Kamel Didan1*, Tomoaki Miura4 , Armando Barreto1

*1 The University of Arizona, 2 University of Hawaii

* didan@email.arizona.edu - http://measures.arizona.edu

Vegetation Phenology and Vegetation Index Products from Multiple Long Term Satellite Data RecordsPhase I : Towards Continuity

Introduction

Changes in vegetation phenology depict an integrated

response to change in environmental factors and provide

valuable information to global change research. Typically,

remote sensing of vegetation phenology is based on the

analysis of vegetation index temporal profiles, because of

their simplicity, stability, and inherent resistant to noise. Most

phenology estimates are, however, limited to using one

sensor owing to the inter-sensor continuity challenges.

Although, phenology is used for a variety of research and

application topics, the central objective remains the study of

vegetation dynamics change in response to change in climate

and other factors. Consequently, the consistency and length

of data records are key requirements.

With satellite missions lasting few years only, long term

phenology measures will have to be based on a mixture of

satellite data records.

Objectives

In this MEASURES' project, our multi-institution team of

investigators will generate a seamless and consistent sensor

independent long term (30+ years) Earth Science Data

Records (ESDRs) of land surface phenology parameters and

vegetation index, by using measurements from different

satellite missions and sensors. We’ll validate, characterize

and establish the error and uncertainty of these products .

Project Overview

We’re using AVHRR, MODIS, VIIRS and VGT daily land

surface reflectance and we’re designing a series of

sensor independent algorithms to apply to these data.

Validation

In collaboration with the National Phenology Network

(USA-NPN) we plan to correlate these remote sensing based

ESDR estimates of VI and phenology with ground

observations. We seek to evaluate their consistency and

accuracy by comparing them with in situ growing season

phenophases observations over different biomes, latitudinal

and elevation gradients.

Project flow Applications & Distribution

Changes in phenology provide valuable information for global

change research, land degradation studies, integrated pest and

invasive species management, drought monitoring, wildfire

risk assessment, and agricultural production.

This project is expected to generate, document, and deliver

30+ years of consistent and well characterized ESDR/CDR

quality daily measurements of Vegetation Index and land

surface phenology metrics. These ESDR products will be

distributed through the LP-DAAC, and separately via a PI

based interactive visualization and analysis system.

We plan to monitor the growing season along elevational and biotic

gradients. We will also leverage from ongoing national and international

efforts, particularly the ones lead by the US-NPN

Conclusions

We ‘re currently building more complex cluster maps and

we plan to generate separate continuity equations for each

of these clusters. The goal is then to reconstruct a new VI

record using these continuity equations. The phenology

algorithm will be driven by this VI ESDR.

Acknowledgements: This work is supported by a

NASA CA# NNX08AT05A, PI Kamel Didan

Others

VIIRS ?

One time data acquisitionOn demand data acquisition Automatic data acquisition

VITO

Global at 0.05o

Phenology and VI

ConterminousUS at 1km

Global daily CMG LSR (and

possibly continental US @ 1km)‏

from different sensors and

time periods

Discard

Data preprocessing and Quality Analysis

Surface Reflectance

Data QA& Reliability

VI ESDR

Annual PhenologyOutput QA

& Reliability

Phenology ESDR

Daily sensor independent and gap

filled VIOutput QA & Reliability

Product Evaluation Error & Accuracy

Fe

ed

ba

ck

Data disseminationWorkshops and Users feedback

Use

r co

mm

un

ity f

ee

db

ack

into

th

e p

roce

ssin

g a

nd

d

istr

ibu

tio

n s

yste

m

Discard/Re-order

VI and Phenology Processing Facility

(MEaSUREs Lab., The Univ of Arizona)‏

AVHRR

MODAPS

DAAC

Task-1

Task-2

Task-3/4/5/6

Task-7

Task-8

Task-9

Fe

ed

ba

ck

Fe

ed

ba

ck

Phenology QA& Reliability

USGS for EROS

DAACPI Web

Initial Data checks

1981 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2020

NOAA-7, -9, -11 AVHRR

(~1:30pm)

NOAA-14 AVHRR

(1:30pm at launch, 5:00pm at the end)

NOAA-16 AVHRR

(2:00pm)

NOAA-17 AVHRR

(10:00am)

SPOT-4 VEGETATION

(10:30am)

SPOT-5 VEGETATION

(10:30am)

Terra MODIS

(10:30am)

Aqua MODIS

(1:30pm)

NPP VIIRS

(1:30pm)

NPOESS VIIRS

(9:30am and 1:30pm)

Jan 1995 Sep 2001

Mar 2001

Aug 2002

Feb 2000

Jun 2002

Apr 1998

May 2002

Apr. 2008 (Launch to be pushed back)

Sep 2009*

Feb 2010

Sep 2003**

Sep 2008*

Jul 2007*

Dec 2002

Apr 2007*

Jul 1981

Sep 1994

Not RecommendedData Gap

Prior to 2000, only AVHRR Post 2000 many newer sensors

with varying characteristics.

Phase I : Continuity – The Cluster method

The cluster method, assumes that phenology (i.e. vegetation

dynamic) is controlled by factors that capture climate, soil,

elevation gradient, sun- shade exposure, and biophysical

limitations. The concept of phenology cluster is similar to

the biotic life-zones used to classify ecosystems. In that

regard, a phenology cluster is also a biotic zone, with similar

plant species, at around the same elevation gradient, and

governed by similar temperature, precipitation and radiation

regimes.

For this initial continuity phase we constructed a global

CMG resolution cluster map to drive the continuity work:

• Global DEM GTOPO30 data

• FAO Global Average annual Temperature

• FAO Global Precipitation map

• USDA-NRCS Global soil

• MODIS CMG Land Cover

Issues with:

Sensor/Platforms

• Bandpass

• Spatial resolution

• Orbital characteristics

• Sensor degradation

Algorithms/Re/Processing

• Atmosphere correction

• Temporal compositing

• Spatial aggregation

• VI formula

San Juan Mountains, CO

Wavelength (nm)

400 500 600 700 800 900 1000 1100

Norm

aliz

ed

Spectr

al R

espo

nse

Terra MODIS

NOAA-14AVHRR

NOAA-16AVHRR

ASTER

ETM+

M2 M3 I1 I2

SPOT-4VEGETATION

Global Temperature Map

Global Land Cover Map

Global DEM Map

Global Homogeneous Cluster Map (2000+units). VI continuity will be

established separately for each cluster.

Application of the cluster method to VI continuity

Global Soils Map

Global Precipitation Map

AVHRR 80ies AVHRR 90ies AVHRR 00ies AVHRR full record MODIS 00

Across sensor continuity: Clustered NDVI Decadal comparisons.

Data from most clusters exhibited robust continuity. Clusters from the

Rainforest and Boreal forest exhibited differences. These differences are due

to band pass, MODIS saturation of the Red/NIR (narrower bands) and

processing differences.

Using the clusters from above, the VI record is separated into data classes

and simple polynomial continuity continuity could be applied to the records.