Elsevier

Remote Sensing of Environment

Volume 148, 25 May 2014, Pages 16-27
Remote Sensing of Environment

Validation of remotely sensed surface temperature over an oak woodland landscape — The problem of viewing and illumination geometries

https://doi.org/10.1016/j.rse.2014.03.016Get rights and content

Highlights

  • We present a model that estimates the effects of land surface structure on LST.

  • It may be applied to any location with few prescribed parameters.

  • It is a useful tool for upscaling of in situ measurements for LST validation.

  • It is an effective tool for inter-comparison of LST from different sensors.

Abstract

Satellite retrieved values of Land Surface Temperature (LST) over structured heterogeneous pixels generally depend on viewing and illumination angles as well as on the characteristics of the land cover. Here we present a method to quantify such dependencies on land surface characteristics, sun illumination and satellite position. The method uses a geometric model to describe the surface elements viewed by an air-borne sensor and relies on parallel-ray geometry to calculate the projections of tree canopies and sunlit and shaded ground: these are considered as basic surface elements responsible for most of the spatial variability of LST. For a woodland landscape we demonstrate that modeling the fractions of these basic surface elements within the sensor field-of-view allows us to quantify the directional effects observed on satellite LST with sufficient accuracy.

Geometric models are an effective tool to upscale in situ measurements for the validation of LST over discontinuous canopies (e.g. forests). Here we present the application of a model to observations of brightness temperature from the LSA-SAF validation site in Évora (Portugal), an area of oak woodland, over the one-year period from October 2011 to September 2012. The resulting composite temperature is compared against LSA SAF LST products from the Spinning Enhanced Visible and InfraRed Imager (SEVIRI) onboard Meteosat as well as against MYD11A1/MOD11A1 (collection 5) products from the MODerate resolution Imaging Spectroradiometer (MODIS) onboard AQUA and TERRA. Comparisons with modeled ground LST show that SEVIRI LST has a bias of 0.26 °C and a RMSE of 1.34 °C, whereas MODIS LST (MYD11A1 and MOD11A1, collection 5) has a bias of − 1.54 °C and a RMSE of 2.37 °C. Both MODIS and SEVIRI LST are closer to in situ values obtained with the geometric model than to those obtained when disregarding the effects of viewing and illumination geometry. These results demonstrate the need to consider the directional character of LST products, especially for validation purposes over heterogeneous land covers. For the new MODIS LST product (MOD21), which is based on the Temperature-Emissivity Separation (TES) algorithm, comparisons with in-situ LST show an improved bias of − 0.81 °C and a RMSE of 1.48 °C (daytime values only). The TES based product presents lower emissivity values than those used for retrieving MYD11A1/MOD11A1 LST, which may partially explain the improved match with in-situ LST.

Discrepancies between LST retrievals obtained from different sensors, especially those on different orbits can also be partly explained by their viewing/illumination geometries. In this study the geometric model is used to correct LST deviations between simultaneous MODIS and SEVIRI LST estimations related to those effects. When the model is used to correct the variable MODIS viewing geometry there is a reduction in standard deviation of about 0.5 °C.

Introduction

Land Surface Temperature (LST) is an important climatological variable (Sellers, Hall, Asrar, Strebel, & Murphy, 1992) as well as a diagnostic parameter of land surface conditions. It plays an important role in the surface energy balance, and as such it has long been used to infer surface heat fluxes (Caparrini et al., 2004, Mannstein, 1987), soil moisture (Carlson, 1986, Nemani et al., 1993), evapotranspiration (Kustas & Norman, 1996) and vegetation properties (Lambin & Ehrlich, 1997), including vegetation hydric stress (Jackson, Idso, Reginato, & Pinter, 1981).

Remote sensing constitutes the most effective method to observe LST over large areas and on a regular basis. Satellite LST products generally rely on measurements within the atmospheric window in the thermal infrared (e.g., Li et al., 2013). As such, remote sensing retrievals of LST correspond to the directional radiometric temperature of the surface within the field of view of the sensor (e.g., Norman & Becker, 1995). The validation of LST retrievals is however not trivial, given its high variability in space and time, along with the anisotropic effects. Validation exercises are commonly performed through comparisons of LST against ground-based measurements or through a radiance-based method (e.g., Wan & Li, 2008). The latter involves using radiative transfer calculations to reconstruct top-of-atmosphere observations from the LST retrievals and assuming surface emissivity and atmospheric profiles are known. The former is usually performed over homogeneous areas such as lakes, deserts and dense or very homogeneous vegetation covers, where station measurements are representative of pixel scale values (Göttsche et al., 2013, Wan et al., 2002, Wan et al., 2004). For heterogeneous surfaces, however, validation can be much more complex as an effective upscaling of the ground measurements is needed (Guillevic et al., 2012).

The comparison of LST estimations obtained from sensors on-board different platforms provides useful insight on product consistency (e.g., Jiménez et al., 2012, Trigo, Monteiro, Olesen and Kabsch, 2008). There are, however, many possible sources of LST differences, and it is difficult to ascertain the actual accuracy of each retrieval. Discrepancies between LST products may be associated to differences (i) in the top-of-atmosphere measurements (sensor calibration, spatial resolutions), (ii) in the algorithm and auxiliary data used for atmospheric and surface emissivity correction, (iii) in cloud mask, and (iv) in angular anisotropy (e.g., Barroso et al., 2005, Pinheiro et al., 2006, Rasmussen et al., 2010). Furthermore, remotely sensed LST is a directional variable, unless some sort of compositing of observations from different viewing angles is performed. As such, hypothetical LST retrievals obtained for the same scene, using the same sensor, but at different viewing angles would likely produce different temperature values, depending on factors like surface type, soil characteristics and slope orientation relative to sun. Although surface structure exerts an important role on the temperature, due in particular to shadowing effects that result in a dependence of LST on the zenith and azimuth view angles, these effects are often disregarded. In validation exercises involving comparisons of LST estimations with in situ observations, or inter-comparisons of LST products, the viewing and illumination geometries should be taken into account.

The effects of viewing and illumination geometries are usually considered by means of geometrical–optical models that have been developed mainly to describe forests and other discontinuous canopies. They operate by assuming that the canopy may be described by an array of geometrical objects arranged in space according to some statistical distribution. The interception and reflection of radiation are computed analytically from geometrical considerations. For these models, the overall radiance at any angle is calculated as a weighted average of the radiances from each component (usually, sunlit and shaded background and sunlit and shaded canopy).

This study presents a geometrical model that allows estimating the projected areas of the different components using parallel-ray geometry to describe the illumination of a three-dimensional vegetation element and the shadow it casts. The proposed model not only allows the correction of LST differences between sensors associated with their viewing geometries, but it is also an effective means for the validation of satellite-derived LST with ground-based measurements.

This type of geometric-optical model has been used by several authors to solve radiative transfer problems associated with surface heterogeneities related to vegetation (Franklin and Strahler, 1988, Lagouarde et al., 1995, Li and Strahler, 1986, Li and Strahler, 1992, Ni et al., 1999, Strahler and Jupp, 1990), as well as in studies of surface temperature anisotropy (Minnis and Khaiyer, 2000, Pinheiro et al., 2006, Rasmussen et al., 2010, Guillevic et al., 2013). Instead of relying on a rigid analytical approach, the procedure developed here has the advantage of using a simple computational method to calculate the geometrical projections, while making very few a priori assumptions. The method consists of projecting a three-dimensional vegetation object onto a fine grid, which allows the use of any vegetation shape and size or the combination of different shapes and sizes.

The model is applied to in situ measurements of brightness temperature gathered at Évora validation site to obtain the ground temperature corresponding to any observation and illumination angles. The site is located in a region dominated by sparse canopies of evergreen oak trees (Southern Portugal; Kabsch, Olesen, & Prata, 2008). The resulting temperature is compared against LST data as obtained from the Spinning Enhanced Visible and Infrared Imager (SEVIRI) onboard Meteosat Second Generation (MSG) satellites (Trigo et al., 2011) and from the MODerate resolution Imaging Spectroradiometer (MODIS) onboard AQUA and TERRA (Salomonson, Barnes, & Masuoka, 2006). Finally, the geometric model is used together with in situ measurements to estimate and remove the LST differences between MSG and MODIS associated with the different viewing geometries.

Section snippets

Data and methods

This study concerns the validation of satellite LST products with in situ measurements collected at Évora validation site in Southern Portugal. The period under analysis spans from October 2011 to September 2012, although the data are limited to clear sky observations. All comparisons with ground data from Évora are for the LST estimations for the satellite pixel nearest to the station.

Model sensitivity to input parameters

The geometric model was used to perform a sensitivity study of in situ LST to the parameters listed in Table 1. Results show that the impact is highest for daytime observations during summer months (June–September). An increase/decrease of 5% in PTC would lead to cooling/warming of daytime LST of up to 1 °C between June and August and up to 0.5 °C in April–May. The impact is very small for the remaining months or at night-time. The variability in the canopy size has significantly lower impact

Satellite inter-comparison

The developed model may be also used to compare LST retrievals from different satellites. Here we compare MSG LST against MODSW (i.e., MOD11A1 and MYD11A1) LST retrievals overlapping in time over Évora. The dependence of the daytime LST differences between MSG and MODSW on MODIS viewing geometry has already been analyzed by Trigo, Monteiro, Olesen, and Kabsch (2008) and Guillevic et al. (2013). In order to further understand this effect, Fig. 8 displays the discrepancies between the two

Concluding remarks

This paper analyses the problem of comparing satellite estimations of Land Surface Temperature with in situ measurements taken at regions with sparse canopies, where we often have strong temperature differences between sunlit background, shaded background and tree crowns. For that purpose, we developed a procedure that allows estimating the impact of viewing and illumination geometries on LST observations from space, while at the same time keeping assumptions at a minimum. The methodology is

Acknowledgments

This work was carried out within the context of the LSA SAF project (http://landsaf.ipma.pt) funded by EUMETSAT (LSA SAF: CDOP-2 proposal).

References (49)

  • F. Caparrini et al.

    Variational estimation of soil and vegetation turbulent transfer and heat flux parameters from sequences of multisensor imagery

    Water Resources Research

    (2004)
  • T.C. Carlson

    Regional-scale estimates of surface moisture availability and thermal inertia using remote thermal measurements

    Remote Sensing Reviews

    (1986)
  • P. Dash et al.

    Land surface temperature and emissivity estimation from passive sensor data: Theory and practice-current trends

    International Journal of Remote Sensing

    (2002)
  • P. Dash et al.

    Optimal land surface temperature validation site in Europe for MSG

  • T.S. David et al.

    Water-use strategies in two co-occurring Mediterranean evergreen oaks: Surviving the summer drought

    Tree Physiology

    (2007)
  • J. Franklin et al.

    Invertible canopy reflectance modeling of vegetation structure in semiarid woodland

    IEEE Transactions on Geoscience and Remote Sensing

    (1988)
  • S.C. Freitas et al.

    Quantifying the uncertainty of land surface temperature retrievals from SEVIRI/Meteosat

    IEEE Transactions on Geoscience and Remote Sensing

    (2010)
  • F.J. García-Haro et al.

    Variable multiple endmember spectral mixture analysis (VMESMA)

    International Journal of Remote Sensing

    (2005)
  • A. Gillespie et al.

    A temperature and emissivity separation algorithm for Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) images

    IEEE Transactions on Geoscience and Remote Sensing

    (1998)
  • F.M. Göttsche et al.

    Validation of land surface temperature derived from MSG/SEVIRI with in situ measurements at Gobabeb, Namibia

    International Journal of Remote Sensing

    (2013)
  • P.C. Guillevic et al.

    Directional viewing effects on satellite land surface temperature products over sparse vegetation canopies — A multisensor analysis

    IEEE Geoscience and Remote Sensing Letters

    (2013)
  • G.C. Hulley et al.

    Generating consistent land surface temperature and emissivity products between ASTER and MODIS data for Earth science research

    IEEE Transactions on Geoscience and Remote Sensing

    (2011)
  • G.C. Hulley et al.

    Quantifying uncertainties in land surface temperature and emissivity retrievals from ASTER and MODIS thermal infrared data

    Journal of Geophysical Research-Atmospheres

    (2012)
  • R.D. Jackson et al.

    Canopy temperature as a crop water stress indicator

    Water Resources Research

    (1981)
  • Cited by (0)

    View full text