Satellite Remote Sensing and Applications for Hydrometeorology

Satellite Remote Sensing and Applications for Hydrometeorology

Liming ZhouGeorgia Institute of Technology

200684

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Motivation?/73

Changes in ClimateGlobal mean surface temperature has risen by about 0.6C over the 20th century, with the largest increase in the past two decades (IPCC, 2001).Global land surface precipitation has increased significantly (by about 2%) over the 20th century (IPCC, 2001)./73Global mean surface temperature anomaly relative to 1951-1990

Land and Climate Interactionsbiogeophysical processes (water, energy, heat, momentum, carbon exchanges)biogeochemical processes (photosynthesis and respiration)

Understanding land-climate interactions is crucial to evaluate the future state of climate.

Land and climate are closely coupled. climateland/73

Presentation Overview

Response of Vegetation to Climate Changes

Impacts of Urbanization on Climate in China

Modeling Land-Climate Interactions/73

Topic I: Response of Vegetation to Climate ChangesEvidence for warming-enhanced plan growth in the north since 1980s/73(Zhou et al., JGR, 2001; 2003a) climateland

Have Climate Changes Promoted Northern Vegetation Growth? Pronounced warming in northern high latitudes

Earlier disappearance of snow in spring

Increased precipitation in northern high latitudes

Increased concentration of atmospheric CO2Changes in Climate Increased productivity through:

enhanced photosynthesis

enhanced nutrient availabilityChanges in Vegetation?/73

Satellite Remote Sensing Data/73Orbit type:Sun synchronous Orbit altitude: 833 kmData:1981-presentSpectral bands: 7Resolution: ~1.1 kmAVHRRMODISOrbit type: Sun synchronous Orbit altitude: 705 kmData: 2000-presentSpectral bands: 36Resolution: 0.25-1.0 km

Satellite Remote Sensing of VegetationGreenness Index:

Normalized Difference Vegetation Index (NDVI)

NDVI=(NIR-RED)/(NIR+RED)

RED NIR/73

Data

GIMMS 15-day composite 8 km NDVI and solar zenith angle (SZA) datasets (1981-1999)

Monthly stratospheric aerosol optical depth (AOD) (1981-1999)

Observed monthly land surface climate data (1981-1999)

NOAA precipitation: 2.5x2.5

GISS temperature: 2x2

A land cover map at 8 km resolution/73

Non-vegetation EffectsFactors that may contaminate long-term satellite measurements

calibration uncertainties (satellite drift and changeover) atmospheric effects (aerosol, vapor, etc) soil background effectsMethods that help reduce some non-vegetation effects

maximum NDVI compositing spatial and temporal aggregations empirical techniquesChanges in solar zenith angleChanges in aerosol optical depthEl ChichonMt. Pinatubo/73

Study Region

Vegetated pixels (defined by NDVI) between April to October

minimize the SZA effectreduce the soil background contribution (snow, barren and sparsely vegetated areas)study the same pixels in the entire analysis.Map of vegetated pixels at 8 km resolution/73

Changes in Vegetation ActivityChanges in vegetation photosynthetic activity can be characterized by

changes in growing season duration

changes in NDVI magnitude Increases in NDVI magnitudeLonger growing season IncreaseNDVI/73

Lengthening in Growing Season(Increased by 18 Days)(Increased by 12 Days)11.9 days/18 yrs (p

Increase in NDVI Magnitude(Increased by 8%)(Increased by 12%)8.4/18 yrs (p

Spatial Patterns of NDVI Changes/73

Continental Differences in WarmingThe greatest warming occurred during winter and spring.

Eurasia has an overall warming while North America hassmaller warming or cooling trends. /73

Consistency Between NDVI and Temperature/73

Statistical Results at Continental ScaleNote: T Temperature; EA Eurasia; NA North America/73

y

xIs there a statistically meaningful relation?y = 0 +1 x + y = 0 +1x+ 2 time + y =0+ 1 x + EA NDVIEA TyesyesyesNA NDVINA TyesyesyesEA NDVINA TnononoNA NDVIEA Tnonono

NDVIsummer=11Twinter+12T2winter+21Tspring+22T2spring+31Tsummer +32T2summer +41Pwinter+42P2winter+51Pspring+52P2spring +61Psummer +62P2summer +7SZAsummer+8AODsummer +summer + Model NDVI at Regional Scale

Data aggregated into 2x 2 boxes by seasons and vegetation types: 1430 boxes

T and P represented by a quadratic specification (a physiological optimum) with effects for earlier seasons.

SZA and AOD used to separate non-vegetation effects.

s estimated using statistical techniques from econometrics./73

Statistical Results at Regional ScaleNote: T - Temperature; P - Precipitation; AOD - Stratospheric aerosol optical depth; SZA - Solar zenith angle/73

Conclusions/73

Eurasia is photosynthetically more vigorous than North America during the past two decades:

Eurasia has a higher percentage of vegetated pixels (61% vs. 30%) showing a larger increase in the NDVI magnitude (12% vs. 8%) and a longer active growing season (18 vs. 12 days) than North America.

There is a statistically meaningful relationship between changes in satellite measured NDVI and those in observed surface air temperature at continental and regional scales.

These results suggest warming-enhanced plant growth in the north since 1980s, with important global-economic implications: warmer planet, greener north.

Topic II: Impacts of Urbanization on Climate in China

Evidence for a significant urbanization effect on climate in China /73(Zhou et al., PNAS, 2004) climateland

Rapid Urbanization in China

China has experienced rapid urbanization and dramatic economic growth since late 1978:

Its GDP increased at 9.5%, compared with 2.5% for developed countries and 5% for developing countries; The number of small towns soared from 2,176 to 20,312, nearly double of the world average;The number of cities increased from 190 to 663; The percent of urban population rose from 18% to 39%.

This could be a good case study to quantify the urbanization effect on Chinas regional climate./73

Rapid Urbanization - Beijing19782000(http://www.unep.org/geo/yearbook/yb2004/034.htm)/73

Rapid Urbanization - Shenzhen19881996(NASA Goddard Space Flight Center)/73

Diurnal Cycle of Surface Air Temperature/73

Maximum/Minimum Temperature, Diurnal Temperature Range (DTR) 0 Local Time 24TemperatureTminTmaxDTRDTR=Tmax-TminTmean=(Tmax+Tmin)/2

Urban Heat Island (UHI) Effect/73(Oke, Boundary layer climates, 1978)

Urban areas are often warmer than their surrounding rural areas, especially during the night

UHI warms Tmin more than Tmax and thus reduces DTR

Factors Contributed to UHI/73

Urban materials (e.g., concrete, glass, asphalt) have higher heat storage capacities and thus store heat during daytime and release it at night.

Urban areas have more waterproofing surfaces and less vegetation and thus have less evaporative cooling due to increased surface runoff and reduced moisture retention.

Increased surface roughness and reduced wind speed over urban areas trap more sunlight during daytime and keep more thermal emission during nighttime.

Increased anthropogenic heating releases from buildings and automobile and increased urban pollutants and aerosols trap thermal emission.

How to Estimate UHI?Observed-Reanalysis Temperature/73

NCEP/NCAR reanalysis temperatures are estimated from atmospheric values instead of surface observations and thus are insensitive to changes in land surface. (Kalnay and Cai, Nature, 2003)US average monthly temperature anomalies in 1990sobservedreanalysis

Choose Study Region and Season:Southeast China in Winter/73

Use an improved new reanalysis dataset and focus only on southeast China winter to ensure the best quality in both observational and reanalysis data by considering R-2 performance and Chinas complex topography and climate.

Study Region:

13 provinces/municipalities194 stationsmost of urbanization occurred

Data /73

Observed monthly mean daily maximum and minimum land surface air temperatures in China from 1979 to 1998.

NCEP/DOE AMIP-II Reanalysis (R-2): improved quality, especially for soil moisture and clouds; covering the period 1979-present when satellite data are available.

Interpolation of R-2 temperatures for each station based on its location (longitude and latitude) Calculations of monthly anomalies of maximum and minimum temperatures and DTR for each station for both R-2 and observed data.

Temperature Trends Due to UrbanizationWinter temperature trends (C/10yrs) from 1979 to 1998

Observed-reanalysis DTR trends explain 68% of the observed DTR trend /73

Consistency with Urbanization Index

If urbanization is responsible for the estimated temperature trends, changes in DTR should be correlated with those in urbanization index such as the percent of urban population. R=-0.77 (p < 0.01) at provincial level/73

Consistency with Satellite Measured Vegetation Loss

If urbanization is responsible for the estimated temperature trends, changes in Tmin should be correlated with changes in satellite measured greenness.Correlation at provincial levelSummer NDVI trends (1982-1998)/73

Conclusions

The estimated warming of mean surface temperature of 0.05C per decade in China is much larger than previous estimates for other periods and locations. (

Topic III: ModelingLand-Climate InteractionsApplication of remote sensing data to improve land surface processes in climate models /73(Zhou et al., GRL, 2003; 2005; Zhou et al., JGR, 2003b; 2003c; Tian et al., GRL, 2004;Tian et al., JGR, 2004a; 2004b)climateland

Climate Driving ForceEarths radiation and energy budget (IPCC, 2001)Land plays a significant role in determining the net radiation absorbed by the land surface and partitioning of sensible and latent heat fluxes released back to the atmosphere.albedoemissivity/73

Land-Climate Interactions in Climate Modelstemperature, precipitation, downward solar radiation, downward longwave radiation, etcatmospherelandland cover/use change Land Surface Parameterizations:albedo, emissivity, roughness, evapotranspiration, etcsensible heatlatent heatupward longwave radiationreflected solar radiationLand Surface Parameters:vegetation type/amount, soil properties, etcinput/73

Land Surface Parameters/73Vegetation Type: tree/grass/crop

Vegetation Amount:LAI (leaf area index)fractional vegetation coverectA model gridA treeGlobal land cover map

Land Surface Parameterizations/73Land surface parameterizations = f (, LAI,g)=f (LAI, g )moreLand surface parameters:LAI, vegetation fraction/type, ect describing exchanges of momentum, energy, andmass between atmosphere and land albedomore

Major ObjectivesTo find major deficiencies in climate models and to best improve them more accurate land surface parametersmore realistic land surface parameterizations

Here MODIS products were used to improve the NCAR Community Land Model (CLM).

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Satellite Remote Sensing Data/73Orbit type:Sun synchronous Orbit altitude: 833 kmData:1981-presentSpectral bands: 7Resolution: ~1.1 kmAVHRRMODISOrbit type: Sun synchronous Orbit altitude: 705 kmData: 2000-presentSpectral bands: 36Resolution: 0.25-1.0 km

Best Way to Improve Climate Modelssatellite measured radiationalbedo, emissivity, LAI,vegetation type/fraction, etc MODIS algorithmsLAI, vegetation type/fraction, etc albedo, emissivityland surface parameterizationsModeling frameworkMODIS frameworkmodel radiation

An optimal connection between models and observations is to ensure that climate models are able to reproduce MODIS observations./73

Topic III: Modeling Land-Climate Interactions/73Question 1: What are major deficiencies in climate model land surface parameters/parameterizations?

Question 2: How much could the climate simulations beimproved if MODIS-derived data are used?

Question 3: Whats the essential problem and how to best improve climate model albedo and emissivity schemes?

Inaccurate Land Surface Parameters:Leaf Area IndexLAI differences (MODIS-CLM2)CLM2 underestimates LAI by 0.5-1.5 globally over most areas.

CLM2 overestimates LAI by 0.5-1.5 over extra-tropical South America (winter), and eastern US and middle latitude Eurasia (summer)./73

Inaccurate Land Surface Parameters: Fractional Vegetation CoverPercent cover differences (MODIS-CLM2)CLM2 overestimates grass/crop fraction by 20~40% globally over most areas.

CLM2 underestimates the fractions of tree, shrub, and soil over most areas./73

Inaccurate Land Surface Parameterizations:AlbedoAlbedo differences (CLM-MODIS)

Significant model albedo biases occur over northern snow-covered vegetated surfaces and over arid/semiarid regions.

Model albedoes are consistent with the MODIS data for dense forests over snow-free regions. /73

Inaccurate Land Surface Parameterizations:EmissivityASTER/MODIS emissivitySoil emissivity = 0.96CLM2 emissivity

CLM2 uses a constant soil emissivity.

Satellite observed soil emissivity varies spatially over North Africa, ranging from 0.84 to 0.96./73

Conclusions

CLM2 underestimates LAI and percent cover of tree, shrub and soil, and overestimates percent cover of grass/crop globally over most areas compared to the MODIS data.

CLM2 has the largest albedo bias over snow-covered and sparely vegetated surfaces and the biggest emissivity bias over arid/semiarid regions./73

Improved Climate Simulationsat Global ScaleTemperature differences (CLM2MODIS CLM2AVHRR)WinterSummerTemperature bias (CLM2AVHRR-observed)/73

Improved Climate Simulationsat Regional ScaleEvapotranspiration over Amazon due to improved LAITemperature over Sahara due to improved emissivity/73

ConclusionsLand surface parameters created from MODIS data improve CLM2 by:

reducing most of the model cold bias over snow-covered regions.decreasing most of the model warm bias over snow-free regions.simulating more realistically ground evaporation and canopy evapotranspiration over tropical regions.

Some climate biases still remain. /73

Essential Problem?Problem: accuracy for horizontally homogeneous canopies but largest errors for semiarid and snow-covered vegetated surfacesSolution: a more realistic radiation model plus a more accurate boundary condition climate model view of vegetationwhat it looks like for semi-arid system

Climate models generally use two-stream radiation schemes to calculate albedos for vegetated surfaces.soil albedoshading effects/73

Step 1:

Develop more realistic new radiation schemes?

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New Albedo Scheme Based on MODISBRDF = f (, , LAI,)Assumptions: uniform leaf distribution, isotropic leaf scatteringalbedo = f (SZA, LAI,)/73

New Emissivity Scheme:Connecting Emissivity to AlbedoMODIS broadband albedoASTER emissivityR=-0.76/73

MODIS bands1234567Correlation (R)-.76-.74-.16-.52-.77-.77-.85

Conclusions

Current climate models only consider horizontally homogeneous vegetation and use a constant soil emissivity. This causes very serious errors for sparsely and snow-covered vegetated surfaces.

The tight connection between MODIS data and climate models provides a best way to establish more realistic albedo and emissivity schemes for the models.

These schemes help better characterize and model land-climate interactions./73

Step 2:

Develop more accurate soil albedos?

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Objective

Current climate models represent soil albedos by a limited number of prescribed values. Soil albedos

vary only by several soil colors globally have a near-infrared to visible albedo ratio of 2are independent of solar zenith angle

Such simple representation produces notable albedo biases over arid and semi-arid regionsTo develop a more accurate soil albedo dataset from MODIS for use in climate models/73

MODIS Albedo with 21 Parameters

MODIS has 7 spectral bands. Each band uses 3 parameters to represent direct and diffuse albedos:/73

Spectral-to-broadband conversions used to produce albedos for 3 broadbands: visible (0.4~0.7 m), near-infrared (0.7~5.0 m), and shortwave (0.4~5.0 m) In total, 21 parameters: 7 spectral bands x 3 parameters (iso, vol, geo )

Data

MODIS albedo parameters for 7 spectral bands averaged from high quality pixels in dust-free seasons from 2000 to 2005

21 parameters

1 km resolution

vegetated pixels excluded Example: True-color RGB image for parameter iso/73An image with 21 bands over 14 million pixels

Need a Simple Statistical Model

Further statistical analyses are more useful for MODIS data

to reduce the data redundancy

to segregate the data noise

to separate albedos into spatial patterns of large-scale, local-scale and noise

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MethodsMNF transformation

Minimum noise fraction (MNF) transformations MODIS data21 parametersfirst few MNF bandslargest variance and highest spatial coherence/73

EigenvaluesEigenvalues for the MNF bands /73

The first 7 MNF bands explain 99% of the total variance in MODIS dataMNF1: 76%MNF1-7: 99%

MNF-based Albedos

New albedos using MNF 1-7 bands to represent MODIS direct and diffuse albedos/73

Quantifying the performance of MNF-based method in representing MODIS albedos: MODIS albedos:21 parametersMNF-based albedos:1-7 parameters variance in MNF-based method R2 = ------------------------------------------- total variance in MODIS

MODIS vs MNF-based Albedos: Spatial PatternShortwave diffuse albedos at 10 km resolution/73

More local-scale variations described with more MNF bands

MODIS vs MNF-based Albedos: Scatter PlotsShortwave diffuse albedos at 10 km resolution/73

The first 7 MNF explains 99.9% of the total variance in MODIS data at 10 km resolutionTotal grids:151,520

MODIS vs MNF-based Albedos: R2/73

R2 increases with more MNF bands, coarser spatial resolution, and smaller solar zenith angleShortwave diffuse albedos

MODIS vs MNF-based Albedos: R2 Table/73R2 for MODIS spectral and broadband diffuse albedos (/10000)

Similar results for all other spectral and broadband albedos R2R2

Conclusions

A more realistic soil albedo dataset with high quality was created for use in climate models through MNF transformations of MODIS data.

Our statistical method is able to capture most of the MODIS albedo variance and extract large-scale albedo patterns from the original MODIS data while improving the data quality and reducing the number of parameters needed to represent the data./73

The End

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Satellite Remote Sensing and Applications for Hydrometeorology