Oleksiy Kravchenko

Senior Data Scientist, Zoral Labs, Kyiv

Satellite orbits

Geostationary satellite images - MSG

Polar orbiting images - MODIS

Multispectral data

Data

– Low resolution

• MODIS (250м)

• Proba-V (100м)

• Sentinel-3 (300 m)

– Medium resolution

• Landsat-7 (30м)

• Landsat-8 (30м)

• Sentinel-1 (20м, 2013)

• Sentinel-2 (10м, 2015)

Довжина хвилі, нм

Від

би

ва

юча

зд

атн

ість Поглинання

хлорофілу Поглинання води

Поглинання

сухої біомаси

Визначається

внутрішньою

структурою

покриву

Видимий

(VIS)

Ближній

інфрачервоний

(NIR)

Середній інфрачервоний

(SWIR)

NDVI = (NIR – RED)/

(NIR + RED)

Applications

Agriculture– Agricultural statistics

• Crop area estimation

– Yield forecasting– Crop state & dynamics monitoring– Drought monitoring– Irrigated land monitoring– Crop rotation control, subsides control– Precision agriculture

• Variable prescription maps

Forestry– Clear-cuts mapping, species mapping– Biomass estimation

Land cover mapping

7

Satellite features for crop classificationL5 2011-11-08

EO1 2012-05-23

L7 2012-05-10

Sich2 2012-04-26

Temporal dynamics

Crop rotation violations (3 years period)

Rapeseed area

2011,2013: 490 ha

2011,2012: 430 ha

2012,2013: 435 ha

2013: 1540 ha

2012 2220 ha

2011 1610 ha

Lviv site, SAR data example, 2013

Landsat8

2013-06-15 Radarsat-2

2013-06-13

No clouds problems

Better crop

separation with SAR

than with optical data

Winter rapeseed

Maize

Gorodotsky county, Lviv region, UkraineCrop mapping, 2013

• Classification accuracy– total 81 %

– Winter rapeseed: 96%

– +10% by using SAR data

Crop area estimation

Project ЕС JRC “Crop area estimation with satellite images in Ukraine”, 2009-2011

Satellite data Ground data

Processing· Orthorectification

· Segmentation

· Classification

Stratified Area

Frame Sampling

Along the road

survey

% pixe ls c lassified as cerea ls

% o

ats

in

gro

un

d s

urv

ey

SegmentsCrop field boundariesLC map

Area estimates

(pixel counting)

Data fusionAdjustment of area

estimates

(Regression estimator)

Final results· Area estimates

· Accuracy

assessment

Data:MODISAWiFSLandsat-5/TMLISS-IIIRapidEye

Satellite data effect:costs decrease in 1.5 times

Area estimation

Odesa region

regsample yVyV

rel_eff

yybx yybxdif x reg x

VyNn

Nnn

G

nreg

xy()

1

1

3

21

2

2

2 2G

kx

x

x

33

( )Vynys rreg xy

112 2

regsample yVyV ˆˆrel_eff

r = 0.986rel_eff = 33.4

r = 0.997rel_eff = 165.8

Data Areath. ha

Error(2σ)ths. Ha

%

Sample 108.32 51.2 57.4%

MODIS 95.18 8.88 9.32%

Landsat 96.18 3.98 4.14%

Ministry of Agric. 101,0 - -

Yield forecast

Winter wheat yield forecast, 2013Forecast issue date – May 1st , 2013

Precision agriculture

Irrigation monitoring

August, 2012

Vegetation State Estimation (Forward &

Inverse Problems)

Leaf model

(PROSPECT)

Canopy RTM

(4SAIL)

Atmosphere RTM

(6S)

ll TR ,

3. Vegetation state

estimation using

radiometrically corrected

satellite data

Inverse problems

2. Vegetation state

estimation using satellite

data with atmospheric

correction

1. Vegetation state

estimation using in-situ

spectra

,, vs

soilR

lSLADLAI ,,

mwab CCCN, ,,

canopyR

TOAR

'

ss

s

s

vv s

dEk E

dz

dEa E E s E

dz

dEa E E s E

dz

dEK E E E w E

dz

1( ) ( ) ( )i i ek K C k

N

90 1,90,

90 1,901

NN

N

RR

R

Satellite product validation (crop state)

• Ukrainian GEO JECAM polygon

• Hemispherical photography (circular fisheye lenses)

LAI=0.22 fCover=16% ALA=16º

LAI=4.0 fCover=79% ALA=65º

Illegal cropping

znaydeno.com.ua

Illegal crops on 5.9 Ha, 7k hryvnas fines payed

Satellite image preprocessing.

Sentinel-2 example

Source: SPOT SATELLITE

GEOMETRY HANDBOOK, 2002

Direct model

Model parameters

• Time, location, velocity (satellite center mass), 1Hz

• Exterior attitude, 1Hz

– measured by star trackers

• Interior camera attitude within satellite coordinate system

– rotation matrix to align camera and star tracker

• Angular position of each pixel in camera coordinates

– Angular position of pixel assembly in focal plane

– Lenses distortion

Inverse model (RPC)

Direct model georeferensing of Sich2 satellite• Geolocation error

– RapidEye (20-50 м)

– Landsat5 (30 м)

– Spot6,7 (10 м)

– Sich2 (200-700 м)• Kyiv (240 м)

• Shatsk (550 м)

Shatsky National ParkSich2, 2011-11-02

550m

USGS approachfor Landsat5

Drones georeferencing by telemetry

data only

Processing pipeline

Image based registration methods

Linear correlation surfacePhase correlation

*

1 2

*

1 2

, ,

, ,

d dx y x y x x y y

x y x y

IM IMe

IM IM

Phase correlation explained

Accuracy estimation

Linear vs. phase correlation

10-100 more control points

Odesa region

20 м ~ 6”

Sich2 image georeferencing using Landsat data as base map

400 m

0 m

Crimea, 2012-05-19

Orthorectification

1A

1А-Orho

Zaporizhzhya, 2012-02-11

Georef. example (1)

Khersonsky region, 2012 -05-12

1A

1A-Ortho

Georef. example (2)

Pixel assembly calibration

Corrections.Metadata

2011-10-03

Trend analysis Corrections provided in metadata

2012-09-05

Afterlaunch

Afterlaunch

1 yearon orbit

Pixel assembly coregistration (1)

1 yearon orbit

Band coregistration

PAN NIR

Along-track shifts

DEMSRTM

Jitter: Sich2 example

Roll oscillationAmplitude ~ 4m

DI

Pitch oscillationAmplitude ~ 2m

DI

DI, pixels

Jitter (2)2011-10-05 2011-10-28

Reducing jitter

SSTL, 2008 Attitude Determination through

Registration of Earth Observational Imagery

Momentum Wheel Activation (Y-Axis) -DU000373

Pixel assembly coregistration (1)

Sentinel-2

Pleiades

Pixel assembly coregistration (2)

Satellite image vs. aerial registration

airplane tracks

Distortions due to tall buildings

Questions?