AATSR derived Land Surface Temperature from heterogeneous areas.

AATSR derived Land Surface Temperature from heterogeneous areas. Guillem Sòria, José A. Sobrino Global Change Unit, Department of Thermodynamics, Faculty of Physics, University of Valencia, Av Dr. Moliner, 50. 46100 Burjassot, Valencia, Spain. ABSTRACT In this paper we present a methodology to validate LST retrieved from AATSR data in heterogeneous areas. To this end, a set of algorithms based on Split-Window (SW) and Dual-Angle (DA) techniques has been proposed and evaluated for estimating land surface temperature (LST) from Advanced Along-Track Scanning Radiometer (AATSR) data at 11 and 12 µm wavelengths, using the angular viewing capability of the AATSR. The evaluation of the algorithms has been made by comparing in situ measurements of LST with the retrieved by applying the algorithms to AATSR data. The experimental data have been obtained in 2 experimental campaigns made in heterogeneous areas, in Marrakech, Morocco in the framework of WATERMED project, and in Barrax, a region of Albacete (Spain), in the framework of EAGLE and SPARC projects. The validation makes use of Landsat and Chris/Proba high spatial resolution images over the test sites. The results suggests applying LST algorithms over averaged AATSR images with a smoothing window filter of 2 x 2 pixels. Results show also that dual-angle algorithms could not be applied to these heterogeneous regions due to the different surfaces observed in the nadir and forward views. Further investigation is required to solve this problem in the overlap of AATSR nadir and forward pixels over heterogeneous areas. 1 INTRODUCTION Retrieval of Land Surface Temperature (LST) from space is of considerable importance for environmental studies, as described in [1] and [2]. In the last years, several theoretical studies have been carried out in order to develope LST algorithms through split-window (SW) [3], [4] and [5] and dual-angle (DA) methods, as [6] and [7]. The SW method uses observations at two different spectral bands within 10-12 µm spectral region to eliminate the influence of the atmosphere. The DA method uses observation of the same channel but under two different angles to exploit the different absorption path-lengths. The benefit of the SW method is based upon the fact that the atmospheric absorption of the surface radiation varies strongly with wavelength, and so, atmospheric effects can be corrected by using data from two different spectral channels. In the case of the DA method, the atmospheric transmission and emission of the atmosphere vary as a function of the viewing angle and allows the elimination of uncertainties due to the wavelength dependency. This confirms the advantage of the DA method in comparison with the SW method if the emissivity spectral variation and the emissivity angular variation are of the same order of magnitude. The Advanced Along-Track Scanning Radiometer (AATSR) sensor, as its predecessor sensors from the ATSR serie, includes an angular viewing capability to observe each point of the Earth s surface twice, first in a forward swath with a range of variation of zenith angle between 52.4º and 55º and secondly, after 120 seconds, a nadir swath with zenith angles from 0º to 26º. The instantaneous fields-of-view (IFOV) at the centre of the swath are 1 km by 1 km in the nadir view and 1.5 km by 2.0 km in the forward view. The AATSR was initially design to provide sea surface temperature (SST) maps. However nowadays AATSR data is being used more and more to obtain LST on a global scale. LST products require of additional considerations from the SST products. Over land the situation is much more complex due to the high heterogeneity of the land surface, besides the emissivity of land surfaces are much variable than sea surface emissivity and if emissivity and atmospheric effects are not correctly accounted for, errors up to 12 K may result in the retrieving LST. This heterogeneity implies that the validation of algorithms will be much more complicated over land than over sea surfaces. 2 ALGORITHM DEVELOPMENT The structure of the theoretical algorithms has been obtained from the radiative transference equation, considering at sensor the at-sensor radiance ( ) for a given wavelength (λ) as: L λ at sensor L ε B( λ, T ) + (1 ε ) L τ + L (1) atm atm λ = [ λ S λ λ ] λ λ where ε λ is the surface emissivity, B (λ, Ts) is, according to Planck s Law, the radiance emitted by a blackbody atm atm (BB) at temperature Ts of the surface, = (1-τ iθ ) B i (T a ) is the downwelling radiance, L λ = (1-τ i53 )B i (T a ) is L λ

the upwelling atmospheric radiance, τ λ is the total transmission of the atmosphere (transmissivity), Ta is the mean temperature of the atmosphere between the surface and the highest level where the information comes from and τ i53 is the total atmospheric path transmittance at 53 degrees. All these magnitudes depend on the observation angle. From Eq. 1 an algorithm involving temperatures can be obtained using a first-order Taylor series expansion of the Planck s law and writing the equation for i and j (i and j being two different channels observed at the same angle, SW method, or the same channel with two different observation angles, DA method): T s = T i +A(T i -T j )-B 0 +(1-ε i )B 1 - ε θ B 2 (2) where A and Bi are coefficients that depend on atmospheric transmittances, Ti and Tj are the radiometric temperatures for two different channels with the same view angle, SW method, or for the same channel with two different view angles, DA method, in accordance with [7]. In order to intercompare both dual-angle and split-window algorithms in the retrieving of surface temperature, it has been used the same mathematical structure for both methods. Reference [6] showed that the transmissivity of 11µm channel with a view angle of 53 degrees practically coincide with the transmissivity of the 12 µm channel at nadir view. So, a split-window algorithm using the 11 µm and 12 µm channels at nadir view is equivalent, in terms of atmospheric correction, to a dual-angle algorithm using the 11 µm channel at nadir and 53 degrees view angles [8]. The determination of the dual-angle and split-window coefficients has been made using MODTRAN simulations for 60 different radiosoundings extracted from the TOVS initial guess retrieval (TIGR) data base and 27 different emissivities, representative of the 90% of the Earth s landcover, obtained from the [9] according to [7]. The method used to minimize the objective function is the Levenberg-Marquardt method. Error theory has been applied to all of the algorithms studied. The errors considered are: the residual atmospheric error, σ mod, which gives an idea of the accuracy in the ST determination; the noise error (σ noise ) in the measurement process of the sensor assuming a noise temperature of 0.05 K for the AATSR channels (it should be noted that σ noise depends on the atmospheric water vapour content, a value of W=1 g cm -2 has been considered); the error associated with the water vapour column determination (σ W ), considering a water vapour content uncertainty of 0.5 g cm -2, that error has a dependence with (T 2n -T 1n ), ε n, and ε θ (to evaluate this error, we have taken for Ts the mean value of the database and we have chosen some representative values from the complete database for ε n and ε θ ); the error associated with the uncertainty in the value of the emissivity (σ ε ) is set at 0.005. The total error has been calculated considering the different errors. Table 1 shows the algorithms that provides the lower theoretical uncertainty values. Table 1. Numerical coefficients and errors for the Split-window and Dual-angle algorithms proposed. NAME ALGORITHM SW1: quad T s = T 11n + 0.61(T 11n -T 12n ) + 0.31(T 11n -T 12n ) 2 + 1.92 1.73 0.07 1.73 SW2: quad, ε T s = T 11n + 0.76(T 11n -T 12n ) + 0.30(T 11n -T 12n ) 2 + 0.10 + 51.2(1-ε) 1.39 0.07 0.18 1.40 SW3: quad, ε, ε SW4: (W), ε, ε, W SW5: quad, ε, ε, W SW6: quad(w), ε, ε, W T s = T 11n + 1.03(T 11n -T 12n ) + 0.26(T 11n -T 12n ) 2 0.11 + 45.23(1-ε) 79.95 ε T s = T 11n + (1.01+ 0.53W)(T 11n -T 12n ) + (0.4-0.85W) + (63.4-7.01W)(1-ε) - (111-17.6W) ε T s = T 11n + 1.35(T 11n -T 12n ) + 0.22(T 11n -T 12n ) 2 (0.82-0.15W) + (62.6-7.2W)(1- ε) - (144-26.3W) ε T s = T 11n + (1.97+0.2W)(T 11n -T 12n ) - (0.26-0.08W)(T 11n -T 12n ) 2 + (0.02-0.67W) + (64.5-7.35W)(1-ε) - (119-20.4W) ε σ mod σ noise σ ε σ WV σ total 1.05 0.09 0.59 1.20 0.59 0.10 0.83 0.45 1.12 0.93 0.11 1.06 0.20 1.43 0.52 0.15 0.89 0.37 1.10 DA1: quad T s = T 11n + 1.36(T 11n -T 11f ) + 0.18(T 11n -T 11f ) 2 + 1.78 1.31 0.11 1.32 DA2: quad, ε T s = T 11n + 1.56(T 11n -T 11f ) + 0.15(T 11n -T 11f ) 2-0.34 + 51.9(1- ε 11n ) 0.72 0.12 0.18 0.75 DA3: quad, ε, ε DA4: (W), ε, ε, W DA5: quad, ε, ε, W DA6: quad(w), ε, ε, W T s = T 11n + 1.57(T 11n -T 11f ) + 0.15(T 11n -T 11f ) 2 0.11 + 51.7(1- ε 11n ) 25.8 ε θ 0.69 0.13 0.26 0.74 T s = T 11n + (1.62+0.3W)(T 11n -T 11f ) + (0.18-0.52W) + (70.1 7.18W)(1-ε 11n ) - (35.4-3.67W) ε θ 0.47 0.13 0.35 0.36 0.70 T s = T 11n + 1.92(T 11n -T 11f ) + 0.12(T 11n -T 11f ) 2 (0.39+0.09W) + (71-7.55W)(1-ε 11n ) - (35.8-3.88W) ε θ 0.57 0.15 0.36 0.17 0.71 T s = T 11n + (2.67-0.07W)(T 11n -T 11f ) - (0.29-0.09W)(T 11n -T 11f ) 2 - (0.31+0.28W) + (72.5-7.9W)(1-ε 11n ) - (35.8 4.1W) ε θ 0.38 0.20 0.37 0.24 0.62

The algorithms are ranked according to their explicit dependence on linear difference of brightness temperature (Ti- Tj), quadratical difference of brightness temperature (quad, (Ti-Tj) 2 ), water vapour content (W), emissivity (ε) and spectral or angular emissivity difference ( ε). Table 1 shows that the error of the model is smaller when the algorithm has more degrees of freedom. Besides, dual angle algorithms give better accuracy than split window ones with the same mathematical structure. Moreover, water vapour dependent algorithms give better results than the other ones, even after including the effect of uncertainty in water vapour content error. This agrees with the observations reported by [10]. The results are quite similar for both dual-angle and split-window models when the simplest algorithm (less input parameters) is considered, however the differences increase when increasing the input parameters (see, for instance, algorithm type 6, where SW error doubles the DA one). 3 VALIDATION 3.1 Study Area In order to validate the AATSR LST algorithms, it has been obtained in situ measurements for surfaces with some requirements of heterogeneity and low topography. So, to achieve this validation, two different field campaigns have been made; the first one was selected near Marrakech, in Morocco, in the framework of the WATERMED project. This site was divided into three different areas. A large plot of bare soil, a mixed of vegetation and bare soil and a vegetated area of barley composed of several plots with a quite good homogeneity. The second site chosen for data collection was located in Barrax in the south of Spain 20 km away from Albacete city in the framework of the EAGLE and SPARC projects. The area around Barrax has been used for agricultural research for many years and is characterised by a flat morphology and large, uniform landuse units. Differences in elevation range up to 2 m only. Also in the Barrax area, plots of 3 different samples (Bare Soil, non-green/senescent vegetation and green vegetation) were selected to carry out the validation process. 3.2 Measurements In order to obtain an average radiometric temperature of every location, a set of transects were carried out with different thermal radiometers: CIMEL CE312 radiometers, EVEREST 3000 transducers and RAYTEK MID radiometers. The radiometric temperatures of each transect within 15 minutes of the satellite overpass were examined and average and standard deviation values were calculated. The emissivity of the locations was obtained by the Emissivity Box Method. The down-welling radiance was also measured during the satellite overpass. The water vapour content was measured all through the transects with a sun photometric instrumentation, a mean value of W = 1.11 ± 0.15 g cm -2 in the case of the Marrakech campaign and of W= 2.36 + 0.15 g cm -2 for the Barrax campaign were obtained. Table 2 summarizes the land surface temperatures for each class considered in both sites. Table 2. Averaged values of the surface temperature obtained from transect measurements. Campaign Date Overpass (UTC) Bare Soil Surface temperature Non-green/senescent vegetation Green Vegetation Marrakech 5 th March 2003 10:53 36.0 ± 1.5 34.1 ± 2.7 22.5 ± 1.4 Barrax 20 th July 2004 10:42 51.4 ± 1.6 39.5 ± 2.2 32.5 ± 0.6 3.3 Classification method Level1b AATSR products of the studied areas were acquired to test the LST obtained with SW and DA algorithms. The test area has a rectangular form and embraces an extent of 4 by 4 pixels in the AATSR image, in the case of Marrakech and 4 by 7 pixels in the case of Barrax. The pixel size in the nadir view is 1km by 1km. In order to apply the proposed algorithms, previously it is necessary to carry out a process of classification of the different sites that the AATSR pixels are made up of, as well as to achieve a statistic analysis of the proportion of every of this sites, with their particular values of temperature and emissivity. With this aim in mind, images of higher spatial resolution than AATSR images have been acquired. A Landsat 5 image of 15 th march 2003 for the study area of Marrakech, with a spatial resolution of 30m in the visible bands; and a CHRIS/PROBA (Compact High Resolution Imaging Spectrometer / Project for On Board Autonomy) image of 16 th july 2004 for Barrax site, with a spatial resolution of 36 m. CHRIS operates in 63 spectral bands over the visible/near infrared band. Its images are acquired at along track angles of 55 degrees, 36 degrees and near nadir angles. Thus, for each one of the AATSR pixels studied, it can be overstruck a minimum set of 1100 Landsat pixels (a polygon of about 33 pixels by side) and a set of 784 CHRIS pixels (a polygon of about 28 pixels by side).

These high resolution images have been classified through a supervised maximum likelihood classification method, taking 3 different classes (bare soil, green vegetation and non-green/senescent vegetation) as training endmembers, in order to know the proportion of each of the sites in the AATSR pixels. A statistical analysis has been carried out to obtain the proportion of the reference areas in every AATSR pixel. Figure 1 shows the classification over the Landsat 5 and CHRIS images. Fig. 1. Classification images of Marrakech (a) and Barrax (b). The images have been classified taking as training endmembers: bare soil (in red), green vegetation (in green) and non-green vegetaton (in blue) The in situ LST is obtained from the surface radiance by inversion of Plank s Law using the radiometric temperatures of each transect (considering from Eq. 1 τ λ =1 and L atm λ=0) and they were also corrected of the effects of the atmosphere and the emissivity by using the down-welling radiance and the values of the emissivity of every sample. These LST values have been compared (see Table 3) with the obtained from AATSR data from all the algorithms proposed in Table 1. Table 3 shows the standard deviation, mean value of the differences (bias) and the root mean square error () between SW LST and in situ LST of the algorithms, for both field campaigns, considering the individual pixels. This data show a better behaviour from the Marrakech campaign than from the values of the Barrax campaign. Barrax area is more heterogeneous than the Marrakech area due to the more variability of crops, and this heterogeneity has an impact in the retrieval of LST. Table 3. Validation of SW algorithms proposed considering individual pixels. Algorithm Marrakech Campaign Barrax Campaign SW1 n, Quad -2.2 1.2 2.5-0.2 3.8 3.8 SW2 n, Quad, ε -2.5 1.2 2.8-0.7 3.8 3.8 SW3 n, Quad, ε, ε -1.8 1.2 2.1-0.2 3.8 3.8 SW4 n (W), ε, ε, W -1.5 1.1 1.9-0.3 3.7 3.7 SW5 n, Quad, ε, ε, W -1.3 1.2 1.8 0.1 3.8 3.8 SW6 n, Quad (W), ε, ε, W -1.1 1.2 1.6-0.4 3.6 3.6 Besides, it is shown in Table 4 the results of the aplication of the SW algorithms in both the experimental campaigns, but now, in order to minimize the co-registration errors, a window filter of 2 x 2 pixels has been applied to the nadir image. The results show that this averaging process ameliorates the in both campaigns, specially for the Barrax campaign.

Table 4. Validation of SW algorithms proposed considering a window filter of 2 x 2 pixels. Algorithm Marrakech Campaign Barrax Campaign SW1 n, Quad -2.2 0.5 2.3 0.0 1.1 1.1 SW2 n, Quad, ε -2.5 0.5 2.6-0.5 1.3 1.4 SW3 n, Quad, ε, ε -1.8 0.5 1.8 0.0 1.3 1.3 SW4 n (W), ε, ε, W -1.5 0.5 1.6-0.1 1.2 1.2 SW5 n, Quad, ε, ε, W -1.3 0.5 1.4 0.3 1.3 1.3 SW6 n, Quad (W), ε, ε, W -1.1 0.5 1.2 0.2 1.1 1.1 The algorithms obtained by DA method incorporate the angular effects due to variation of the emissivity, water vapour content and radiometric temperature with the view angle. But this method supposed that the surface observed at different angles is the same, an assumption that is not always reliable. In the AATSR images, nadir pixels have a nominal size of 1.0 km x 1.0 km; instead of this, forward pixels have a size of 1.5 km x 2.0 km (according to the sensor specifications). In this way, when AATSR data is processed from Level 0 product to Level 1b product, pixels are re-gridded onto a regular grid of a nominal pixel size of 1 x 1 km 2, so, values of some pixels are allocated according to values of neighbour pixels. This effect would be negligible when evaluating DA algorithms in homogeneous surfaces because, in this case, it is not so important to distinguish the value of a pixel from the nearest one. However, in heterogeneous surfaces, like the analyzed in the present paper, it is of principal importance to know the area of each pixel; a forward pixel should cover the same area than the nadir pixel, instead, forward pixel embrace areas from the neighbouring nadir pixels. Fig. 2 displays this effect. 6 7 5 6 4 5 Tnadir - Tforward (ºC) 3 2 1 0 0 0.1 0.2 0.3 0.4 0.5 0.6 0.7-1 Tnadir - Tforward (ºC) 4 3 2 1 0-0.25-0.2-0.15-0.1-0.05 0 0.05 0.1 0.15 0.2 0.25-1 -2-2 -3 NDVI nadir -3 NDVI nadir - NDVI forward Fig. 2. a) Difference between nadir and forward brightness temperatures of AATSR channel 2 as a function of NDVI values in nadir, b) Brightness temperature difference between nadir and forward views for AATSR channel 2 versus nadir-forward NDVI differences. Fig. 2a shows a maximum difference for low NDVI values, this implies that nadir image presents a higher proportion of soil (higher temperature) than the forward image, where vegetation proportion increases. However, for high NDVI values (that correspond to vegetation) the difference in radiometric temperatures decreases. This behaviour would be in accordance with the observation geometry of the crops. In Fig. 2b, negative values on x axis correspond to areas of higher vegetation proportion in forward than in nadir views. These values are logical for incomplete canopies. As a consequence, for these areas, Tnadir is higher than Tforward. Here, it should be noticed the existence of negative radiometric temperature differences. These values mean that we are observing more bare soil surface (less NDVI) in forward view than in nadir view, which cannot be explained considering the structure of the crops. However, these values can be obtained for situations where the observation area for a given pixel is different in the forward and nadir views. Hence, forward view pixel will be contaminated by bare soil surface that does not appear in the nadir pixel. This could be due to the process of regridded to a 1km x 1km size of the forward pixels when Level 1b and Level 2 AATSR products are created.

4 CONCLUSIONS A set of dual-angle and split-window algorithms to estimate land surface temperature from AATSR data has been proposed. Its coefficients have been obtained from MODTRAN simulations. A supervised maximum likelihood classification method has been used to validate AATSR LST in heterogeneous sites. A thorough comparison using ground truth data shows a better than 1.7 K for the split-window algorithms proposed. When an averaging process is carried out over the AATSR images using a window filter of 2 x 2 pixels, the results are improved from 3.6 to 1.1 K. LST obtained from DA algorithms present a worse accuracy for heterogeneous surfaces due to the process of re-gridded where forward pixels of an AATSR image are rescaled from a spatial resolution of 1.5 km x 2 km to a 1km x 1km resolution. For incomplete canopies, the proportion of vegetation in relation to bare soil showed in a forward-view image is greater than the proportion showed in the nadir-view one. However, it has been observed situations where the proportion of bare soil increases in the forward-view images. Thus, it is very difficult to evaluate the proportion of each class in the same pixel, and therefore further investigation is required to solve this problem in the overlap of AATSR nadir and forward pixels over heterogeneous areas. 5 REFERENCES 1. Lagouarde, J. P., Kerr, Y. H. and Brunet, Y. (1995). An experimental study of angular effects on surface temperature for various plant canopies and bare soils, Agric. Forest Meteorol., 77, 167-190 2. Schmugge, T., French, A., Ritchie, J. C., Rango, A. and Pelgrum, H. (2002). Temperature and emissivity separation from multispectral thermal infrared observations, Remote Sens. Environ., 79, 189-198 3. Becker, F. and Li, Z.-L. (1990). Temperature-independent spectral indices in thermal infrared bands, Remote Sens. Environ., 32, 17-33. 4. Prata, A. J. (1993). Land surface temperatures derived from the AVHRR and ATSR, 1 Theory, J. Geophys. Res., 89D9, 16689-16702 5. Sobrino, J.A., Li, Z.-L., Stoll, M.P., and Becker, F. (1994). Improvements in the split-window technique for the land surface temperature determination. IEEE Trans. Geosc. and Remote Sens., Vol. 32, No. 2, 243-253 6. Sobrino, J.A., Li, Z.-L., Stoll, M.P., and Becker, F. (1996). Multi-channel and multi-angle algorithms for estimating sea and land surface temperature with ATSR data. International Journal of Remote Sensing, 17, 2089-2114. 7. Sobrino, J. A., Sòria, G. and Prata, A. J. (2004). Surface temperature retrieval from Along Track Scanning Radiometer 2 data: Algorithms and validation, J. Geophys. Res., 109, D11101, doi:10.1029/2003jd004212. 8. Prata, A.J. (1994). Land surface temperatures derived from the advanced very high resolution radiometer and the along-track scanning radiometer 2. Experimental results and validation of AVHRR algorithms. Journal of Geophysical Research, vol. 99, no. D6, 13025-13058 9. Salisbury J. W. and D Aria, D. M., (1992). Emissivity of terrestrial materials in the 8-14 mm atmospheric window, Remote Sensing of Environment, 42, pp. 83-106. 10. Jacob, F., Gu, X., Hanocq, J.-F. And Baret, F. (2003) Atmospheric corrections on single broadband channel and multidirectional airborne thermal infrared data. Application to the ReSeDA Experiment. International Journal of Remote Sensing, 24, 3269-3290.