The Use of Earth Observation in Agriculture Stuart Green...What’s driving Teagasc interest in EO Policy Analysis Economic cost of agri-environment measures Climate Change Harvest

The Use of Earth Observation in

Agriculture

Stuart Green Teagasc

March 31st 2020

Earth Observation for Ag.

Using satellites and drones

to monitor vegetation:

How well is grows

Where it grows

When it grows

Why it grow

Food Security

Agri-Environment

Precision Ag

Teagasc Presentation Footer 2

Advisory Offices

Research Farms

Colleges

Food Research Centres

HQ, Oak Park, Carlow

Agricultural Research Centres

The national body providing integrated

research, advisory and training services to

agriculture and the food industry

www.teagasc.ie/

Who are Teagasc?

What’s driving Teagasc interest in EO

Policy Analysis

Economic cost of

agri-environment measures

Climate Change

Harvest 2020

Farm Biodiversity

Landscape regulations

Soils

Earthobservation.wordpress.com

Food Security- the start of EO

NASA’s Landsat

program is the

longest continuous

global record of

Earth observations

from space – ever.

Since its first

satellite went up in

the summer of

1972, Landsat has

been looking at our

planet.

https://www.nasa.gov/mission_pages/lands

at/news/landsat-history.html

https://www.nasa.gov/mission_pages/landsat/news/landsat-history.html




The Great Grain Robbery

It became clear

food security was

an essential part of

the cold war- and

LANDSAT has been

estimating grain

yields globally since

then


USSR bought US

grain on open

market (grain

subsidiesed by US

gov) because of

Their own crop

failure in 72.

Earth Observation (EO) constellations.

There are approximately

800 EO satellites in orbit now.

50/50 split Military and Civilian

https://business.esa.int/newcomers-earth-observation-guide









Why use satellites:

find free image and mapping data.

Where is this?

http://gisgeography.com/100-earth-remote-sensing-applications-

uses/

• Wide overview

• Complete access

• Repeatability

• A different perspective

http://gisgeography.com/100-earth-remote-sensing-applications-uses/












Why use satellites:


• Wide overview

• Complete access

• Repeatability


Why use satellites:


http://staff.aub.edu.lb/~webeco/rs%20lectures.htm

• Wide overview

• Complete access

• Repeatability


http://staff.aub.edu.lb/~webeco/rs lectures.htm

http://staff.aub.edu.lb/~webeco/rs lectures.htm

Why use satellites:


In Short

• Wide overview

• Complete access

• Repeatability


• A new perspective • Information that’s not available in other ways • A synoptic view • The data is directly useable in GIS mapping

systems

Cameras collect: ELECTROMAGNETIC ENERGY


We’ll be using the Visible and IR

• We are all familiar with EMR (even if we don’t realise it!)

• Light, radio, microwaves – are all EMR and part of the Electromagnetic Spectrum

Violet

Indigo

Blue

Cyan

Green

Yello

w

Orange

Red

NIR

SWIR

MWIR

No Atmospheric

TransmissonLW

IR

Wavelength, nm 350 430 450 500 520 565 590 625 740 1000 3000 5000 8000

Properties of EMR


All Electromagnetic radiation (EMR) travels at the speed of light, C

You can chose to think of light as either model:

• The machine gun model: Image the sun (or a laser pointer or a lamp) firing out little packets of energy that shoot through the sky and bounce of, or through or are scattered by objects

Or

• The radio model: Imagine the sun broadcasting waves of energy like a radio antenna that reflect, or transmit or are diffracted by objects

In both cases the amount of energy is determined by the wavelength of the light involved and we can only see stuff because some of that energy is bounced or reflected off an object into our eyes (or our camera or our satellite imaging device).

c=l.n

C speed measured in meters per second,

ms-1

n frequency measured in hertz, Hz

λ wavelength measured in nano-meters,

nm

E is energy, measured in Joules, J

l

1E

Wave length is measured in nanometers nm Blue light has a wave length of ~450nm Green ~550nm Red ~650nm

14

15

Absorption (A) occurs when radiation (energy) is absorbed into the target while transmission (T) occurs when radiation passes through a target. Reflection (R) occurs when radiation "bounces" off the target and is redirected. In remote sensing, we are most interested in measuring the radiation reflected from targets.

What happens when a photon meets an object?

AES2012 L6 NDVI

Simple Ratio Index (SR) = NIR/R

Normalized Difference Vegetation Index (NDVI) =

NIR R

NIR R

Vegetation Index

Remember the images are stored in the image file matrices with a pixel in

one band corresponding with the pixel in another band with same xy

coordinates.

45 12 19

44 10 16

27 90 56

10 67 12

99 70 53

2 98 1

55 79 31

143 80 69

29 188 57

B1 B2 B1+B2

AES2012 L6 NDVI

So sun light is reflected form the earths surface,

interacts with the atmosphere and is then “Captured” by

the satellite sensor as an image.

20

Resolution


MODIS 250m Landsat ETM 30m

SPOT 7, 1.5m SPOT 7, 1.5m WorldView 0.46m

https://sentinel-hub.com/explore





First we need to know what we are looking at

CLASSIFICATION


Distinguishing landcovers using optical satellites

Sensors recording optical wavelengths have long been used to distinguish land cover classes based on reflectance patterns Red (0.6-0.7µm) and NIR (0.9-1.2µm) to create vegetation indices During the growing season spectral signature changes requiring multiple images to capture vegetation dynamics and phenology – cloud can preclude acquisition of optical images at optimal times

https://land.copernicus.eu/global/products/lc


https://land.copernicus.eu/global/products/lc

Suir Catchment- 2014

Covering about 2950

km2

Regional & Local Scale – Management examples

Historical land use with the Teagasc Landsat Archive

And our open source software

FOOD SECURITY




Main Global Food Security Systems http://www.fao.org/giews/en/

http://www.fews.net/

http://www.geoglam.org/inde

x.php/en/

https://www.wfp.org/content/

seasonal-monitor

http://dataviz.vam.wfp.org/

http://agri4cast.jrc.ec.europa

.eu/mars-explorer/

http://www.cropwatch.com.c

n/

https://www.fas.usda.gov/


http://www.fao.org/giews/en/

http://www.fao.org/giews/en/




http://www.geoglam.org/index.php/en/



https://www.wfp.org/content/seasonal-monitor









http://agri4cast.jrc.ec.europa.eu/mars-explorer/





http://www.cropwatch.com.cn/







Ag Business

Intel-

commodities

CropForcast

MARS Bulletins

GMES Globcast

Lanworth Insurance

Most of these systems look at trends over time



Crop Forecasts MCYFS is an integrated

analysis tool based on satellite observations of Earth, meteorological observations, meteorological forecasts, agro-meteorological and biophysical modelling, and statistical analyses. JRC scientists have developed specific applications to estimate rainfall, detect anomalies and produce early warning


SUSTAINABILITY


SDG https://sustainabledevelopment.un.org/sdgs

monitoring air and water quality, mapping

land use, development, and infrastructure

while assessing compliance with land use

regulations and property rights

assessing and monitoring the potential for

solar, wind, hydropower, and biofuel

development

mapping and monitoring forests, by

identifying degradation, rehabilitation, and

recovery

providing early warnings of vector-borne

diseases and natural disasters

providing information on crop health and

yields, market access, and pests and

diseases


https://sustainabledevelopment.un.org/sdgs

Impacts

Loss of Habitats

Water Pollution

Emissions

Erosion

Ecosystem Services

Storms

Flooding


https://www.eo4idi.eu/





Satellite-based detailed

land cover/use

information shows

changes in agricultural

ecosystems. In this

case, natural vegetation

was transformed into oil

palm plantations in a

catchment of southern

Palawan, Philippines.

Such information

supports integrated

ecosystem

management.

Copyright: GeoVille for

ESA/World Bank

WAVES

EO to predcit Locust Swarms

Modelling swarm

movement/emergen

ce based on soil

moisture and

greeness


https://earthobservatory.nasa.gov/images/1

46495/could-satellites-help-head-off-a-

locust-invasion?utm=carousel

https://earthobservatory.nasa.gov/images/146495/could-satellites-help-head-off-a-locust-invasion?utm=carousel
















Laser Scanning

LIDAR4Hedge

Using an aerial laser scanning

technique called LIDAR we can

create 3D models of

vegetation.

Here we modelled hedgerows

and estimated the carbon

content and sequestration

potential. Using an earlier

Hedgerow map of Ireland I

produced, we estimated

national carbon holding in

hedgerows.

It may be important in the future to know the carbon

stock/sequestration potential of a parcel

Both Traditional Survey methods (plot

based) and new UAV approaches were

tested

Managed hedge on ditch (red arrow) Hedge on a wall (red arrow)

Treeline Ditch with no hedge

Remote sensing in Teagasc- drainage status of fields calculated using the latest remote

sensing techniques.

Drainage

Overland flow can know be

modelled cheaply with

drone photogrammetry-

lidar not needed in many

cases

In DRAINMAP we are monitoring the effectiveness of

artificial drainage using RS.

Mapping the impact of persistent flooding on grass

production

Floods can persist in some areas for some time. Combining RADAR and multispectral satellite data, we can map the impact of prolonged flooding on grass growth. If saturation persists after mid-February, the time required to recover to levels of production is longer. Fields still saturated in early April can take several weeks to recover fully.

O’Hara R, Green S, & McCarthy T. The agricultural impact of the 2015–2016 floods in Ireland as mapped through Sentinel 1 satellite imagery. Irish Journal of Agricultural and Food Research 2019; 58(1):44 https://doi.org/10.2478/ijafr-2019-0006

https://doi.org/10.2478/ijafr-2019-0006





Regional & Local Scale - Grass

PRECISION AG


Integration of modern technologies for continuous monitoring of

Spatiotemporal variability in the field and efficient management of

resources to increase the yield and minimise economic and environmental

costs.

GPS technologies

Sensor networks

Data Cloud

Continuous data analysis

Remote Sensing and mapping

- satellite, airborne, UAVs

field instruments

Modelling and prediction

- Combining high spatial

and temporal resolution

data.




https://rd.springer.com/chapter/10.1007/978-3-319-65633-5_12










The Tech Variable rate technology (VRT) – any technology or method allowing

farmers to control the amount of inputs applicable within defined

farming areas.

GPS soil sampling – this method is based on taking samples of soil

to check nutrients, pH level, and other data to make profitable

decisions in agriculture.

Computer-based applications – this refers to applications used to

create precise farm plans, field maps, crop scouting, yield maps and

to define the exact amount of inputs to be applied to fields.

Remote sensing technology – the method determines factors that

can stress a crop at a specific time to estimate the amount of moisture

in the soil. The dataset is obtained from drones and satellites.

Compared to drone data, satellite imagery is more accessible and

multi-purpose.

https://cropsat.com/




https://eos.com/


https://eos.com/

FARMSTAR


Oakpar

k

Grange

Remote sensing in Teagasc- Monitoring grass from space – precision

agriculture and “Big Data” for Ireland’s most important crop.

Biomass from Satellite Using Moorepark grass growth

data a Machine Learning

algorithm (ANNFIS) has been

taught to estimate biomass at

field scale from daily satellite

observations.

New RADAR Imagery from ESA allows us to observe

management in all weathers.

Monitoring grass from space – precision agriculture and “Big Data” for

Ireland’s most important crop.

SATGRASS

Combining real time

observation of growing

conditions we can relate farm

decision making to conditions.

.

The current online services showing trends in grass cover will be

developed into grass growth predictions.

Mapping buried drains with UAS TIR

Mapping P loss with UAS photogrammetry


https://www.frontiersin.org/articles/10.3389/fsufs.2019.00054/full

Big Data


https://www.frontiersin.org/articles/10.3389/

fsufs.2019.00054/full



Link sensors to EO


We know we can teach the

computer to recognise farm

land covers from satellite- but

can we also teach the

computer to recognise similar

land covers in photographs

and match them together.

Farm & Field scale– MANAGEMENT examples

• For every week earlier grass grows in spring, farmers gain

3.7 days in grazing season.

• Every 100mm extra rain in spring means Turn Out is a day

later

• A Well-drained soil makes turn out 2.5 days earlier

compared to a poorly drained soil.

• TOD gets a day later for every 16km north from the south

coast.

We now have a definition of what is

extreme weather in farming in Ireland

For every extreme wet day in summer,

the concentrate use will increase around

4kg/LU

For every extreme cold day in spring, the

concentrates will increase around 9kg/LU.

END


Documents

The Use of Earth Observation in Agriculture Stuart Green...What’s driving Teagasc interest in EO Policy Analysis Economic cost of agri-environment measures Climate Change Harvest