A Remote Sensing Approach for Estimating A Remote Sensing Approach for Estimating Regional Scale Surface MoistureRegional Scale Surface Moisture

Luke J. MarzenLuke J. Marzen

Associate Professor of GeographyAssociate Professor of Geography

Auburn UniversityAuburn University

Co-Director AlabamaViewCo-Director AlabamaView

Research funded by Alabama Water Resources Research Institute

AmericaView MembershipAmericaView Membership

http://www.geosociety.org/meetings/06drought/facthttp://www.geosociety.org/meetings/06drought/factsheet.pdfsheet.pdf

Maintain and enhance hydrologic and meteorologic data collection capabilities and existing data sets, and develop new data sets needed to improve assessments. Automate data collection to the maximum practical extent, and collect data at the frequency and spatial scale needed to support model analyses and decision-making. Fully fund and implement the National Integrated Drought Information System (NIDIS) passed by Congress in 2006.

Estimated that drought costs US $6-8 Estimated that drought costs US $6-8 billion annually (Wilhite, D.A. and M.D. billion annually (Wilhite, D.A. and M.D. Svodoba. 2000)Svodoba. 2000)

Meteorological droughtMeteorological drought is usually measured by is usually measured by how far from normal precipitation has been over how far from normal precipitation has been over a period of time.  a period of time. 

Agricultural droughtAgricultural drought occurs when soil moisture occurs when soil moisture is insufficient to meet crops’ needs to produce is insufficient to meet crops’ needs to produce an average crop.  It may occur in times of an average crop.  It may occur in times of average precipitation depending on soil types. average precipitation depending on soil types.

Hydrological droughtHydrological drought refers to deficiencies in refers to deficiencies in surface and subsurface water supplies. surface and subsurface water supplies.

N

CMI for Alabama (Sep 29 - Oct 12)

CMI for Alabama (Sep 29 - Oct 12)0.4150.416 - 0.860.861 - 1.071.071 - 1.5951.595 - 3.005

PDSI for Alabama (Sep 29 - Oct 12)-0.695-0.694 - 0.520.521 - 1.5651.566 - 1.691.691 - 1.715

N

PDSI for Alabama (Sep 29 - Oct 12)

ObjectiveObjective

Evaluate an approach to estimate surface Evaluate an approach to estimate surface moisture conditions using remote sensing moisture conditions using remote sensing at the regional scale at the regional scale

Scale methods down to field levelScale methods down to field level

Vegetation and EMRVegetation and EMR

small bodies in leaf that contains chlorophyllsmall bodies in leaf that contains chlorophyllAbsorbs blue and red light, reflects green and NIRAbsorbs blue and red light, reflects green and NIR

Normalized Difference Vegetation IndexNormalized Difference Vegetation Index (Red – NIR)/(Red + NIR)(Red – NIR)/(Red + NIR)

Values 0-1Values 0-1

Land Surface TemperatureLand Surface TemperatureThermal RSThermal RS

Past research using AVHRR has exploited the Past research using AVHRR has exploited the relationship between the Normalized relationship between the Normalized Vegetation Index and Land Surface Vegetation Index and Land Surface Temperatures to evaluate surface moisture Temperatures to evaluate surface moisture status (Nemani and Running, 1989)status (Nemani and Running, 1989)

LST and NDVI relationshipLST and NDVI relationship

During drier periods NDVI values fall and During drier periods NDVI values fall and vegetation canopy temperatures increasevegetation canopy temperatures increase

LST

NDVI

LST

NDVI

Drier conditions Less dry

LST - Land Surface TemperatureNDVI – Normalized Difference Vegetation Index

Data and MethodsData and Methods

-Use NDVI and LST MODIS products-Use NDVI and LST MODIS products-growing season of 2000-2003-growing season of 2000-2003

-Evaluate ratio of NDVI/LST as an indicator -Evaluate ratio of NDVI/LST as an indicator of surface moistureof surface moisture

-compare to ground-based indices-compare to ground-based indices

• Global coverage - 2330 km swath

• 36 channels - 2 @ 250m pixels, 5 @ 500m, 29 @ 1km

• various levels of processing

• EOS Validated products-MOD13, MOD11

The MODIS InstrumentThe MODIS InstrumentModerate Resolution Imaging SpectroradiometerModerate Resolution Imaging Spectroradiometer

Direct to PIWebsites

EOS DataGateway

Land ValidationHome Site

http://modis.gsfc.nasa.gov/cgi-bin/texis/organigram/weblinks

http://modis.gsfc.nasa.gov/cgi-bin/texis/organigram/weblinks

MOD13MOD13

NDVI NDVI composites composites uses “best uses “best value”value”

Both 250m Both 250m and 1kmand 1km

MOD11 Land Surface TemperatureMOD11 Land Surface Temperature

Shown to be Shown to be accurate accurate within 1 within 1 degree K degree K

Averaged 2 Averaged 2 8 day 8 day composites composites to match to match NDVINDVI

NDVI/LSTNDVI/LST

Crop Moisture IndexCrop Moisture Index Southeast Southeast

Regional Climate Regional Climate CenterCenter

Mean CMI was Mean CMI was compared to the compared to the mean of mean of NDVI/LST on a NDVI/LST on a Climate division Climate division basisbasis

NDVINDVI LSTLST

Table 1. Pearson's Product Correlations for Remotely sensed variables with CMI

Duration LST-CMI

NDVI-CMI

WSVI-CMI

April-May 2000

-0.73 0.305 0.53

June-July 2000

-0.69 0.035 0.04

Oct-00 -0.44 -0.110 -0.2

April-May 2001

-0.57 0.340 0.66

Oct-01 -0.41 -0.162 0.36

April-May 2002

-0.83 0.720 0.776

June-July 2002

-0.74 -0.560 -0.502

Oct-02 -0.66 0.259 0.77

April-May 2003

-0.44 0.085 0.45

June-July 2003

-0.57 -0.009 0.02

Oct-03 -0.55 0.228 0.43

Period 4 for entire southeastPeriod 4 for entire southeast

N=46r = 0.79****significant at .001

NDVI/LST CMI

ConclusionsConclusions

The ratio of NDVI/LST may provide an The ratio of NDVI/LST may provide an effective indicator of surface moisture effective indicator of surface moisture conditionsconditions

LST performed substantially better in our LST performed substantially better in our three year studythree year study

Future workFuture work

Economic StudyEconomic Study

local scale/field levellocal scale/field level

Atlas thermal sensor 1m resolutionAtlas thermal sensor 1m resolution

High crop yield = red Cool temps = red*not done by this geographer