Arnon Karnieli

A mono-window algorithm for retrieving land surface temperature from Landsat TM data and its application to the Israel-Egypt border region

Remote sensing of land surface temperature (LST) from the thermal band data of Landsat Thematic Mapper (TM) still remains unused in comparison with the extensive studies of its visible and near-infrared (NIR) bands for various applications. The brightness temperature can be computed from the digital number (DN) of TM6 data using the equation provided by the National Aeronautics and Space Administration (NASA). However, a proper algorithm for retrieving LST from the only one thermal band of the sensor still remains unavailable due to many difficulties in the atmospheric correction. Based on thermal radiance transfer equation, an attempt has been made in the paper to develop a mono-window algorithm for retrieving LST from Landsat TM6 data. Three parameters are required for the algorithm: emissivity, transmittance and effective mean atmospheric temperature. Method about determination of atmospheric transmittance is given in the paper through the simulation of atmospheric conditions with LOWTRAN 7 program. A practicable approach of estimating effective mean atmospheric temperature from local meteorological observation is also proposed in the paper when the in situ atmospheric profile data is unavailable at the satellite pass, which is generally the case in the real world especially for the images in the past. Sensitivity analysis of the algorithm indicates that the possible error of ground emissivity, which is difficult to estimate, has relatively insignificant impact on the probable LST estimation error i T, which is sensible to the possible error of transmittance i 6 and mean atmospheric temperature i T a . Validation of the simulated data for various situations of seven typical atmospheres indicates that the algorithm is able to provide an accurate LST retrieval from TM6 data. The LST difference between the retrieved and the simulated ones is less than 0.4°C for most situations. Application of the algorithm to the sand dunes across the Israel-Egypt border results in a reasonable LST estimation of the region. Based on this LST estimation, spatial variation of the interesting thermal phenomenon has been analysed for comparison of LST difference across the border. The result shows that the Israeli side does have significantly higher surface temperature in spite of its denser vegetation cover than the Egyptian side where bare sand is prevalent.

Absorption of sunlight by dust as inferred from satellite and ground‐based remote sensing

Dust absorption of solar radiation is not well known due to limitations in the accuracy of in situ measurements. Here we report two new independent remote sensing techniques that provide sensitive measurements of dust absorption. One uses satellite spectral measurements, the second ground based sky measurements. Both techniques demonstrate that Saharan dust absorption of solar radiation is several times smaller than the current international standards. For example, at wavelength of 0.64 µm the dust single scattering albedo is reported here as 0.97±0.02 rather than 0.87±0.04 in recent review.

Mapping of several soil properties using DAIS-7915 hyperspectral scanner data—a case study over clayey soils in Israel

The data acquired from the hyperspectral airborne sensor DAIS-7915 over Izrael Valley in northern Israel was processed to yield quantitative soil properties maps of organic matter, soil field moisture, soil saturated moisture, and soil salinity. The method adopted for this purpose was the Visible and Near Infrared Analysis (VNIRA) approach, which yields an empirical model for predicting the soil property in question from both wet chemistry and spectral information of a representative set of samples (calibration set). Based on spectral laboratory data that show a significant capability to predict the above soil properties and populations using the VNIRA strategy, the next step was to examine this feasibility under a hyperspectral remote sensing (HSR) domain. After atmospherically rectifying the DAIS-7915 data and omitting noisy bands, the VNIRA routine was performed to yield a prediction equation model for each property, using the reflectance image data. Applying this equation on a pixel-bypixel basis revealed images that described spatially and quantitatively the surface distribution of each property. The VNIRA results were validated successfully from a priori knowledge of the area characteristics and from data collected from several sampling points. Following these examinations, a procedure was developed in order to create a soil property map of the entire area, including soils under vegetated areas. This procedure employed a random selection of more than 80 points along non-vegetated areas from the quantitative soil property images and interpolation of the points to yield an isocontour map for each property. It is concluded that the VNIRA method is a promising strategy for quantitative soil surface mapping, furthermore, the method could even be improved if a better quality of HSR data were used.

Use of NDVI and Land Surface Temperature for Drought Assessment: Merits and Limitations

Abstract A large number of water- and climate-related applications, such as drought monitoring, are based on spaceborne-derived relationships between land surface temperature (LST) and the normalized difference vegetation index (NDVI). The majority of these applications rely on the existence of a negative slope between the two variables, as identified in site- and time-specific studies. The current paper investigates the generality of the LST–NDVI relationship over a wide range of moisture and climatic/radiation regimes encountered over the North American continent (up to 60°N) during the summer growing season (April–September). Information on LST and NDVI was obtained from long-term (21 years) datasets acquired with the Advanced Very High Resolution Radiometer (AVHRR). It was found that when water is the limiting factor for vegetation growth (the typical situation for low latitudes of the study area and during the midseason), the LST–NDVI correlation is negative. However, when energy is the limiting fact...

Size distribution and scattering phase function of aerosol particles retrieved from sky brightness measurements

Ground-based measurements of the solar transmission and sky radiance in a horizontal plane through the Sun are taken in several geographical regions and aerosol types: dust in a desert transition zone in Israel, sulfate particles in Eastern and Western Europe, tropical aerosol in Brazil, and mixed continental/maritime aerosol in California. Stratospheric aerosol was introduced after the eruption of Mount Pinatubo in June 1991. Therefore measurements taken before the eruption are used to analyze the properties of tropospheric aerosol; measurements from 1992 are also used to detect the particle size and concentration of stratospheric aerosol. The measurements are used to retrieve the size distribution and the scattering phase function at large scattering angles of the undisturbed aerosol particles. The retrieved properties represent an average on the entire atmospheric column. A comparison between the retrieved phase function for a scattering angle of 120°, with phase function predicted from the retrieved size distribution, is used to test the assumption of particle homogeneity and sphericity in radiative transfer models (Mie theory). The effect was found to be small (20%±15%). For the stratospheric aerosol (sulfates), as expected, the phase function was very well predicted using the Mie theory. A model with a power law size distribution, based on the spectral dependence of the optical thickness, a, cannot estimate accurately the phase function (up to 50% error for λ = 0.87 μm). Before the Pinatubo eruption the ratio between the volumes of sulfate and coarse particles was very well correlated with α. The Pinatubo stratospheric aerosol destroyed this correlation. The aerosol optical properties are compared with analysis of the size, shape, and composition of the individual particles by electron microscopy of in situ samples. The measured volume size distributions before the injection of stratospheric aerosol consistently show two modes, sulfate particles with rm 0.7 μm. The “window” in the tropospheric aerosol in this radius range was used to observe a stable stratospheric aerosol in 1992, with rm ∼ 0.5 μm. A combination of such optical thickness and sky measurements can be used to assess the direct forcing and the climatic impact of aerosol. Systematic inversion for the key aerosol types (sulfates, smoke, dust, and maritime aerosol) of the size distribution and phase function can give the relationship between the aerosol physical and optical properties that can be used to compute the radiative forcing. This forcing can be validated in dedicated field experiments.

Climatology of dust aerosol size distribution and optical properties derived from remotely sensed data in the solar spectrum

Simultaneous spectral remote observations of dust properties from space and from the ground create a powerful tool for the determination of ambient dust properties integrated on the entire atmospheric column. The two measurement methods have a complementary sensitivity to variety of dust properties. The methodology is demonstrated using spectral measurements (0.47-2.21 mm) from Landsat TM over the bright Senegalian coast and dark ocean, and Aerosol Robotic Network (AERONET) radiances measured in several locations. We derive (1) the dust size distribution, showing a dominant coarse mode at 1-5 mm and a secondary mode around 0.5 mm effective radius; (2) dust absorption, which is found to be substantially smaller than reported from previous measurements; (3) the real part of the refractive index which varies within the range 1.53- 1.46; and we show that (4) the effect of the dust nonspherical shape on its optical properties is not significant for scattering angles ,1208.

Derivation of split window algorithm and its sensitivity analysis for retrieving land surface temperature from NOAA-advanced very high resolution radiometer data

Retrieval of land surface temperature (LST) from advanced very high resolution radiometer (AVHRR) data is an important methodology in remote sensing. Several split window algorithms have been proposed in last two decades. In this paper we intend to present a better algorithm with less parameters and high accuacry. The algorithm involves only two essential parameters (transmittance and emissivity). The principle and method for the linearization of Plancks radiance equation, the mathematical derivation process of the algorithm, and the method for determining the atmospheric transmittance are discussed with details. Sensitivity analysis of the algorithm has been performed for evaluation of probable LST estimation error due to the possible errors in transmittance and emissivity. Results from the analysis indicate that the proposed algorithm is able to provide an accurate estimation of LST from AVHRR data. Assuming an error of 0.05 in atmospheric transmittance estimate and 0.01 in ground emissivity for the two AVHRR thermal channels, the average LST error with the algorithm is 1.1°C. Two methods have been used to validate the proposed algortihm. Comparison has also been done with the existing 11 algorithms in literature. Results from validation and comparison using the standard atmospheric simulation for various situations and the ground truth data sets demonstrate the applicability of the algorithm. According to the root mean square (RMS) errors of the retrieved LSTs from the measured or assumed LSTs, the proposed algorithm is among the best three. Considering the insignificant RMS error difference among the three, the proposed algorithm is better than the other two because they require more parameters for LST retrieval. Validation with standard atmospheric simulation indicates that this algorithm can achieve the accuacry of 0.25°C in LST retrieval for the case without error in both transmittance and emissivity estimates. The accuary of this algorithm is 1.75°C for the ground truth data set without precise in situ atmospheric water vapor contents. The accuracy increases to 0.24°C for another ground truth data set with precise in situ atmospheric water vapor contents. The much higher accuracy for this data set confirms the appplicability of the proposed algorithm as an alternative for the accurate LST retrieval from AVHRR data.

A review of mixture modeling techniques for sub‐pixel land cover estimation

Abstract Five different types of mixture models are reviewed. These are: linear, probabilistic, geometric‐optical, stochastic geometric, and fuzzy models. A summary of the conception and formulation of each of these types of models is presented. A comparative analysis of the different attributes of the models is made. In a general sense, the linear, probabilistic, and fuzzy models are relatively simple while the geometric (geometric‐optical and stochastic geometric) models are complicated, involving the incorporation of parameters of scene geometry. There is some difference in the number and nature of components that can be resolved with the different models. Available information is insufficient to categorize the models in terms of accuracy levels, but it is evident that mixture models produce more accurate land‐cover estimation than conventional classification.

Predicting forest structural parameters using the image texture derived from WorldView-2 multispectral imagery in a dryland forest, Israel

a b s t r a c t Estimation of forest structural parameters by field-based data collection methods is both expensive and time consuming. Satellite remote sensing is a low-cost alternative in modeling and mapping structural parameters in large forest areas. The current study investigates the potential of using WordView-2 mul- tispectral satellite imagery for predicting forest structural parameters in a dryland plantation forest in Israel. The relationships between image texture features and the several structural parameters such as Number of Trees (NT), Basal Area (BA), Stem Volume (SV), Clark-Evans Index (CEI), Diameter Differen- tiation Index (DDI), Contagion Index (CI), Gini Coefficient (GC), and Standard Deviation of Diameters at Breast Heights (SDDBH) were examined using correlation analyses. These variables were obtained from 30 m × 30 m square-shaped plots. The Standard Deviation of Gray Levels (SDGL) as a first order texture feature and the second order texture variables based on Gray Level Co-occurrence Matrix (GLCM) were calculated for the pixels that corresponds to field plots. The results of the correlation analysis indicate that the forest structural parameters are significantly correlated with the image texture features. The highest correlation coefficients were calculated for the relationships between the SDDBH and the contrast of red band (r = 0.75, p < 0.01), the BA and the entropy of blue band (r = 0.73, p < 0.01), and the GC and the contrast of blue band (r = 0.71, p < 0.01). Each forest structural parameter was modeled as a function of texture measures derived from the satellite image using stepwise multi linear regression analyses. The determi- nation coefficient (R2) and root mean square error (RMSE) values of the best fitting models, respectively, are 0.38 and 109.56 ha −1 for the NT; 0.54 and 1.79 m 2 ha −1 for the BA; 0.42 and 27.18 m 3 ha −1 for the SV; 0.23 and 0.16 for the CEI; 0.32 and 0.05 for the DDI; 0.25 and 0.06 for the CI; 0.50 and 0.05 for the GC; and 0.67 and 0.70 for the SDDBH. The leave-one-out cross-validation technique was applied for validation of the best-fitted models (R 2

Spectral reflectance of biogenic crust developed on desert dune sand along the Israel-Egypt border

Abstract The effect of biogenic crust on imagery acquired by spaceborne sensors is demonstrated. The crust consists mostly of microphytes such as cyanobacteria. The macrophytes (higher vegetation) on the sand dunes are sparse and have a relatively low spectra! reflectance response. However, since a considerable ponton of the ground is covered by this biogenic crust, (which has a different spectral reflectance from that of the mobile sands), a sharp brightness contrast is created between the two areas. It can be concluded that the well-known contrast between Sinai (Egypt) and the Negev (Israel), that has long drawn the attention of many observers, is not a direct result of vegetation cover but is caused by an almost complete cover of biogenic crust in the Negev, and a lack of this crust in Sinai, due largely to mans activities.