footpaths do not matter, several fields included in a single sample.

Field Campaigns

First pilots carried out with dedicated campaigns

Good control of the sampling, free to go everywhere, guaranteed coverage of all strata (irrigated, commercial, subsistence)

Expensive, though reach and implementation easy for WFP

Field Campaigns

Recommended for WFP: Piggyback on Household Surveys

No control of the sampling, focused on settlements, requires special arrangements for coverage of non-subsistence strata (irrigated, commercial)

Cost is diluted in wider data collection exercise

No need for dedicated logistical apparatus

Large number of samples can be captured

Some numbers

South Sudan FSNMS: 2 rounds / year, now 700 clusters visited

Karamoja FSMS: 2 rounds / year, this year 300 cluster visited

We recommend 2 samples per crop type present with a minimum of 6 samples

Ideally 4200 samples for South Sudan, realistically 3000

Ideally 1800 samples for Karamoja, realistically 1300

Field Campaigns

Ask us in early September!

The Future

Field Campaigns

DRONES!

For more information, please contact:

Rogerio Bonifacio

[email protected]+39 06 6513 3917

mailto:[email protected]

Testing Sentinel-2 vegetation indices for the assessment of the state of winter crops in Bulgaria (TS2AgroBG) - ESA PECS Project

Vlaamse Instelling voor Technologisch Onderzoek NV

(VITO)

Space Research and Technology Institute – Bulgarian Academy of

Sciences (SRTI-BAS)

Institute of Soil Science, Agrotechnologiesand Plant Protection "Nikola Poushkarov“

(ISSAPP )

Project start date: 1 September 2016; Duration: 24 months

Project Officer: Assoc. Prof. Dr. Petar Dimitrov, Technical Officer: Assoc. Prof. Georgi Jelev

The main objectives are:

1. To conduct series of field campaigns in a selected test site and to provide geo-database containing ground measurements of winter wheat biophysical variables;

2. To develop regression models for retrieval of different biophysical variables of winter wheat using Sentinel-2 vegetation indices applicable to winter wheat grown in Bulgaria;

3. To propose a methodology for generating assessment maps of crop condition for winter wheat grown in Bulgaria;

4. To produce crop type map at national level using classification of PROBA-V 100 m time series.

19.07.2018 Sen2Agri webinar

Field Campaigns07 – 11.11.2016 Tillering (Z20)

20 – 24.03.2017 Tillering (Z21-Z26)

24 – 28.04.2017Stem elongation (Z31-Z34)

15 – 19.05.2017 Anthesis (Z65 – Z69)

06 – 10.11.2017 Tillering (Z20-Z22)

02 – 04.04.2018 Tillering (Z29-Z30)

Date of field campaign and corresponding development stage of winter wheat

•Above Ground Biomass [g m-2]

•Leaf Area Index

•Nitrogen content [%]

•Nitrogen uptake [g m-2]

•Canopy Chlorophyll Content [g m-2]

•Fraction Vegetation Cover

•Fraction of Absorbed Photosynthetically

Active Radiation

Field check of north part of Zlatiya -2018

• 4 working days• 28 maps with crop

classification• Application for the

field check –OruxMaps 7.0.2 (Android, Google Play)

• Data post-processing• 678 fields checked in

2018. Defining the winter wheat phenophase – Knezha

Address

SRTI-BASScientific complex 1,Acad. Georgi Bonchev Str., bl. 11113 Sofia, P.O.Box 799, Bulgaria

E-mail: [email protected]: [email protected]

Bulgarian Academy of Sciences (BAS)

Space Research and Technology Institute (SRTI-BAS)

Remote Sensing and GIS Department

Field data collection campaign for

Sen2Agri validation in Mexico

Urs Schulthess

Francelino Rodrigues

Ivan Ortiz-Monasterio

Nicolas Bellemans (UCL)

July 19, 2018

Sen2Agri Webinar

Mexico: wind shield survey

Data Sets: Crop types reported by

farmers

Mexico: Major crop types

Crop (Spanish) # Ave Area (ha) Crop (English) Start End

TRIGO 2666 24.5 Wheat nov-dic 10-abr-20may

MAIZ (Otono) 340 19.7 Maize Fall 15-nov-30nov mayo-15jun

GARBANZO 261 18.1 Chickpea dic abril-may Anytime (90 day crop)

ALFALFA 128 8.9 Alfalfa Perennial

SOYA 119 18.3 Soybean mayo-10jun 15 sep-15oct

FRIJOL 113 18.4 Beans Regular 20-sep-15oct enero/febrero Anytime (90 day crop)

CARTAMO 106 11.5 Safflower dic jun

MAIZ (Prim.) 89 17.6 Maize Spring 15feb-30marzo agosto-sep

MAIZ (Ver.) 49 20.9 Maize Summer junio octubre

NARANJA 38 24.0 Citrus Perennial

TOMATILLO 37 8.5 Tomato nov/dic may Anytime (90 day crop)

PAPA 36 40.5 Potato oct enero-feb

CHILE 35 20.3 Chili nov/dec junio Anytime (90 day crop)

FRIJOL (Prim.) 32 23.2 Bean Spring enero/febrero mayo Anytime (90 day crop)

ESPARRAGO 24 34.2 Asparagus Perennial

NOGAL 19 39.3 Walnut Perennial

TOMATE 18 17.2 Tomato nov-enero junio Anytime (90 day crop)

ZACATE 15 18.0 Forage Perennial

CALABAZA 14 15.3 Pumpkin octubre -enero 30-Apr Anytime (90 day crop)

CEBOLLA 13 9.2 Onion 15 ago- 15 dec 30-Apr Anytime (90 day crop)

BROCOLI 11 22.4 Broccoli octubre-enero 30-Apr Anytime (90 day crop)

LECHUGA 9 31.7 Salad octubre-enero 15-May Anytime (90 day crop)

Perennial

Perennial

Perennial

Perennial

Perennial

Farmer reported crop type by

block and parcelMY_ID Block Lote Area(ha) Crop

5963 1006 1 20.0 ALFALFA

5952 1006 3 12.0 MAIZ (OtoÃ±o)

5933 1006 4 13.9 TRIGO

5967 1006 6 20.0 TRIGO

5965 1006 8 7.0 MAIZ (OtoÃ±o)

5938 1006 9 7.0 TRIGO

5956 1006 10 7.0 TRIGO

5956 1006 10 1.0 ZACATE

5955 1006 11 1.6 TRIGO

5955 1006 11 2.9 TRIGO

5955 1006 11 21.1 TRIGO

5955 1006 11 4.5 TRIGO

5953 1006 14 1.5 TRIGO

5953 1006 14 21.5 TRIGO

5941 1006 16 7.0 TRIGO

5942 1006 17 10.0 TRIGO

5942 1006 17 10.0 TRIGO

5942 1006 17 7.5 TRIGO

5972 1006 20 2.5 TRIGO

Research questions

1) What is the optimal size of the calibration data set? I.e. how sample size affects the mapping

accuracy? How the relative proportion of classes inside of the calibration dataset affects the map

accuracy?

2) How does the the accuracy change over the season? i.e., how early can we detect wheat (or any

other major crop within wheat season)?

3) Does road side data collection cause an over-fitting? What is its impact on the mapping accuracy?

4) Can we predict 2018 crop types (at least, wheat/no wheat) using a model trained with 2017

dataset?

5) How sensitive is the random forest classifier to errors in the in situ data set (specifically when

grouping in non wheat for all the other crops than wheat)? How does the robustness of RF

compare to other algorithms (incl., deep learning)?

Thank you

for your

interest!

3rd Sen2Agri User Workshop - Rome 28-29 June 2017

Sen2AgriField data collection campaign, 2017

Mali

Souleymane S. TRAORE


Mali – Sen2Agri 2016• Country size: 1 241 238 km2

•Population: 17 990 000 (WB, 2016)


• The region covers 12% of

Malian geographical territory

and around 51% of country

population (RGPH, 2009)

• Major agricultural production

region in Mali - ~50% National

crop production (CMDT-OHVN, 2013)

• Main crops: Cotton, Sorghum,

Millet, Maize, Rice

Sen2Agri 2017 – area covered

Malian cotton belt150 000km²


Survey using JECAM protocol

• Windshield survey using motor

bike - 3 teams

• Survey on 28 villages randomly

selected in the area

• Smart phone app “GeoODK”

for data capturing

• GeoTrace along field boundary

and GeoPoint in the field


• 6155 GeoTraces and 2897 GeoPoints collected in 2 weeks field work

• Rice and groundnut represent respectively 32% 15% of GeoPoint

• From the 9052 samples, 6005 field were extracted for the analysis

Samples collected

GeoTraces GeoPoints


Preliminary results

Non cropCrop


Aw ni ce…..

Thank you….


In situ data collection for Sen2Agri system

1. Farmers declaration for their fields

2. Existing surveys for ag. statistics or insurance purpose

3. Piggyback on existing household surveys

4. Specific field campaign for Sen2Agri


In situ data collection for Sen2Agri system

In situ data collection tool : - ground survey apps : GeoODK (WFP, Mali), ESRI app, OruxMaps 7.0.2., … from motorbike, car or in the field

- drones (STARS, WFP)- very light aircrafts (South Africa)

IT platform to compile and quality control the in situ data : - ONA Platform (WFP)- Google Earth or Bing combined with in season Sentinel-2 imageryfor quality control (Mali)


To capture the entire diversity of the cropland and all the non cropland classes

To cover the full diversity of the crop types of interest and all the minor crop types (it isimportant to define the targeted crop types to emphasize the field data collection on themand to capture all the gradients in crop growing conditions of the crop types of interest)

To integrate the control the quality of the in situ data (labeling error, location error, editingerror, etc. ) as the in data quality is of paramount importance

To ensure the spatial distribution of the in situ data across the whole study area to avoid a local overfitting of the classification model to specific growing conditions

To use spatially independent in situ data for robust accuracy assessment (closer the calibration and validation data set are in space, more the accuracy metrics represent the quality of the classification model rather than the quality of the entire map); Sen2Agri system delivers accuracy metrics for the entire map when the in situ covers the wholeregion of interest

Challenges for in situ data collection


In situ data collection

2 different objectives

In situ data for calibration (training): sampling to cover the diversity of situationsexisting in the study site (possibly the national territory) in order to represent the rangeof possible signatures for the different elements of interest (i.e. croplands vs noncroplands on one hand and, the five main crop types and the other frequent crops onthe other hand).

In situ data for validation to estimate the products accuracy (with a confidence interval)using a statistically-sound sampling to be objective and independent; for logisticreasons, sampling not strictly random but 2-stage sampling (with PSU and ESU) toassess the crop types (one field campaign)

Calibration and validation field campaigns for cropland and crop type can be allcombined but the sampling design should be explicitly different to beindependent.

Sometimes, 2 field campaigns are needed for early and end of season maps when all

crop types can not be identified during the mid-season campaign (before some crop emergence).


JECAM Guidelines - http://www.jecam.org/


1. Stratification according to existing agro-ecological zoning to sample the range of diversity

2. On screen visual interpretation to select samples (min. 1 ha) of land cover types different than cropland

3. Ground survey to delineate crop type samples (min. 1 ha but larger is better)

Calibration – 3 steps


Calibration

➢ Stratification according to existing agro-ecological zoning to sample the range of diversity ex. Ukraine: 4 zones



2. On screen visual interpretation to select samples (min. 1 ha) of land cover types different than cropland on recent aerial photographs, Google Earth or Bing imagery to capture the diversity of the non cropland land cover types. The sample distribution between strata could also consider the stratum size and their respective diversity (~ 15 samples by land cover type by stratum)

ex. Ukraine : 720 samples for non-cropland (15 samples x 12 land cover types x 4 strata)=> 5 days in office

3. Ground survey to delineate crop type samples (min. 1 ha but larger is better)



Selection of calibration polygons for each of

non cropland class on Google earth

Check the image date (recent!)



2. On screen visual interpretation to select samples (min. 1 ha) of land cover types different than cropland on recent aerial photographs, Google Earth or Bing imagery to capture the diversity of the non cropland land cover types. The sample distribution between strata could also consider the stratum size and their respective diversity (~ 15 samples by land cover type by stratum)

ex. Ukraine : 720 samples for non-cropland (15 samples x 12 land cover types x 4 strata)=> 5 days in office

3. Ground survey to delineate crop type samples (min. 1 ha but larger is better): for each stratum, 75-100 samples for each main crop and 20-30 samples for each minor crop. No strict sampling design but need to capture each crop diversity. Visual delineation could use the most recent color composite. A this time of the year, summer crops are not yet visible.

ex. Ukraine: 2000 samples for major crops (5 major crop types x 100 samples x 4 strata)840 samples for minor crops (7 minor crop types x 30 samples x 4 strata)

=> 10 days before the mid-season



National demo in Ukraine

Windshield surveyIn-situ data set – 7689 parcels75% for algorithm calibration25% for products validation

4+3 days, 1 car, 2 persons/car2 weeks/campaings, including days in the office


1. Stratification

2. On screen interpretation of non cropland samples on GE imagery

3. Ground survey of 75-100 / 20-30 samples per crop and stratum

CROPLAND MAP

CROP TYPE MAPCalibration set made of non

cropland (on screen) and cropland (ground survey) samplesto train each stratum separatelyand produce the first cropland

mask at the mid-season

Calibration set made of croptypes (ground survey) to

train each stratum separatelyand produce the first crop

type map at the mid-season

=> 1st field campaign data delivery before the mid-season (calibration only)

Calibration – 3 steps for 2 products


➢ Stratification according to existing agro-ecological zoning to sample the range of diversity

➢ Two-stage sampling strategy for crop type validation

❖ Delineation of large Primary Sampling Units (PSU) based on ancillary data set (typically admin. regions) ex. : Ukraine = around 30 oblasts (districts); local site = 5 to 15 districts

❖ Random selection of few (2 or 3) PSU distributed in the different strata according to their cropland area (cropland area-weighted sampling probability).

❖ “Windshield survey” for each selected PSU to identify the crop type for each Elementary Sampling Unit (ESU) along the roads (e.g. tablet onboard of vehicle). ESU corresponds to parcels covered by the same crop/crop association. Delineation should use the best contrasted color composite for parcel identification. The road selection and the ESU selection should be as systematic as possible, unbiased with a min. parcel size larger than 0,25 ha (25 S2 pixels). Typical density of 1 ESU / 100 sq km.

National case. Ukraine : 2 oblasts/stratum * 4 strata => 8 oblasts => 1600 crop samples

1 day to reach the selected oblast (PSU), 200 ESUs/day by windshield survey => 16 days campaign

Local case. 1 district/stratum * 3 strata => 3 districts => 600 crop samples200 ESUs/day by windshield survey => 3-4 days campaign

➢ Sampling strategy for non-cropland validation (2 options)

1) Collection of non cropland ESU during the windshield survey2) If up-to-date very high resolution imagery (GE) is available for large parts of the area of interest, on screen

identification of cropland/non-cropland samples of randomly selected in each stratum. Typically 100 – 150 ESUs of 0,25 ha per stratum. => 3 days in the office

Validation

JECAM Guidelines


1. Welcome



4. Next webinar: 13 September 2018

Webinar outline

welcome to the 5th webinar - esa-sen2agri.org...cosmin cara, cosmin udroiu from cs-romania welcome...

Documents