Thermal Remote Sensing in Land Surface Processes - Chapter 3

Chapter 3 High spatial resolution mapping of surface energy balance components with remotely sensed data Karen Humes, Ray Hardy,William P. Kustas, John Prueger and Patrick Starks 3.1 Introduction 3.1.1 Background In order to better understand the exchange of heat and moisture between the land surface and lower atmosphere, it is important to quantify the compo-nents of the surface energy balance in a distributed fashion at the landscape scale. Remotely sensed data can provide spatially distributed information on a number of key land surface characteristics and state variables that control the surface energy balance. When combined with near-surface meteorologi-cal measurements and a relatively simple model, satellite and aircraft-based remotely sensed data can be used to create “maps” of spatially distributed surface 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 Rnet = G +H +LE (3.1) 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), andLEreferstothelatentheatflux(theenergyusedtotransfer water vapor from the surface to the atmosphere). The influence of the land surface energy fluxes on regional and global atmospheric 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 simulating land 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). High spatial resolution mapping 111 The use of these schemes within atmospheric models has helped to improve the performance of both regional and mesoscale atmospheric models. However, most of these models, referred to as soil–vegetation–atmosphere transfer (SVAT) models, require a priori knowledge of a considerable num-ber of surface parameters and detailed information for initialization. They also require pertinent ground data and substantial human effort for model calibration. Additionally, when complex point-scale models are run within the 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 based models 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 using high spatial resolution remotely sensed data, combined with driving mete-orological data from a ground network and a relatively simple model, to compute 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 uses satellite and ground data to estimate the fluxes at the time of the satellite overpass. Almost all the model parameters and variables used by the model (such as surface temperature, land cover type, and vegetation density) are estimated from remotely sensed data. The meteorological inputs required by the model were derived from a ground network. This approach has the advantage of being very “data driven” and the model does not need to be calibrated or “tuned” for a particular site. Thus, the fluxes estimated from this approach can be useful for validation or assimilation into more complex simulation 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 cover types than these previous efforts. Additionally, the watershed studied here has a special instrumentation network that makes possible more detailed spatial analysis of the factors influencing the surface fluxes. The motiva-tion for applying this model at relatively high spatial resolution over the watershed is twofold: (a) at higher spatial resolution the approach is more easilyvalidatedusingground-basedpointmeasurementsand(b)mappingthe fluxes at high spatial resolution allows an evaluation of the relative impor-tanceofvarioussurfaceandatmosphericvariablesindeterminingthesurface fluxes. 112 Humes et al. 3.2 Study area The USDA/Agricultural Research Service (ARS) Little Washita Watershed (LWW), operated by the ARS Grazinglands Research Station, is located in central Oklahoma. The land cover types present in the watershed include a mixture of cultivated areas (primarily winter wheat, soybeans, alfalfa, and corn), pastures with native grasslands and non-native species, varied man-agement practices, and (depending considerably on climatological variables that vary considerably from east to west) wooded areas. The LWW has also been the site of several special experimental campaigns involving the simultaneous 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 one field campaign in 1992 and three field campaigns in 1994 which included many 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 data sets 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 energy fluxes 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 the acquisition of ground data over a large, relatively uniform target area at the time of the Landsat satellite overpass. These data were used to vali-date the atmospherically corrected radiometric surface temperatures derived from 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 (1.8m) air temperature and relative humidity. These measurements were used for meteorological input to the model. The data sets used in this analysis were from the August 1994 field cam-paign on the CWW. A false color composite image from Landsat 5 Thematic Mapper (TM) data acquired on August 18, 1994, is shown in Figure 3.1. In this image, the data from the TM band 4 (near-infrared) are displayed as red, data from the TM band 3 (red) are displayed as green, and data from the TM band 2 (green) are displayed as blue. In August, the winter wheat fields 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 most extensive in the western portion of the watershed. The bright red areas of the image correspond to riparian vegetation along drainage areas, the relatively small watershed area corresponding to cultivated crops that are green at this High spatial resolution mapping 113 Figure 3.1 False color composite image from the LandsatTM sensor for the LWW from August 18,1994 (see Colour Plate XII). time of year (such as corn and alfalfa), and, to a lesser degree, the spatially extensive pastures of various density and species composition. In the early morning hours of August 18, a relatively intense thunder-storm moved through the watershed. The cumulus clouds that can be seen inFigure3.1, andthecirruscloudcontaminationoveraportionofthewater-shedevidentinthethermalband, wereremnantsfromthatstorm. Thesystem moved out of the watershed region approximately 1h before the image was acquired. 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 are done 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 LWW during the time of the Landsat TM overpass during the August 1994 field campaign. A diagram of model inputs and outputs is shown in Figure 3.2. The 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 solar 114 Humes et al. Model inputs/outputs Point-based meteorological data from Micronet stations Air temperature Landsat TM derived surface characteristics 30m RED and NIR reflectance from TM data Relative humidity Solar radiation 120m Atmospherically corrected radiometric surface temperatures 30m Land cover classification from TM data Kriging of each data parameter to Aggregate to 120m pixel Grids 120m pixel grids Norman two-source model Model output 120m Net radiation 120m Sensible heat flux 120m Ground heat flux 120m Latent heat flux 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 theAugust 18, 1994, Landsat scene over the LWW. radiationandassumedvaluesofsurfacealbedofordifferentlandcovertypes; (c) incoming longwave radiation is estimated using ground-based measure-ments of air temperature and relative humidity and an empirical expression for clear sky conditions (Idso 1981); (d) outgoing longwave radiation is computed using the surface temperature from the satellite data and an assumed emissivity of 0.98. It should be noted that for some “snapshot”-typemodelsforestimatingfluxes, surfacealbedoiscalculatedusingempirical functions that relate surface hemispherical albedo to reflectance in the finite wavebands of the Landsat TM sensor. This approach was not utilized in this application because of uncertainty in the atmospheric correction of the satellite data to absolute surface reflectance. The net radiation at the surface is partitioned between the soil and vege-tation layers using a typical “Beers law” formulation. The exponent in this relationship is controlled by an estimate of the fractional vegetation cover (which is estimated from remotely sensed data in the manner described in more detail below), and an assumption of spherical leaf inclination angle distribution. Soil heat flux is assumed to be a constant fraction (0.35) of the net radiation reaching the soil. The total sensible and latent heat fluxes are simply taken to be the sum of the vegetation and soil contributions. Those contributions are determined by doing a separate surface energy balance on the soil and vegetation lay-ers and assuming that the flux of heat from the soil and vegetation layers ... - tailieumienphi.vn