Françoise Nerry

Bidirectional reflectivity in AVHRR Channel 3 : Application to a Region in Northern Africa

Abstract This article is devoted to the retrieval of bidirectional reflectivity in AVHRR Channel 3 (3.75 μm). In the mid-infrared spectral region, the ground radiance is a combination of a radiance emitted by the surface and a reflected radiance due to sun irradiation. Using combined day/night AVHRR data, the emitted part of the radiance is estimated allowing for the determination of the reflected part. Emphasis is placed on the accuracy achievable on the measurement of the reflectivity in AVHRR Channel 3. A detailed sensitivity analysis of the method is performed, taking into account the different sources of error. The analysis is applied to series of images covering a region of Northern Africa. Atmospheric corrections are performed using the MODTRAN code with atmospheric data extracted from the AVISO (Meteo France) data base. Processing a large number of orbits gives access to a wide range of satellite view angles. Furthermore, use of morning NOAA 12 and noon NOAA 14 overpasses leads to essentially two Sun illumination directions (principal and perpendicular planes). For several selected areas, the angular reflectivity is represented along with absolute error. Reflectivity exhibits a large angular variation demonstrating a non-Lambertian behavior at the scale of the measurements. The levels of reflectivity are different from one area to another. A simple semiempirical model is used to represent the angular reflectivity. Emissivity in Channel 3, retrieved from reflectivity by means of an angular form factor, may exhibit strong variations for large angles (>18%). If angular variation of reflectivity is not taken into account, error on emissivity can be up to 15% for the areas studied in this work.

Atmospheric water vapor content over land surfaces derived from the AVHRR data: application to the Iberian Peninsula

A study has been carried out using simulated NOAA/advanced very high resolution radiometer (AVHRR) data at 11 and 12 /spl mu/m (with LOWTRAN-7, MODTRAN 2.0, and the TIGR database), AVHRR images of the Iberian Peninsula and the Palma de Mallorca Island, radiosonde observations at seven meteorological stations, and the AVISO database provided by Meteo France to describe, compare, and analyze two different approaches for estimating the total atmospheric water vapor content (W) over land surfaces from AVHRR data. These two techniques are: 1) the split-window covariance-variance ratio (SWCVR), based on a quadratic relationship between W and the ratio of the spatial covariance and variance of brightness temperatures measured in channels 4 (T/sub 4/) and 5 (T/sub 5/) of AVHRR in subsets of N neighboring pixels and 2) the linear split-window relationship (LSWR), based on a linear regression between W and the difference of brightness temperatures measured in the same channels (/spl Delta/T=T/sub 4/-T/sub 5/). The results demonstrate the advantage of the SWCVR technique for regions with a certain level of thermal heterogeneity (standard deviation of T/sub 4/ in the subset >0.5 K), which is capable of estimating W from NOAA-14 afternoon and night passes over the Iberian Peninsula with a standard deviation of 0.5 (g cm/sup -2/), whereas the LSWR technique predicts the atmospheric water vapor with a standard deviation from 1.3-1.5 (g cm/sup -2/). Finally a water vapor image of the entire Iberian Peninsula constructed by applying the SWCVR to NOAA-14 data is presented.

Temperature and Emissivity Retrievals From Hyperspectral Thermal Infrared Data Using Linear Spectral Emissivity Constraint

Owing to the ill-posed problem of radiometric equations, the separation of land surface temperature (LST) and land surface emissivity (LSE) from observed data has always been a troublesome problem. On the basis of the assumption that the LSE spectrum can be described by a piecewise linear function, a new method has been proposed to retrieve LST and LSE from atmospherically corrected hyperspectral thermal infrared data using linear spectral emissivity constraint. Comparisons with the existing methods found in literature show that our proposed method is more noise immune than the existing methods. Even with a NEΔT of 0.5 K, the rmse of LST is observed to be only 0.16 K, and that of LSE is 0.006. In addition, our proposed method is simple and efficient and does not encounter the problem of singular values unlike the existing methods. As for the impact of the atmosphere, the results show that our proposed method performs well with the uncertainty of the atmospheric downwelling radiance but suffers from the inaccuracy of the atmospheric upwelling radiance and atmospheric transmittance, which implies that an accurate atmospheric correction is still needed to convert the radiance measured at the satellite level to the at-ground radiance. To validate the proposed method, a field experiment was conducted, and the results show that 80% of the samples have an accuracy of LST within 1 K and that the mean values of LSE are accurate to 0.01.

Quantification of land-atmosphere exchanges of water, energy and carbon dioxide in space and time over the heterogeneous Barrax site

To advance our understanding of land–atmosphere exchanges of water, energy and carbon dioxide (CO2) in space and time over heterogeneous land surfaces, two intensive field campaigns were carried out at the Barrax agricultural test site in Spain during 12–21 July 2004 (SPARC 2004) and 8–14 July 2005 (SEN2FLEX 2005) involving multiple field, satellite and airborne instruments for characterizing the state of the atmosphere, the vegetation and the soil from the visible to the microwave range of the spectrum. Part of the experimental area is a core site of area 25 km2, within which numerous crops are grown, on both irrigated and dry land, alongside fields of bare soil. The campaigns were carried out in the framework of the Earth Observation Envelope Programme of the European Space Agency (ESA) with the aim of supporting geophysical algorithm development, calibration/validation and the simulation of future spaceborne Earth Observation missions. Both campaigns were also contributions to the EU 6FP EAGLE Project. The emphasis of this contribution is on the in situ measurements of land–atmosphere exchanges of water, energy and CO2 as well as the thermal dynamic states of the atmosphere, the soil and the vegetation. Preliminary analysis and interpretation of the measurements are presented. These two data sets are open to the scientific community for collaborative investigations.

NOAA-AVHRR Orbital Drift Correction From Solar Zenithal Angle Data

This paper presents a new method for NOAAs (National Ocean and Atmospheric Administration) orbital drift correction. This method is pixel-based, and in opposition with most methods previously developed, does not need explicit knowledge of land cover. This method is applied to AVHRR (Advanced Very High Resolution Radiometer) channel information, and relies only on the additional knowledge of solar zenithal angle (SZA) and acquisition date information. In a first step, anomalies in SZA and channel time series are retrieved, and screened out for anomalous values. Then, the part of the parameter anomaly which is explained by SZA anomaly is removed from the data, to estimate new parameter anomalies, and this iteratively until the influence of SZA anomalies is totally removed from the parameter data. This correction has been applied to bimonthly AVHRR data provided by the GIMMS group (Global Inventory Modeling and Mapping Studies), covering Africa from November 2000 to December 2006. NDVI and LST (land surface temperature) have been estimated from raw and corrected data, and averaged over homogeneous vegetation classes. Differences between raw and corrected averaged parameters show an improvement in the quality of the data. In order to validate this method, a whole week (10 to 17 July 2004) of METEOSAT SEVIRI (Spinning Enhanced Visible and InfraRed Imager) data have been used, from which LST have been estimated using a similar method to the one used to retrieve LST from AVHRR data. The comparison between both platforms at the same time of acquisition shows good concordance.

Directional Effects Consideration to Improve out-doors Emissivity Retrieval in the 3–13 µm Domain

This work analyses and solves for issues encountered when retrieving surface emissivity in LWIR (750 to 1250 cm(-1)) and MWIR (2000 to 3500 cm(-1)) bands under outdoor conditions. The Spectral Smoothness method, which takes advantage of high spectral resolution measurements to solve for temperature emissivity separation, and which has already proven its efficiency in the LWIR domain, was applied in an experimental campaign to assess its ability to operate both in the LWIR and MWIR domains. In the MWIR band, directional behaviour of surface emissivity is shown to be a source of systematic errors in the retrieved emissivity and a new method, called SmaC (SMoothness And Continuity), corrects for this error by providing quantitative estimates on the deviation of the surface from Lambertian behavior.

Mapping temperature independent spectral indice of emissivity and directional emissivity in AVHRR channels 4 and 5

This work addresses the retrieval of AVHRR channel 4 and channel 5 emissivities from time series of images over two extended regions, one over Northern Africa and centred on Tunisia, the other centred on the Iberian Peninsula. The retrieval is based on the Temperature Independent Spectral Indice of Emissivity (TISIE) concept, a particular ratio of channel emissivities, which values are computed from a temperature independent combination of infrared channel radiances. This paper is focused on the use of two-channels TISIE indices relating channel 3 emissivity to either channel 4 or channel 5 emissivity. The retrieval of channel 3 directional emissivity from the data sets was achieved and discussed in Part I, whereas this paper emphasizes the determination and characteristics of the TISIE indices. These indices are found to be, within experimental error, ( i ) temperature independent; ( ii ) independent of zenithal view angle; ( iii ) stable over the whole period for a given surface type, allowing for building pixel-wise mean temporal indices. These mean values are then used to determine and model the directional emissivities in both channel 4 and 5. Emissivities exhibit little spectral and angular variations for surfaces covered with vegetation, with values higher than 0.96. Over arid areas (bare soils), emissivities are mostly less than 0.95, slightly higher in channel 5 than in channel 4, and exhibit significant angular variation, up to 3% decrease from nadir to 60° for low emissivity surface. Results suggest that TISIE indices may have a good potential for discriminating between different bare soil types.

Estimation and Validation of Land Surface Temperatures from Chinese Second-Generation Polar-Orbit FY-3A VIRR Data

This work estimated and validated the land surface temperature (LST) from thermal-infrared Channels 4 (10.8 µm) and 5 (12.0 µm) of the Visible and Infrared Radiometer (VIRR) onboard the second-generation Chinese polar-orbiting FengYun-3A (FY-3A) meteorological satellite. The LST, mean emissivity and atmospheric water vapor content (WVC) were divided into several tractable sub-ranges with little overlap to improve the fitting accuracy. The experimental results showed that the root mean square errors (RMSEs) were proportional to the viewing zenith angles (VZAs) and WVC. The RMSEs were below 1.0 K for VZA sub-ranges less than 30° or for VZA sub-ranges less than 60° and WVC less than 3.5 g/cm2, provided that the land surface emissivities were known. A preliminary validation using independently simulated data showed that the estimated LSTs were quite consistent with the actual inputs, with a maximum RMSE below 1 K for all VZAs. An inter-comparison using the Moderate Resolution Imaging Spectroradiometer (MODIS)-derived LST product MOD11_L2 showed that the minimum RMSE was 1.68 K for grass, and the maximum RMSE was 3.59 K for barren or sparsely vegetated surfaces. In situ measurements at the Hailar field site in northeastern China from October, 2013, to September, 2014, were used to validate the proposed method. The result showed that the RMSE between the LSTs calculated from the ground measurements and derived from the VIRR data was 1.82 K.

Evaluation of land surface temperature and emissivities retrieved from MSG/SEVIRI data with MODIS land surface temperature and emissivity products

Land surface temperature (LST) and land surface emissivity (LSE) are two key parameters in global climate study. This article aims to cross-validate LST/LSE products retrieved from data of the Spinning Enhanced Visible and Infrared Imager (SEVIRI) on board the first geostationary satellite, Meteosat Second Generation (MSG), with Moderate Resolution Imaging Spectroradiometer (MODIS) LST/LSE version 5 products over the Iberian Peninsula and over Egypt and the Middle East. Besides time matching, coordinate matching is another requirement of the cross-validation. An area-weighted aggregation algorithm was used to aggregate SEVIRI and MODIS LST/LSE products into the same spatial resolution. According to the quality control (QC) criterion and the view angle, the cross-validation was completed under clear-sky conditions and within a view angle difference of less than 5° for the two instruments to prevent land surface anisotropic effects. The results showed that the SEVIRI LST/LSE products are consistent with MODIS LST/LSE products and have the same trend over the two study areas during both the daytime and the night-time. The SEVIRI LST overestimates the temperature by approximately 1.0 K during the night-time and by approximately 2.0 K during the daytime compared to MODIS products over these two study areas. The SEVIRI LSE underestimates by about 0.015 in 11 μm and by about 0.025 in 12 μm over the Iberian Peninsula. However, both LSEs agree and show a difference of less than 0.01 over Egypt and the Middle East.

Mapping directional emissivity at 3.7 μ m using a simple model of bi-directional reflectivity

Algorithms for Land Surface Temperature determination from satellite data need incorporating pixel-wise emissivity values to take care of the variability of this parameter over land and achieve a high accuracy in the surface emissivity. This parameter should be determined at the pixel scale and thus directly from the sensors data. This work addresses the issue of extracting the land emissivity from AVHRR data. The necessary emissivity-temperature decoupling is achieved thanks to a method (Becker and Li 1990) that uses a combination of day/night channels 3, 4 and 5 data. Single channel atmospheric corrections are performed using MODTRAN and atmospheric profiles from outputs of GCMs (MétéoFrance AVISO database). The channel 3 emissivity is extracted in two steps: first the channel 3 bi-directional reflectivity is retrieved using the emissivity-temperature decoupling; second, emissivity is related to the reflectivity thanks to Kirchoffs relation. Accuracy assessment indicates that the expected overall error on channel 3 emissivity ranges between 3% and 6% at most (for low emissivity value). Series of day/night AVHRR images are processed, resulting in a wide range of view angles. A simple empirical model is used to fit the angular variation of the bi-directional reflectivity, from which a form factor can be calculated, allowing directional emissivity to be obtained. A clear distinction is observed in the angular behaviour between bare soils and vegetation covered surfaces where backscattering appears dominant. Directional channel 3 emissivity shows a large dynamical ranges, from above 0.95 for fully vegetation covered surfaces, to below 0.6 for desert surfaces. Amplitude of emissivity angular variation is small for vegetation, whereas it may be up to 10% between nadir and 60°-view angle for desert areas.