ARD Preparation and Integration in Taiwan Data Cube
Transcript of ARD Preparation and Integration in Taiwan Data Cube
1This document contains proprietary and controlled information of National Space Organization (NSPO) of Taiwan and shall not be duplicated in whole or in part for any purposes without permission from NSPO.
Associate Researcher
Li-Yu Chang
ARD Preparation and Integration in
Taiwan Data Cube
2This document contains proprietary and controlled information of National Space Organization (NSPO) of Taiwan and shall not be duplicated in whole or in part for any purposes without permission from NSPO.
Content
Pre-processing of remote sensing data
– Top of Atmospheric Reflectance of FORMOSAT-2/5 optical image
– Surface Reflectance of Sentinel-2 optical image
– Radar backscatter coefficient of Sentinel-1 SAR image
Taiwan Data Cube (TWDC) integration with ARD data
– FORMOSAT-2/5 ARD
– Sentinel-1/2 ARD
TWDC data export
– Custom mosaic of data cube application
– Jupyter envrionment
Application of TWDC
– Paddy rice detection using FORMOSAT-5 Images
– Flooding area detection using Sentinel-1 SAR Data
3
Pre-processing ofremote sensing data
Top of Atmospheric Reflectance of FORMOSAT-2/5 optical image
4This document contains proprietary and controlled information of National Space Organization (NSPO) of Taiwan and shall not be duplicated in whole or in part for any purposes without permission from NSPO.
Process of optical remote sensing
Parameters that affect the received
radiance of optical satellite
– Reflectance
– Solar incident angle
– Sensor’s view angle
– Atmosphere related parameters
• Atmospheric transmission
• Path radiance
• Diffused radiance
Reflectance estimation of ground
object
– Surface Reflectance, SR
– Top of Atmosphere Reflectance, TOAR
• A reasonable approximation
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Estimation of TOAR
DN (for Digital Numbers) to spectral radiance (or
TOA Radiance) L
L(b)=DN(b)/gain(b)+bias(b)
(in W/m2/steradians/micrometers)
TOA Radiance to TOA Reflectance
TOAR(b) = (π*L(b)*d*d) / (ESUN(b)*cos(θ))
L(b): the spectral radiance for band b
d: earth-sun distance (in astronomical units)
ESUN(b): mean TOA solar irradiance (or solar illumination) in W/m2/micrometers
θ : solar incident angle in degrees
θ
θ
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Why we need reflectance
Reflectance as a normalized
observations for ground information
retrieval
– Multi-temporal image application
– Multi-sensor image application
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DN、TOAR and NDVI obtained by
FORMOSAT-5 and SPOT-6
FORMOSAT-5 SPOT-6
2017/12/20 02:40
(UTC)
2017/12/20 02:15
(UTC)
Temporal information of used images
Location of Study Area
FORMOSAT-5
SPOT-6
Study area is 9km x 9km
NDVI of FORMOSAT-5
NDVI of SPOT-6
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Comparison of FORMOSAT-5 and
SPOT-6:DN
DN of SPOT-6 Images
DN
of
FO
RM
OS
AT
-5 I
ma
ges
DN of SPOT-6 Images
DN
of
FO
RM
OS
AT
-5 I
ma
ges
DN of SPOT-6 Images
DN
of
FO
RM
OS
AT
-5 I
ma
ges
DN of SPOT-6 Images
DN
of
FO
RM
OS
AT
-5 I
ma
ges
Band 1 Band 2
Band 3 Band 4
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Comparison of FORMOSAT-5 and
SPOT-6:TOAR
TOAR of SPOT-6 Images
TO
AR
of
FO
RM
OS
AT
-5 I
ma
ge
s
TOAR of SPOT-6 Images
TOAR of SPOT-6 Images
TO
AR
of
FO
RM
OS
AT
-5 I
ma
ges
TOAR of SPOT-6 Images
Band 1 Band 2
Band 3 Band 4
TO
AR
of
FO
RM
OS
AT
-5 I
ma
ge
sT
OA
R o
f F
OR
MO
SA
T-5
Im
ag
es
10This document contains proprietary and controlled information of National Space Organization (NSPO) of Taiwan and shall not be duplicated in whole or in part for any purposes without permission from NSPO.This document contains proprietary and controlled information of National Space Organization (NSPO) of Taiwan and shall not be duplicated in whole or in part for any purposes without permission from NSPO.
Comparison of FORMOSAT-5 and
SPOT-6: NDVI
NDVI obtained
from DN
NDVI =IR − R
IR + R
NDVI obtained
from TOAR
NDVI of SPOT-6 Images
ND
VI
of
FO
RM
OS
AT
-5 I
ma
ges
NDVI of SPOT-6 Images
ND
VI
of
FO
RM
OS
AT
-5 I
ma
ges
11This document contains proprietary and controlled information of National Space Organization (NSPO) of Taiwan and shall not be duplicated in whole or in part for any purposes without permission from NSPO.This document contains proprietary and controlled information of National Space Organization (NSPO) of Taiwan and shall not be duplicated in whole or in part for any purposes without permission from NSPO.
Multi-temporal and Multi-sensor rice
phenology observation by TOAR
2017/07/20
RapidEye
2017/08/28
RapidEye
2017/08/06
SPOT-7
2017/10/01
RapidEye
2017/09/19
FORMOSAT-5
2017/12/14
RapidEye
2017/11/10
SPOT-6
2017/12/20
FORMOSAT-5
12This document contains proprietary and controlled information of National Space Organization (NSPO) of Taiwan and shall not be duplicated in whole or in part for any purposes without permission from NSPO.This document contains proprietary and controlled information of National Space Organization (NSPO) of Taiwan and shall not be duplicated in whole or in part for any purposes without permission from NSPO.
Multi-Temporal NDVI Composite for
rice observation
R: 2017/12/14 NDVI by RapidEye
G: 2017/09/19 NDVI by FORMOSAT-5
B: 2017/08/06 NDVI by SPOT-7
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Rice phenology fitting by
double logistic functionSite 1
Rice Field
Site 2
Rice Field
Site 3
Building
Site 4
Forest
Red lines are fitted by
Double Logistics Function:
14
Pre-processing ofremote sensing data
Surface Reflectance of Sentinel-2 optical image
15
Sentinel-2 Optical Data
https://sentinel.esa.int/web/sentinel/missions/sentinel-2/data-products
Buriram
Study Area:
Near by region
around Buriram
Multi-Temporal Remote Sensing Data of OCT-NOV, 2019
Paddy
fields
SENTINEL-2
(2019/10/21)
Maximum
Biomass
SENTINEL-2
(2019/11/20)
Harvested
Estimated paddy rice area:
14477.7(HA)
16
Sentinel-2 Optical Data
Level-2A Source data in Copernicus Open Access Hub is SR already
Once downloaded, no further pre-processing needed
17
Pre-processing ofremote sensing data
Radar backscatter coefficient of Sentinel-1 SAR image
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Sentinel-1 SAR Data
Acquisition modes (C-band, 5.404 GHz,
693KM)
– Stripmap (SM)
– Interferometric Wide Swath (IW)
– Extra Wide Swath (EW)
– Wave (WV)
ModeResolution
rg x az
Number of
looks
SM 1.7x4.3 m to 3.6x4.9 m 1x1
IW 2.7x22 m to 3.5x22 m 1x1
EW 7.9x43 m to 15x43 m 1x1
WV 2.0x4.8 m and 3.1x4.8 m 1x1
https://sentinel.esa.int/web/sentinel/technical-guides/sentinel-1-sar/sar-instrument
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Interferometric Wide Swath
IW mode captures
three sub-swaths
using Terrain
Observation with
Progressive Scans
SAR (TOPS)
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Product Types and Processing
Levels
L0 RAW
L1 SLC
L1 GRD• Full Resolution
• High Resolution
• Medium Resolution
L2 OCN• Ocean Wind Field
• Ocean Swell Spectra
• Surface Radial Velocity
L0 RAW
L1 SLC
L0 RAW
L1 SLC L1 SLC
L1 GRD• High Resolution
• Medium Resolution
L1 GRD• High Resolution
• Medium Resolution
L2 OCN• Ocean Wind Field
• Surface Radial Velocity
L2 OCN• Ocean Wind Field
• Surface Radial Velocity
L2 OCN• Ocean Wind Field
• Surface Radial Velocity
L1 SLC
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Product Types and Processing
Levels Level-0
– The SAR Level-0 products consist of compressed and unfocused SAR
raw data. Level-0 products are the basis from which all other high level
products are produced.
Level-1
– Level-1 focused data are the products intended for most data users.
The Level-0 product (raw data) is transformed into a Level-1 product by
the Instrument Processing Facility (IPF)
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Why we need σ0 ?
Normalization of SAR imagery is
required for a meaningful analysis of
multi-sensor or even single-sensor
multi-track data
Radar backscatter coefficient σ0 is a
normalized estimation to enable more
robust use of the retrieved SAR data
values in applications
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Radar backscattering coefficient σ0
Radar backscatter is the effects of terrain on
the radar signal that we are most interested
in, i.e. the amount of radar cross-section, σ,
reflected back to the receiver, per unit area
on the ground
σ
σ
Small, D. (2011). Flattening gamma: Radiometric terrain correction for SAR
imagery. IEEE Transactions on Geoscience and Remote Sensing, 49(8), 3081-3093.
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Processing of σ0 for Sentinel-1 SAR Data
Manual procedure
– Thermal noise removal
– Calibration to radar
backscattering coefficient
– Deburst and merge for TOPS
data
– Multilooking
– Speckle filtering
– Slant Range to Ground
Range conversion
– dB Conversion
– Topographic Correction
Processing with ESA SNAP
(Sentinel Application Platform)
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Processing of σ0 for Sentinel-1 SAR Data
Automatic procedure– Using Graphic Builder under ESA
SNAP to generate XML of
processing procedure
– Applying GPT (Graph Processing
Tool)、GDAL (Geospatial Data
Abstraction Library) and
customized code to create SAR
σ0 and corresponding Quick-Look
image
• Different IW mode data are
processed separately to increase
efficiency
– Building a batch process to
automate whole workflow
SNAP
Graphic Builder
Batch for
Workflow Control
ESA SNAP XML for GPT
26
Taiwan Data Cube (TWDC) integration with ARD data
27This document contains proprietary and controlled information of National Space Organization (NSPO) of Taiwan and shall not be duplicated in whole or in part for any purposes without permission from NSPO.Credit to Alex Held (CSIRO, Australia) @ workshop in Taiwan, 2017
28This document contains proprietary and controlled information of National Space Organization (NSPO) of Taiwan and shall not be duplicated in whole or in part for any purposes without permission from NSPO.
Surface reflectance and radar
backscatter as ARD datasets
Truckenbrodt, John, et al. "Towards Sentinel-1 SAR Analysis-Ready
Data: A Best Practices Assessment on Preparing Backscatter Data for
the Cube." Data 4.3 (2019): 93.
http://ceos.org/ard/index.html#slide1
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Reflectance and radar backscatter
as ARD datasets in TWDC
Data Cube integration with
FORMOSAT-2/5 and Sentinel-1 data
– FORMOSAT-2/5: TOAR with quality Index
– Sentinel-1: Back scatter coefficient
– Sentinel-2: Surface reflectance with quality
Index
30
TWDC Integration of FORMOSAT-2/5 ARD
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TWDC Integration of FORMOSAT-2/5
ARD
Pre-processing and orthoimage
generation
ARD preparation
– TOAR
– Quality index (QI)
ARD import
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Pre-processing and rthorectification
By integrated processing of DMS (Data Management System) and
DPS (Data Processing System) in NSPO, the received FORMOSAT-
2/5 data can be processed in streamline and the orthoimage cab be
generated automatically
Product Generation Framework
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TOAR ARD
product
Ortho-rectified
product
FORMOSAT-2/5 ARD import
Generation of FORMOSAT-2/5 ARD
including TOAR and QI is part of
processing procedure of Formosat-
2/5 image production line
ARD is sent to NCHC by FTP and
import to TWDC
– In NCHC, needed .PY and .YAML
scripts are prepared for importing
ARD to TWDC
Import to DC
QITOAR
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Cloud coverage detection
Stage 1: Check if image cloud free
– Classify image by k-means
– Check cloud existence by spectral
thresholds
Stage 2: Extract cloud and potential
cloud
– Segment image by region growing
– Cluster segment patch by k-means
– Extract cloud and potential cloud with
different spectral thresholds
Stage 3: Cloud refinement
– Merge neighboring potential cloud by using
extracted cloud as seed
– Filter small potential clouds with area
threshold
– Remove broken parts and gaps in clouds by
morphology closing
FORMOSAT-2/5 Level4
MS TOAR Image
k-means clustering
Image Segmentation
Cloud Mask
Cloud free or
not
Cloud free image
Cloud Refinement
Image with Cloud
Higher
Spectral
Threshold as
Clouds
Lower
Spectral
Threshold as
Potential
Clouds
35
TWDC Integration of Sentinel-1/2 ARD
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Integration of Sentinel-1 ARD
Procedure for automatic processing of
Sentinel-1 ARD
– Follow the steps suggested in
Copernicus Open Access Hub to
download Sentinel-1 data
• Obtain download List
• Download the data according to list
– Check integrity of downloaded file
– Radar backscattering coefficient ARD
generation (As describe above)
– Post processing for ARD
Prepare needed .PY and .YAML scripts
for importing ARD to DC
Above processing procedures are
triggered periodically for data updating
Period and Coverage Setting
Search Download List from
Copernicus Data Hub
Download SLC Products
Import σ0 ARD to DC
Generate σ0 Using SNAP GPT
and Perform Post-Processing
σ0 ARD Product in DC
Product
Integrity
Check
Success
Fail
37This document contains proprietary and controlled information of National Space Organization (NSPO) of Taiwan and shall not be duplicated in whole or in part for any purposes without permission from NSPO.
Integration of Sentinel-2 ARD
Procedure for automatic processing
of Sentinel-2 ARD
– Follow the steps suggested in
Copernicus Open Access Hub to
download Sentinel-2 data
• Obtain download List
• Download the data according to list
– Check integrity of downloaded file
Prepare needed .PY and .YAML
scripts for importing ARD to DC
Above processing procedures are
triggered periodically for data
updating
Once L2A product is not available,
L1C product will be downloaded and
“Sen2Cor” process will be followed
to generate L2A product
Period and Coverage Setting
Search Download List from
Copernicus Data Hub
Download L2A Products
Product
Integrity
Check
Fail
L2A ProductL1C Product
Sen2Cor
L2A Product
38
TWDC data export
Custom mosaic of data cube application
39
Exporting data by custom mosaic
40
Single date data: 2018/11/21
Cloud Mask
41
Single date data: 2018/12/17
No Data Area
42
Multiple date data mosaic:2018/11/21+2018/12/17
Better Result
43
TWDC data export
Jupyter enviroment
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TWDC data export with Jupyter
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TWDC data export with Jupyter
FS-2 Image export for
Thailand Area
FS-2 Image export for
Taiwan Area
S-1 Image export for
Taiwan Area
46
Application of TWDC
Paddy rice detection using FORMOSAT-5 Images
47This document contains proprietary and controlled information of National Space Organization (NSPO) of Taiwan and shall not be duplicated in whole or in part for any purposes without permission from NSPO.
Paddy rice detection using
FORMOSAT-5 Images Change detection for rice growing area in Taiwan
– Farm fields prepared in winter and filled with water in spring for rice
planting
– Some farmers also grow green manure or cover crops in winter to
increase land fertility or income
Source Data
– FORMOSAT-5 images in winter (2018/12/17) and spring (2019/03/01)
Applied approaches
– Change detection by Change Vector Analysis (CVA)
• Using CVA to detect changed area
– Normalized Difference Vegetation Index (NDVI)
• Delineating vegetation and bare soil areas in prior image
– Normalized Difference Water Index (NDWI)
• Extracting water area in later image
48This document contains proprietary and controlled information of National Space Organization (NSPO) of Taiwan and shall not be duplicated in whole or in part for any purposes without permission from NSPO.This document contains proprietary and controlled information of National Space Organization (NSPO) of Taiwan and shall not be duplicated in whole or in part for any purposes without permission from NSPO.
Workflow of paddy rice detection
Prior Period ARD Image Later Period ARD Image
Obtaining NDVI Obtaining NDWI
Change Vector Analysis
(CVA)
Paddy Rice
with green manure
Is Land
Changed?
(CVA > TCVA)
Is Prior Period
Vegetation?
(NDVI > TNDVI)
Is Later Period
Water?
(NDWI>TNDWI)
No Paddy RicePaddy Rice
without green manure
YES
YES
YES
NO
NO
NO
49This document contains proprietary and controlled information of National Space Organization (NSPO) of Taiwan and shall not be duplicated in whole or in part for any purposes without permission from NSPO.
Paddy rice detection with ERDAS
Imagine modeler (Post-processing)
50This document contains proprietary and controlled information of National Space Organization (NSPO) of Taiwan and shall not be duplicated in whole or in part for any purposes without permission from NSPO.
Paddy rice detection with Jupyter
(Online processing)
51This document contains proprietary and controlled information of National Space Organization (NSPO) of Taiwan and shall not be duplicated in whole or in part for any purposes without permission from NSPO.
Source FORMOSAT-5 images
2018/12/17 2019/03/01
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Analyzed result
Paddy rice with green manure Paddy rice without green manure
2018/12/17 2019/03/01
53This document contains proprietary and controlled information of National Space Organization (NSPO) of Taiwan and shall not be duplicated in whole or in part for any purposes without permission from NSPO.This document contains proprietary and controlled information of National Space Organization (NSPO) of Taiwan and shall not be duplicated in whole or in part for any purposes without permission from NSPO.
Map publishing for rice field map
54
Application of TWDC
Flooding area detection using Sentinel-1 SAR Data
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Flooding area detection using SAR
Images
Radar backscatter of water is small due to
water’s specular reflection property
If an area was changed from non-water to
water, the corresponding backscatter
coefficients should be decreased
𝜎𝐿𝑎𝑡𝑒𝑟0 − 𝜎𝑃𝑟𝑖𝑜𝑟
0 <δ
In addition, following criteria should be
satisfied as well
𝜎𝑃𝑟𝑖𝑜𝑟0 > 𝜎𝑊𝑎𝑡𝑒𝑟
0
𝜎𝐿𝑎𝑡𝑒𝑟0 ≤ 𝜎𝑊𝑎𝑡𝑒𝑟
0
56This document contains proprietary and controlled information of National Space Organization (NSPO) of Taiwan and shall not be duplicated in whole or in part for any purposes without permission from NSPO.This document contains proprietary and controlled information of National Space Organization (NSPO) of Taiwan and shall not be duplicated in whole or in part for any purposes without permission from NSPO.
Workflow of flooding area detection
Prior Period SAR
Backscatter Image
Later Period SAR
Backscatter Image
Multi-Temporal
Backscatter Differencing
Flooded Area
Is Difference
Smaller Than
Threshold?
Is Later Period
Water?(𝜎𝐿𝑎𝑡𝑒𝑟
0 ≤ 𝜎𝑊𝑎𝑡𝑒𝑟0 )
Is Prior Period
Non-Water?
(𝜎𝑃𝑟𝑖𝑜𝑟0 > 𝜎𝑊𝑎𝑡𝑒𝑟
0 )
Not Flooded Area
YES
YES
YES
NO
NO
NO
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Flooding area detection with ERDAS
Imagine modeler (Post-processing)
58This document contains proprietary and controlled information of National Space Organization (NSPO) of Taiwan and shall not be duplicated in whole or in part for any purposes without permission from NSPO.
Flooding area detection with Jupyter
(Online processing)
59This document contains proprietary and controlled information of National Space Organization (NSPO) of Taiwan and shall not be duplicated in whole or in part for any purposes without permission from NSPO.This document contains proprietary and controlled information of National Space Organization (NSPO) of Taiwan and shall not be duplicated in whole or in part for any purposes without permission from NSPO.
Flooding over south Taiwan due to
heavy rain on 2018/8/23
Sentinel-1 2018/08/14 Sentinel-1 2018/08/26
60This document contains proprietary and controlled information of National Space Organization (NSPO) of Taiwan and shall not be duplicated in whole or in part for any purposes without permission from NSPO.This document contains proprietary and controlled information of National Space Organization (NSPO) of Taiwan and shall not be duplicated in whole or in part for any purposes without permission from NSPO.
Analyzed result
Sentinel-1 2018/08/14 Sentinel-1 2018/08/26
Flooded area
61This document contains proprietary and controlled information of National Space Organization (NSPO) of Taiwan and shall not be duplicated in whole or in part for any purposes without permission from NSPO.This document contains proprietary and controlled information of National Space Organization (NSPO) of Taiwan and shall not be duplicated in whole or in part for any purposes without permission from NSPO.
Map publishing for flooded area