Zhao-ang Li

Towards a local split window method over land surfaces

Abstract The split window method is successfully being used to retrieve the temperature over sea surfaces from satellite radiances in clear sky and has the great advantage of simplicity. However, such a method does not work over land surfaces, mainly because the emissivity is not equal to 1 and depends on the channel. An extension of this method to apply to land surfaces requires one to take account of emissivity—such an extension is presented in this paper. First, using Lowtran 6, the accuracies of the various linearizations of the radiative transfer equation leading to the split window are checked. This implies that the retrieved surface temperature depends linearly on emissivities and brightness temperatures. Such behaviour has been checked on actual examples. Theoretical equations are then derived which show that the actual surface temperature can again be expressed as a linear combination of the brightness temperatures measured in two adjacent channels with coefficients depending on spectral emissivi...

A physics-based algorithm for retrieving land-surface emissivity and temperature from EOS/MODIS data

The authors have developed a physics-based land-surface temperature (LST) algorithm for simultaneously retrieving surface band-averaged emissivities and temperatures from day/night pairs of MODIS (Moderate Resolution Imaging Spectroradiometer) data in seven thermal infrared bands. The set of 14 nonlinear equations in the algorithm is solved with the statistical recession method and the least-squares fit method. This new LST algorithm was tested with simulated MODIS data for 80 sets of hand-averaged emissivities calculated from published spectral data of terrestrial materials in wide ranges of atmospheric and surface temperature conditions. Comprehensive sensitivity and error analysis has been made to evaluate the performance of the new LST algorithm and its dependence on variations in surface emissivity and temperature, upon atmospheric conditions, as well as the noise-equivalent temperature difference (NE/spl Delta/T) and calibration accuracy specifications of the MODIS Instrument. In cases with a systematic calibration error of 0.5%, the standard deviations of errors in retrieved surface daytime and nighttime temperatures fall between 0.4-0.5 K over a wide range of surface temperatures for mid-latitude summer conditions. The standard deviations of errors in retrieved emissivities in bands 31 and 32 (in the 10-12.5 /spl mu/m IR spectral window region) are 0.009, and the maximum error in retrieved LST values falls between 2-3 K.

Quality assessment and validation of the MODIS global land surface temperature

This paper presents an evaluation of the Earth Observing System (EOS) Moderate Resolution Imaging Spectroradiometer (MODIS) thermal infrared bands and the status of land surface temperature (LST) version-3 standard products retrieved from Terra MODIS data. The accuracy of daily MODIS LST products has been validated in more than 20 clear-sky cases with in situ measurement data collected in field campaigns in 2000–2002. The MODIS LST accuracy is better than 1°C in the range from −10 to 50°C. Refinements and improvements were made to the new version of MODIS LST product generation executive code. Using both Terra and Aqua MODIS data for LST retrieval improves the quality of the LST product and the diurnal feature in the product due to better temporal, spatial and angular coverage of clear-sky observations.

A Review of Current Methodologies for Regional Evapotranspiration Estimation from Remotely Sensed Data

An overview of the commonly applied evapotranspiration (ET) models using remotely sensed data is given to provide insight into the estimation of ET on a regional scale from satellite data. Generally, these models vary greatly in inputs, main assumptions and accuracy of results, etc. Besides the generally used remotely sensed multi-spectral data from visible to thermal infrared bands, most remotely sensed ET models, from simplified equations models to the more complex physically based two-source energy balance models, must rely to a certain degree on ground-based auxiliary measurements in order to derive the turbulent heat fluxes on a regional scale. We discuss the main inputs, assumptions, theories, advantages and drawbacks of each model. Moreover, approaches to the extrapolation of instantaneous ET to the daily values are also briefly presented. In the final part, both associated problems and future trends regarding these remotely sensed ET models were analyzed to objectively show the limitations and promising aspects of the estimation of regional ET based on remotely sensed data and ground-based measurements.

Temperature-independent spectral indices in thermal infrared bands

Abstract In order to perform spectral analysis in the thermal infrared bands, temperature-independent thermal infrared spectral indices (TISI) are derived from observable thermal infrared radiances. Being temperature-independent and simply related to spectral emissivities of the observed surface, these indices are as easy to use to perform spectral analysis as reflectances in the visible and near infrared spectral domains. Examples of such indices are given for TIMS and AVHRR. Several properties of these indices are discussed, particularly their sensitivity to spectral emissivity variation, their efficient combination, and the derivation of relative spectral emissivity. These indices are quite complementary to NDVI and can lead to stronger results if used together rather than separately. For instance, in the examples worked out, these indices are more sensitive to bare soils characteristics than NDVI. Furthermore, these indices can be tailored to weight certain bands more heavily than others, giving to these indices a wide range of application. They may, for instance, be used for geologic compositional mapping. Practical examples from TIMS and AVHRR data are shown and briefly discussed with a possible applications to the determination from AVHRR data of the surface temperature and surface emissivity.

Feasibility of land surface temperature and emissivity determination from AVHRR data

Abstract The surface temperature and emissivity of natural media are very useful to know for many applications. In this article, we show that the scheme proposed in our previous work to retrieve both the spectral emissivities and land surface temperature from AVHRR data is feasible. From the concept of the temperature independent spectral index (TISI) in thermal infrared bands, we recall how the emissivities in Channels 3, 4, and 5 are obtained by combining day and night data, assuming that the TISI for Channels 3, 4, and 5 of AVHRR does not vary substantially between day and night, which has been checked for a particular data acquired in situ over the La Crau test site in Southeastern France. Once the emissivities in Channels 4 and 5 are known, the surface temperature can be determined using the local split-window method proposed in our previous work. In order to test the applicability of the method when atmospheric radiosoundings are not available, we discuss the effects of the errors generated by an approximate knowledge of the atmosphere on the retrieval of both emissivity and surface temperature. It is shown that if a radiosounding or a satellite sounding (from TOVS for instance) of the atmosphere is not available, the use of a climatic atmosphere leads to satisfactory results because the retrieval of emissivity is less sensitive to atmospheric uncertainties than the temperature itself. The method is applied to one AVHRR image over La Crau and results fit within situ measurements. The problems raised by this method and errors are then discussed, and further work to improve the method is suggested.

A Comparative Study of Land Surface Emissivity Retrieval from NOAA Data

Abstract A comparative study has been carried out on the most recent algorithms for the estimation of land surface emissivity (ϵ) using Advanced Very High Resolution Radiometer (AVHRR) data. Three of the algorithms are based on the Temperature-Independent Spectral Indices (TISI) concept using atmospherically corrected channels 3, 4, and/or 5, namely: (1) TISI BL , (2) TS-RAM, and (3) Δ day. The fourth is a simplified method based on the estimation of ϵ using atmospherically corrected data in the visible and near-infrared channels, called Normalized Difference Vegetation Index (NDVI) Thresholds Method (NDVI THM ). This method integrates a wide spectral data set of bare soil reflectivity measurements in the 0.4–14-μm band and uses different approaches in function of the NDVI value. All methods have been applied to the Iberian Peninsula using AVHRR/National Oceanic and Atmospheric Administration (NOAA-14) data during March 17th, 1997. In terms of emissivity, the results show that the difference between the NDVI THM method and the other methods is always positive, with a bias of less than 0.010 and a root mean square (rms) error of less than 0.010 when compared to the TISI BL method, a bias of 0.018 and an rms error of 0.020 when compared to the Δday method, and a bias of 0.025 and an rms error of 0.012 when compared to the TS-RAM method. In terms of land surface temperature, the NDVI THM method shows a bias of less than −0.4 K and an rms error of less than 1.0 K when compared to TISI BL , a bias of −1.0 K and a rms error of 1.3 K when compared to Δday, and a bias of −1.1 K and an rms error of 0.5 K when compared to TS-RAM. In conclusion, although the TISI BL is a precise method, it needs the AVHRR channel 3 for its application, which is not always available, for example, as in the AVHRR archives provided in the frame of the Pathfinder AVHRR Land project (more than 18 years of data). In this case we have shown that the NDVI THM method shows promising results and can be applied to obtain land surface temperature and emissivity from NOAA data without losing accuracy.

Land surface emissivity retrieval from satellite data

As an intrinsic property of natural materials, land surface emissivity (LSE) is an important surface parameter and can be derived from the emitted radiance measured from space. Besides radiometric calibration and cloud detection, two main problems need to be resolved to obtain LSE values from space measurements. These problems are often referred to as land surface temperature (LST) and emissivity separation from radiance at ground level and as atmospheric corrections in the literature. To date, many LSE retrieval methods have been proposed with the same goal but different application conditions, advantages, and limitations. The aim of this article is to review these LSE retrieval methods and to provide technical assistance for estimating LSE from space. This article first gives a description of the theoretical basis of LSE measurements and then reviews the published methods. For clarity, we categorize these methods into (1) (semi-)empirical or theoretical methods, (2) multi-channel temperature emissivity separation (TES) methods, and (3) physically based methods (PBMs). This article also discusses the validation methods, which are of importance in verifying the uncertainty and accuracy of retrieved emissivity. Finally, the prospects for further developments are given.

Improvements in the split-window technique for land surface temperature determination

Land surface temperature (LST) retrievals obtained from NOAA Advanced Very High Resolution Radiometer (AVHRR) are of considerable importance for climatic research. However, the accurate evaluation of LST from space has been severely limited because of the difficulty in separating atmospheric from surface effects as the surface cannot be modeled as a black-body radiator. With this goal in mind, a novel extension of the split-window technique is presented in which the atmospheric contribution to the radiance measured by the satellite is investigated by the ratioing of covariance and variance of the brightness temperatures measured in channels 4 and 5 of AVHRR/2. Furthermore, the contribution of emissivity is evaluated from coefficients that depend on the spectral emissivities in both thermal channels. Using a wide range of simulations from an atmospheric radiative transfer model it is shown that the proposed algorithm provides an estimate of LST, to within 0.4 K if the spectral surface emissivity is known, which is better than that given by the currently used split-window algorithms for LST determination. Also the limitations on algorithm accuracy are discussed considering different values of noise equivalent temperature. Finally the authors present the preliminary results obtained using the proposed method from AVHRR data over a semi-arid region-of Northwestern Victoria in Australia provided by CSIRO, and a mountainous region of Northeast of France acquired in the frame of Regio Klimat Projekt. >

Radiance-based validation of the V5 MODIS land-surface temperature product

This paper presents the procedure and results of the radiance‐based validation approach for the Moderate Resolution Imaging Spectroradiometer (MODIS) Land‐Surface Temperature (LST) product. Surface emissivity spectra were retrieved by a sun‐shadow method from surface‐leaving radiance spectra measured with a thermal infrared (TIR) spectroradiometer in the 3.5–14 µm spectral region under sunshine and sun‐shadow conditions. By using the measured surface emissivity spectrum and atmospheric profiles obtained by radiosonde balloons, and the LST values at validation sites in the V5 MODIS level‐2 LST products, radiative transfer simulations were made with the MODTRAN4 code to calculate the top‐of‐atmosphere (TOA) radiance values in MODIS band 31 (L31). By adjusting the LST input values in the simulations to match the calculated L31 values to the MODIS measured radiance (MOD L31) values, MOD L31 inverted LSTs can be obtained. The MODIS LST product was validated by comparison to the values of the MOD L31 inverted LSTs. This approach compares well with the conventional temperature‐based approach. The results of the radiance‐based validation indicate that the accuracy of the MODIS LST product is better than 1 K in most cases, including lake, vegetation and soil sites in clear‐sky conditions. The errors in the split‐window retrieved LSTs may be larger in bare soil sites and highly heterogeneous sites due to large uncertainties in surface emissivities. The results of the radiance‐based validation also reveal the weakness of the split‐window algorithm used for the generation of the MODIS LST product in two situations: one in cases where LSTs are larger than the air temperature at the surface level (T s‐air) by more than 16 K and the columnar water vapour (cwv) is larger than 1.5 cm, and another in cases under the influence of thin cirrus clouds or heavy aerosol loadings. These two situations were not considered in the development of the current MODIS LST algorithm.