Advanced Along-Track Scanning Radiometer Data Research Articles

Determination of AATSR Biases Using the OSTIA SST Analysis System and a Matchup Database

Abstract Sea surface temperature (SST) analyses are produced on a daily basis at the Met Office using the Operational SST and Sea Ice Analysis (OSTIA) system. OSTIA uses satellite SST data, provided by international agencies via the Global Ocean Data Assimilation Experiment (GODAE) High-Resolution SST Pilot Project (GHRSST-PP) regional/global task sharing (R/GTS) framework, which includes an estimate of bias error (available online at http://www.ghrsst-pp.org). The OSTIA system produces a foundation SST estimate (SSTfnd), which is the SST that is free of diurnal variability, at a resolution of 1/20° (∼6 km). Global coverage outputs are provided each day in GHRSST-PP L4 netCDF format. The verification and intercomparison of the OSTIA analysis, with observations and analyses, has revealed a cold bias of approximately 0.1 K in the OSTIA outputs. Because OSTIA uses the operational 1-km Envisat Advanced Along-Track Scanning Radiometer (AATSR) ATS_NR_2P data [via the GHRSST-PP/European Space Agency (ESA) Medspiration Project, available online at http://www.medspiration.org] as a reference dataset for bias adjustment of other satellite data, the AATSR data were identified as the likely cause of the observed bias. To test this, a series of experiments were carried out in June 2006 using the Medspiration AATSR observations in which the Single Sensor Error Statistics (SSES) bias estimate was assigned fixed magnitudes of 0.0, 0.05, 0.15, and 0.2 K. The authors find that the AATSR data have approximately zero bias relative to in situ buoys. Because AATSR measures the SST skin temperature (SSTskin) and was given a mean global SSTskin deviation of −0.17 K (based on in situ radiometer data), this result suggests that ATS_NR_2P SSTskin data have a warm bias of 0.17 K. Using a matchup database of near-contemporaneous 10 arc min AATSR and in situ data, the authors find that the AATSR SSTskin dual- and triple-window retrievals have a warm bias of 0.14 and 0.17 K, respectively, between August 2002 and July 2006. The results of the experiments confirm that the current Medspiration SSES bias correction provided with the Medspiration AATSR L2P observations is poorly specified. The database was not configured to test the relationship between the cloud proximity confidence value and the AATSR bias error. Based on the matchup database and reanalysis results, the authors suggest that Medspiration be modified to use an SSES bias estimate of 0.17 K for all category 2–6 proximity confidence values for the current AATSR dual-view SST ATS_NR_2P products to provide a correct SSTskin estimate. In response to the results presented in this study, operational changes have been made to the Medspiration processing, which improve the bias estimates provided in the AATSR data. The authors suggest that a concerted effort be invested to develop the most appropriate SSES for the AATSR class of sensors that have specific characteristics that must be included in the SSES estimation scheme. The main elements of such a scheme are presented in this paper.

ENVISAT/AATSR derived land surface temperature over a heterogeneous region

In this paper a method for evaluating land surface temperature (LST) algorithms over heterogeneous areas is presented. The evaluation was made for a set of 12 algorithms derived by using the split-window (SW) and dual-angle (DA) techniques for estimating sea and land surface temperature (SST and LST) from Advanced Along-Track Scanning Radiometer (AATSR) data. A validation of the proposed algorithms was carried out over a heterogeneous region of Morocco in the framework of the WATERMED (WATer use Efficiency in natural vegetation and agricultural areas by Remote sensing in the MEDiterranean basin) project. AATSR data and in situ measurements over this heterogenous region were compared by implementing a classification based strategy over a higher spatial resolution Landsat image. Three reference classes were considered when performing the classification from the Landsat image. Ground based measurements where then used to assign an effective surface radiometric temperature to each of these three classes. Finally, an averaging procedure based on class proportion was implemented for deriving surface radiometric temperature at the AATSR pixel scale. For this heterogeneous site, the results showed that LST can be obtained with a root mean-square error (RMSE) lower than 1.7 K from the split-window algorithms. Dual-angle algorithms, on the other hand, provided greater RMSE due to the different surfaces observed in the nadir and forward views. The results suggest that to retrieve LST from 1 km pixels over heterogeneous surfaces spatial averaging is required to improve accuracy on temperature estimation.

Surface Temperature Variation with Vegetation Phenology over Deciduous Forests – A Study over Nagarjunasagar Srisailam Tiger Reserve (NSTR) Using ENVISAT ‐ AATSR Data

Combined analysis of reflective and thermal data recorded by coarse resolution and high repetitive satellites provide a useful means to study seasonal vegetation characteristics and other related phenological parameters. ENVISAT Advanced Along Track Scanning Radiometer (AATSR) data for different seasons from January 04 to April 04 has been analyzed to derive the Normalized Difference Vegetation Index (NDVI) and Surface Temperature (ST) images for the forest regions of Nagarjunasagar Srisailam Tiger Reserve (NSTR), India. Temporal variation of NDVI and ST reflected the phenology of the forest area. A negative relationship was observed between the NDVI and ST over all the vegetation types and the proportion of vegetation cover seemed to has bearing on the ST. Scatter plots for NDVI and ST drawn for winter season showed a good separability of land use/land cover types of the study area.