Thermal Remote Sensing in Land Surface Processes - Chapter 3 docx

the energy used to warm the near-surface soil layers, H refers to the sensible heat flux the energy used to transfer heat from the surface to the atmosphere, and LE refers to the latent h

High spatial resolution mapping

of surface energy balance

components with remotely

a number of key land surface characteristics and state variables that controlthe surface energy balance When combined with near-surface meteorologi-cal measurements and a relatively simple model, satellite and aircraft-basedremotely sensed data can be used to create “maps” of spatially distributedsurface energy balance components over a watershed Assuming no advec-tion of energy into an area, the simplest form of the surface energy balance

is given by

where Rnet refers to the net radiation balance, G refers to the soil heat flux (i.e the energy used to warm the near-surface soil layers), H refers to the

sensible heat flux (the energy used to transfer heat from the surface to the

atmosphere), and LE refers to the latent heat flux (the energy used to transfer

water vapor from the surface to the atmosphere)

The influence of the land surface energy fluxes on regional and globalatmospheric processes has become well recognized in the climate and meteo-rological modeling communities (e.g Avissar and Pielke 1989; Chen and

Avissar 1994; Betts et al 1996) This has given rise to the development

of quite a number of more sophisticated parameterizations for simulatingland surface processes within mesoscale and global atmospheric models

(Dickinson et al 1986; Sellers et al 1986; Entehkhabi and Eagleson 1989; Noilhan and Planton 1989; Avissar and Verstraete 1990; Xue et al 1991).

The use of these schemes within atmospheric models has helped to improvethe performance of both regional and mesoscale atmospheric models.However, most of these models, referred to as soil–vegetation–atmospheretransfer (SVAT) models, require a priori knowledge of a considerable num-ber of surface parameters and detailed information for initialization Theyalso require pertinent ground data and substantial human effort for modelcalibration Additionally, when complex point-scale models are run withinthe context of mesoscale or global atmospheric models, the grid cell reso-lution is generally on the order of hundreds to thousands of meters in size.Many of the key parameters and variables in the complex physically basedmodels would be expected to vary considerably within grid cells of that size.

3.1.2 Objectives ofthis study

The primary objective of this study is to demonstrate the feasibility of usinghigh spatial resolution remotely sensed data, combined with driving mete-orological data from a ground network and a relatively simple model, tocompute spatially distributed values of surface energy balance components.The model employed here is a relatively simple “snapshot” model That is,

it does not simulate any of the processes as a function of time; rather, it usessatellite and ground data to estimate the fluxes at the time of the satelliteoverpass Almost all the model parameters and variables used by the model(such as surface temperature, land cover type, and vegetation density) areestimated from remotely sensed data The meteorological inputs required

by the model were derived from a ground network This approach has theadvantage of being very “data driven” and the model does not need to becalibrated or “tuned” for a particular site Thus, the fluxes estimated fromthis approach can be useful for validation or assimilation into more complexsimulation models

The model was applied on a pixel-by-pixel basin across a watershed in

a sub-humid climate zone Although surface fluxes have been previously

mapped using these types of approaches (Moran et al 1990; Holwill and Stewart 1992; Humes et al 1997), this study represents the application

of a more complex (two-layer) model over more heterogeneous land covertypes than these previous efforts Additionally, the watershed studied herehas a special instrumentation network that makes possible more detailedspatial analysis of the factors influencing the surface fluxes The motiva-tion for applying this model at relatively high spatial resolution over thewatershed is twofold: (a) at higher spatial resolution the approach is moreeasily validated using ground-based point measurements and (b) mapping thefluxes at high spatial resolution allows an evaluation of the relative impor-tance of various surface and atmospheric variables in determining the surfacefluxes

3.2 Study area

The USDA /Agricultural Research Service (ARS) Little Washita Watershed(LWW), operated by the ARS Grazinglands Research Station, is located incentral Oklahoma The land cover types present in the watershed include amixture of cultivated areas (primarily winter wheat, soybeans, alfalfa, andcorn), pastures with native grasslands and non-native species, varied man-agement practices, and (depending considerably on climatological variablesthat vary considerably from east to west) wooded areas The LWW hasalso been the site of several special experimental campaigns involving thesimultaneous acquisition of ground and remotely sensed data The water-shed was a US Supersite for the SIR-C (Shuttle Imaging Radar) Experiments

in 1992 and 1994 The SIR-C experiments became the focal point for onefield campaign in 1992 and three field campaigns in 1994 which includedmany different ground measurements, as well the acquisition of many types

of remotely sensed data from ground, aircraft, and satellite-based sensors(Jackson and Scheibe 1993; Starks and Humes 1996) Remotely sensed datasets included passive microwave, active microwave, and optical sensors.Among the many special ground observations acquired during these cam-paigns were the measurement of surface energy fluxes by Bowen ratio and

eddy correlation techniques (Prueger 1996; Kustas et al 1999) These

ground-based measurements were used for validation of the surface energyfluxes produced by this modeling effort Observations also included ground-based radiometric measurements of surface reflectance and temperature.These were acquired with a backpack-type apparatus that facilitated theacquisition of ground data over a large, relatively uniform target area atthe time of the Landsat satellite overpass These data were used to vali-date the atmospherically corrected radiometric surface temperatures derivedfrom satellite data Additionally, the ARS operates the Micronet network

in the LWW, which consists of 42 monitoring stations on a 5-km grid.These stations record meteorological variables such as incoming solar radi-ation and near-surface (1.8 m) air temperature and relative humidity Thesemeasurements were used for meteorological input to the model

The data sets used in this analysis were from the August 1994 field paign on the CWW A false color composite image from Landsat 5 Thematic

cam-In this image, the data from the TM band 4 (near-infrared) are displayed asred, data from the TM band 3 (red) are displayed as green, and data fromthe TM band 2 (green) are displayed as blue In August, the winter wheatfields are typically bare and thus appear bluish green on the false color com-posite image It can be observed from the image that these areas are mostextensive in the western portion of the watershed The bright red areas of theimage correspond to riparian vegetation along drainage areas, the relativelysmall watershed area corresponding to cultivated crops that are green at thisMapper (TM) data acquired on August 18, 1994, is shown in Figure 3.1

Figure 3.1 False color composite image from the Landsat TM sensor for the LWW from

time of year (such as corn and alfalfa), and, to a lesser degree, the spatiallyextensive pastures of various density and species composition

In the early morning hours of August 18, a relatively intense storm moved through the watershed The cumulus clouds that can be seen

thunder-in Figure 3.1, and the cirrus cloud contamthunder-ination over a portion of the shed evident in the thermal band, were remnants from that storm The systemmoved out of the watershed region approximately 1 h before the image wasacquired

water-3.3 Model description and implementation

3.3.1 Model description

The model utilized here is described in detail in Norman et al (1995) It is a

two-source model, meaning that separate energy balance computations aredone for the soil and vegetation layers of the surface It was run on a pixel-by-pixel basis to compute spatially distributed energy fluxes over the LWWduring the time of the Landsat TM overpass during the August 1994 fieldThe conceptual model formulation is summarized here

The four components of net radiation are quantified as follows: (a)incoming solar radiation is a model input typically provided by ground mea-surements; (b) outgoing solar radiation is computing using incoming solarcampaign A diagram of model inputs and outputs is shown inFigure 3.2

August 18, 1994 (seeColour Plate XII)

30 m RED and NIR reflectance from TM data

30 m Land cover classification from TM data

Aggregate to 120 m pixel Grids Kriging of each data parameter to

120 m pixel grids

Norman two-source model

Model output

120 m Latent heat flux

120 m Ground heat flux

120 m Sensible heat flux

120 m Net radiation

Figure 3.2 Conceptual diagram of the input and output quantities used for the application

of the Norman et al (1995) model to data from the August 18, 1994, Landsat

scene over the LWW

radiation and assumed values of surface albedo for different land cover types;(c) incoming longwave radiation is estimated using ground-based measure-ments of air temperature and relative humidity and an empirical expressionfor clear sky conditions (Idso 1981); (d) outgoing longwave radiation iscomputed using the surface temperature from the satellite data and anassumed emissivity of 0.98 It should be noted that for some “snapshot”-type models for estimating fluxes, surface albedo is calculated using empiricalfunctions that relate surface hemispherical albedo to reflectance in the finitewavebands of the Landsat TM sensor This approach was not utilized inthis application because of uncertainty in the atmospheric correction of thesatellite data to absolute surface reflectance

The net radiation at the surface is partitioned between the soil and tation layers using a typical “Beers law” formulation The exponent in thisrelationship is controlled by an estimate of the fractional vegetation cover(which is estimated from remotely sensed data in the manner described inmore detail below), and an assumption of spherical leaf inclination angledistribution Soil heat flux is assumed to be a constant fraction (0.35) of thenet radiation reaching the soil

vege-The total sensible and latent heat fluxes are simply taken to be the sum ofthe vegetation and soil contributions Those contributions are determined

by doing a separate surface energy balance on the soil and vegetation ers and assuming that the flux of heat from the soil and vegetation layers

lay-act in parallel This gives rise to a simpler resistance formulation than

some multi-layer models (e.g Shuttleworth and Wallace 1985; Xue et al.

1991), but several studies have shown that for low to moderate vegetationcover, the various levels of complexity in resistance networks are indistin-guishable because air temperature gradients are small in the upper canopy(Norman and Campbell 1983) The key to estimating both contributions tothe sensible heat flux is in the decomposition of the radiometric surface tem-perature(Trad), derived from satellite observations, into soil and vegetation

components using

Trad(θ) = [f (θ)T n

c + (1 − f (θ))T n

where Tcis the vegetation canopy temperature, Tsis the soil surface

temper-ature, n is the power of the temperature and approximates the blackbody

function when considered over the entire wavelength of the sensor,θ is the view angle of the sensor, f (θ) is the fraction of the field of view of the

radiometer occupied by canopy and is given by

f (θ) = 1 − exp

−0.5Fcosθ

where fcis the fractional vegetation cover

The component surface temperatures and the turbulent flux componentsfor the soil and vegetation layers are derived using a series of steps that some-

times require iteration In the following equations, the symbols Rnet,c, Hc,

and LEcrefer to the canopy portion of the net radiation, sensible, and latent

heat fluxes, respectively, and the symbols Rnet,s, Hs, and LEs refer to thesoil contribution to the net radiation, sensible, and latent heat fluxes First,

an approximation for the canopy portion of the latent heat flux is estimatedusing a Priestly and Taylor (1972) type formulation with the canopy portion

of the net radiation:

where fg is the fraction of the vegetation cover which is green, s is the

slope of the saturation vapor pressure versus temperature curve, γ is the

psychrometric constant(0.66 kPa C−1).

The sensible heat flux of the canopy layer is then computed as the residual

in the energy balance for the canopy layer:

The canopy temperature is then estimated by inverting the equation for asimple resistance formulation for sensible heat flux from the canopy:

where Tc is the surface temperature of the canopy, Tair is the near-surface

air temperature and rahis the aerodynamic resistance The formulation forthe aerodynamic resistance is derived from the diabatically corrected logtemperature profile equations (Brutsaert 1982) The roughness lengths used

in the calculation of aerodynamic resistance were set according to the landcover type as shown in Table 3.1

Using this approximation for Tc and the satellite measurement of Trad,

equation (3.2) is used to solve for Ts, the soil temperature This value of Ts

is used to calculate the soil contribution to sensible heat flux using a bulkresistance formulation for the soil layer, given by

Hs= ρCp (Ts− Tair)/(rah+ rs) (3.8)

where rs is the soil-surface resistance as derived in Norman et al (1995).

The soil component of latent heat flux is then computed as the residual inthe soil energy balance:

If the soil evaporation which results from this calculation is less than zero,

then LEsis set equal to zero and Hsis recomputed using equation (3.9), Ts

Table 3.1 Roughness length (Z0), canopy height (h), and albedo

for each land cover type

length (m)

Canopy height (m)

Pasture – moderate density 0.0625 0.5 0.15

Pasture – less dense 0.0625 0.5 0.175

Sparse or senescent 0.0125 0.1 0.20

Bare soil and wheat stubble 0.01 0.1 0.15

is recomputed using equation (3.8), new values of Tcand Hcare computed

using equations (3.2) and (3.7), respectively, and a new value of LEc iscomputed using equation (3.6)

One advantage of this model formulation over other resistance-based mulations and more complicated SVAT schemes is that it does not require anestimate of the canopy surface resistance to evaporation Since this quantity

for-is highly spatially variable, very dynamic in time, and not readily obtainedfrom remotely sensed data, a formulation that can reliably estimate surfacefluxes without the use of this parameter can be more readily applied to newareas and larger spatial scales

3.3.2 Inputs derived from ground data

As discussed above, the meteorological inputs required for the data include:incoming solar radiation, near-surface air temperature, relative humidity,and windspeed Spatially distributed values for the near-surface air temper-ature (1.8 m above the surface) and incoming solar radiation are shown infrom the USDA/ARS Micronet network of 42 stations located across thewatershed, shown on the maps The point data correspond to the data fromthe 5-min averaging interval closest in time to the satellite overpass time of

Air temperature

Little Washita Watershed

Micronet station Air temperature (°C) 30–30.4 30.5–30.9 31–31.4 31.5–31.9 32–32.4 32.5–32.9 33–33.4 33.5–33.9 34–34.4 34.5–34.9 35–35.4 35.5–35.9 August 18, 1994

1640 UTC

5,000 m

Figure 3.3 Gridded field of air temperature 2 m above the surface interpolated from

Figures 3.3 and3.4, respectively These maps were derived using point data

measurements at Micronet stations (seeColour Plate XIII)

1640 UTC

5,000 m

Figure 3.4 Gridded field of incoming solar radiation measurements interpolated from

approximately 1640 UTC A universal kriging algorithm was used for spatialinterpolation between the point measurements

The Micronet does not include measurements of windspeed, which arerequired for model calculations of aerodynamic resistance To obtain areasonable watershed-wide average value of windspeed, data from fourOklahoma Mesonet stations were used The Oklahoma Mesonetwork is

a state-wide monitoring network consisting of 112 stations that providemeasurements of meteorological and surface variables at 5-min intervals.Four of the Mesonet stations are located just outside the boundaries of thewatershed Values of the windspeed (at 9 m above the surface) and relativehumidity from these four stations were averaged to compute a watershed-wide average for these variables for the 5-min interval closest to the satelliteoverpass time

3.3.3 Inputs derived from remotely sensed data

Radiometric surface temperature

One of the key inputs to the model is the radiometric surface temperature,

in this case derived from TM Band 6 (bandpass 10.9–12.3µm) Data from

measurements at Micronet stations (seeColour Plate XIV)

the Landsat thermal band were corrected for atmospheric effects by running

the radiative transfer code Lowtran 7 (Kniezys et al 1988) Atmospheric

temperature and water vapor profiles from on-site radiosonde data acquired

by a team from the National Severe Storms Lab at the time of the satelliteoverpass was used as input to the radiative transfer model The resultingcorrections were applied on a pixel-by-pixel basis across the scene Theradiometric temperature of a large ground target area was measured atthe time of the satellite overpass with instruments mounted on two back-pack type apparatuses The satellite pixels that most closely corresponded

to this large target area were extracted from the scene and compared with theground-based temperature measurement The TM-derived temperature wasslightly higher than the ground-based temperature (approximately 1.5◦C).The ground radiometric measurements and radiosonde measurements weremade just adjacent to one another at a site near the center of the watershed.The map of surface temperature for the watershed is shown in Figure 3.5.The cool areas in the east-central portion of the image correspond to contam-ination by cirrus clouds, and the cool spots in the far southern and westernportions of the watershed correspond to cumulus clouds and shadows

Corrected radiometric surface temperature

Little Washita Watershed

30–30.5 31.1–31.5 31.6–32 32.1–32.5 32.6–33 33.1–33.5 33.6–34 34.1–34.5 34.6–35 35.1–35.5 35.6–36 36.1–36.5 36.6–37 37.1–37.5 37.6–38 38.1–38.5

5,000 m

Figure 3.5 Atmospherically corrected radiometric surface temperature derived from a

Landsat 5 TM scene acquired over the Little Washita Watershed on August 18,

1994 The dark areas in the east-central portion of the image corresponds tocontamination by cirrus clouds, and the dark spots in the far southern and west-ern edges of the watershed correspond to contamination by cumulus clouds(seeColour Plate XV)

Land use land cover

Little Washita Watershed

5,000 m 5,000

August 18, 1994

0

Land cover Bare soil Clouds and shadows Crops

Pasture – dense Pasture – less dense Pasture – moderate Roads and urban Sparse or senescent Water Woodland

Figure 3.6 Land cover map derived from the unsupervised classification of data from six of

Land cover type

Information on land cover type across the watershed was derived from anunsupervised classification run on the six solar reflectance bands of theLandsat scene The classes were identified and merged based on approxi-mately 15 sites for which vegetation characteristics and density were known

A separate set of 197 points of known land cover type were used to assessthe accuracy of this classification, and 81% of these points were accuratelyclassified The land cover map was derived at the original 30-m pixel reso-lution for the TM reflective bands, then aggregated to 120-m resolution tomatch the resolution of the thermal band data The aggregation procedureassigned the land cover type that occupied the majority of the area of the120-m pixels The resulting map is shown in Figure 3.6 This land covermap was used to assign a number of surface characteristics for individualpixels, namely the albedo, canopy height, and surface roughness The values

Vegetation cover

The data from Landsat TM Band 3 (0.63–0.69 µm) and Band 4 (0.76–

0.90µm) were converted to exoatmospheric reflectance using the calibration

coefficients and solar irradiance data given by Markham and Barker (1986)

of these parameters used in this analysis are given inTable 3.1

the Landsat TM bands from the August 18, 1994, image (seeColour Plate XVI)