RECONSTRUCTION OF REMOTE

SENSING TIME SERIES DATA

Javier Rivera

VIP Laboratory

REMOTE SENSING

Remote sensing can be defined as the acquisition of information

about the state and condition of an object with sensors without

taking a physical sample.

Remote sensing technology is used, among others things, for:

Agricultural management

Land cover and land use status and change

Weather (data collection)

Study of environmental changes

Fires monitoring

Phenology of plants, etc…

REMOTE SENSING SYSTEM

EXAMPLE PLATFORMS

Multispectral coarse resolution Global Imager

Study of the whole Earth System (multi-purpose data records)

AVHRR (Advanced Very High Resolution Radiometer )

1981-Present

Local Area Coverage (LAC @ 1.1 km) vs. Global Area Coverage

(GAC @ 4km)

MODIS (Moderate-resolution Imaging Spectroradiometer)

(Terra : 2000-Present & Aqua 2002 – Present)

250m, 500m and 1km

SPOT VGT

1998 to present

1km

Multispectral Medium resolution

Landsat TM/ETM+ series

The full record is now free (use to be $500/scene)

High resolution and hyperspectral

HYPERION, ALI, EO1, IKONOS, QuickBird, etc…

INTRODUCTION (VEGETATION INDICES)

Spectral Vegetation Indices are

radiometric measures of the

amount, structure, and condition of

vegetation, and a precise monitoring

tool

They provide key data to:

famine early warning systems

Hydrologic and Biogeochemical

studies

Phenology and Seasonality

Land cover and land cover change

(interannual variation)

Natural resource

management, monitoring and

sustainable development

Climate studies

Improves

the signal

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% R

efl

ecta

nce

Wavelength (nm)

VEGETATION INDICES

1*5.7*6*5.2

bluerednir

rednirEVI

rednir

rednirNDVI

1*4.2*5.22

rednir

rednirEVI

ρred=reflectance in the red region

ρnir=reflectance in the NIR region

ρblue=reflectance in the blue region

The objective of VI is to extract the “green” signal only (what is the green signal

is highly debatable, but it is a proxy for many vegetation conditions).

PHENOLOGY

Vegetation phenology can be defined as the plants study of the biological cycle events throughout the year and the seasonal and interannual response by climate variations. Phenology products, produced daily or on 16-day compositing period, provided different parameters which describe the seasonal behavior of the vegetation.

WHAT WE ACTUALLY DESIRE IN DATA

RECORD

What are the best characteristics of a data record

Resolution

Frequency

Accuracy

Fidelity

Long term

CURRENT METHODS

Threshold-based – Sacrifice temporal resolution

Fourier-based – Sub-estimate VI

Gaussian functions fitting – Time consuming and

cannot be applied to all types of data

PROBLEM STATEMENT

Time series data records exhibit multiple spatial and temporal discontinuities that inhibit the proper and accurate study of the natural and gradual process of growth of land surface vegetation.

These discontinuities, or gaps, are a result of: cloud cover

atmosphere contaminants

Shadow

less than ideal viewing

geometry

Sensor acquisition geometry

OBJECTIVES

Develop a new Data GAP filling method that

improves the temporal resolution and preserves

the data quality

To develop a gap filling technique that improves the

temporal characterization of the land surface and

preserves the integrity of data in support of accurate

change studies.

To evaluate the performance and reliability of this

gap filling method. To test the performance of this

data gap filling technique/Method

Impact on Phenology

Impact on Time series profiles

DATA - MODIS• Daily Global Data

• AQUA : 2002 – Current

• TERRA: 2000 - Current

• Resolution: CMG, 0.05 degrees (5600 meters)

• Projection: Unprojected, Latitude/Longitude

• Image dimensions: 3600 Rows, 7200 Cols

METHODOLOGY

Obtain long term time series data (from MODIS and AVHRR)

Data Filtering

Determine the average of the long term time series data and its confidence interval

Determine the Maximum Compositing Period

Fill Remaining GAPs using Inverse Distance Weighted method

DATA FILTERING - DECISION TREE

Rank=5IsCloud

y

Yes

No

START

Cloud

Shadow

Vz<=30

Rank=1 Rank=3

Yes

No

Rank=2

Data

valid

Yes

NoRank =7

Rank=4IsSnow

Yes

Yes

Low

Aerosol

No

No

Yes

No

Low

Aerosol &

Vz<30

Rank=2

No

Yes

This process depends heavily on the input data, and aims at limiting and eliminating problematic data from the ESDR records.

Cloudy Data, data with high Aerosols loads (MODIS only), and out of range data are all removed. Large spatial gaps result from this process.

Rank Description

1 Ideal Data, Good and Useful

2 Good to Marginal

3 Marginal to Questionable

4 Snow

5 Cloud

6 Estimated

7 NO DATA

COMPOSITING PERIODS

Compositing periods (days): 2, 3, 4, 5, 6, 7, 8, 9, 10, 15, 20, 25, 30, 40

and 50

DETERMINE AN OPTIMAL COMPOSING NUMBER OF

GLOBAL DAILY IMAGES

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Fre

qu

en

cy

Qu

ali

ty (

%)

Glo

ba

l V

I g

oo

d d

ata

(%

)

Compositing Period (days)

S80

S260

S175

FQ

MEAN OF MISSING POINTS (DAYS WITHOUT VI DATA)

BETWEEN TWO CONSECUTIVE KNOWN POINTS.

Jan, Feb, Mar Apr, May, Jun

Jul, Aug, Sep Oct, Nov, Dec

LONG TERM DATA

Long term average records are necessary for gap filling, data

characterization and for constraining various data processes, as well

as for long term phenology extraction.

The long term average can be used for constraining the gap filling

algorithm.

When using the long term averages to constrain the gap filling

algorithm preference is for the shorter periods (to minimize long term

biases), then longer periods are used depending on the algorithm

performance ( ± .5, 0.75 and 1.0 ).

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ND

VI

Day of Year

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VI

Day of Year

GAP FILLED

Where:

VIi is the vegetation index value of the known points

dij is the distance to the known point

VIj is the vegetation index value of the unknown

point

n is a user defined power parameter (often 1, 2 or 3)

in

ij

in

ij

i

j

d

d

VI

VI1

Spatial Gaps resulting from data filtering are filled, QA

labeled, and stored in the final data records. The Gap filling

algorithm uses 2 methods:

1. Linear Interpolation for the long term average data records

2. Inverse Distance Weighting for the daily and multi-day data

records

3. To eliminate outliers and minimize under/over performance of

the Gap filling algorithm we’re constraining the estimates by

the 5-year moving window of the long term average. One

standard deviation is used to restrict the boundaries of the

values. Values outside these boundaries are replace with the

longer term average value and properly labeled in the output

QA (Rank parameter).

GAP FILLED - COMPARISON

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ge

tati

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VI)

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VIFilled

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Piecewice's

Method

INPUT/OUTPUT

CONCLUSIONS

It is simpler and less computer intensive.

The method looks only at the data around the temporalgap which helps eliminate bias that may result frommethods that simultaneously need the full annual cycle,and

It balances between providing higher frequency andhigher quality data without the noise associated withdaily data while avoiding the excessive smoothing ofother methods.

Impacts phenology research, where change is measuredwith days over decades (so higher frequency makes adifference)

Better characterization of the VI Time Series improvesthe quantitative analysis. For example change in Carboncycling is measured in fraction of a percent and multi-daydata will not provide such accuracy

ACKNOWLEDGMENT

QUESTIONS?

Dr. Kamel Didan

Dr. Muluneh Yitayew

Armando Barreto