Comparisons of regional satellite sea surface temperature gradients derived from MODIS and AVHRR sensors

Regional sea surface temperature (SST) gradients were examined for a 6-year (2003–2008) period using data from the NASA Moderate Resolution Imaging Spectroradiometer (MODIS) on Aqua and the Advanced Very High Resolution Radiometer (AVHRR) on two NOAA satellite platforms. Two regions, one in the California Current System and the other in the Gulf Stream, representing an eastern boundary upwelling region and strong western boundary current, respectively, were chosen to investigate the seasonal variability, statistical differences and similarities, and correlations with respect to the two sets of SST gradients. Results indicated higher gradient magnitudes using MODIS SST in relative comparison to those derived from AVHRR that are attributed to instrument and algorithm differences. These observed differences are important for any studies that employ SST gradients, such as fisheries investigations that have traditionally relied on AVHRR SST gradients only.     

Ground measurements for the validation of land surface temperatures derived from AATSR and MODIS data

An experimental site was set up in a large, flat and homogeneous area of rice crops for the validation of satellite derived land surface temperature (LST). Experimental campaigns were held in the summers of 2002-2004, when rice crops show full vegetation cover. LSTs were measured radiometrically along transects covering an area of 1 km². A total number of four thermal radiometers were used, which were calibrated and inter-compared through the campaigns. Radiometric temperatures were corrected for emissivity effects using field emissivity and downwelling sky radiance measurements. A database of ground-based LSTs corresponding to morning, cloud-free overpasses of Envisat/Advanced Along-Track Scanning Radiometer (AATSR) and Terra/Moderate Resolution Imaging Spectroradiometer (MODIS) is presented. Ground LSTs ranged from 25 to 32 °C, with uncertainties between ± 0.5 and ± 0.9 °C. The largest part of these uncertainties was due to the spatial variability of surface temperature. The database was used for the validation of LSTs derived from the operational AATSR and MODIS split-window algorithms, which are currently used to generate the LST product in the L2 level data. A quadratic, emissivity dependent split-window equation applicable to both AATSR and MODIS data was checked as well. Although the number of cases analyzed is limited (five concurrences for AATSR and eleven for MODIS), it can be concluded that the split-window algorithms work well, provided that the characteristics of the area are adequately prescribed, either through the classification of the land cover type and the vegetation cover, or with the surface emissivity. In this case, the AATSR LSTs yielded an average error or bias of − 0.9 °C (ground minus algorithm), with a standard deviation of 0.9 °C. The MODIS LST product agreed well with the ground LSTs, with differences comparable or smaller than the uncertainties of the ground measurements for most of the days (bias of + 0.1 °C and standard deviation of 0.6 °C, for cloud-free cases and viewing angles smaller than 60°). The quadratic split-window algorithm resulted in small average errors (+ 0.3 °C for AATSR and 0.0 °C for MODIS), with differences not exceeding ± 1.0 °C for most of the days (standard deviation of 0.9 °C for AATSR and 0.5 °C for MODIS).     

Aircraft validation of ERS-1 ATSR and NOAA-14 AVHRR sea surface temperature measurements

Infrared brightness temperature (BT) measurements obtained from the UK Meteorological Research Flight C-130 Hercules aircraft, spatially coincident and near contemporaneous with ERS-1 Along Track Scanning Radiometer (ATSR) and NOAA-14 Advanced Very High Resolution Radiometer (AVHRR), are presented. These data have been used to obtain much needed on-going validation of the ERS-1 ATSR prior to the de-commissioning of the ATSR instrument in March 1997. BT comparisons between the ATSR and AVHRR nadir channels show negligible differences of 0.3 deg K, indicating that both radiometers are well calibrated. However, significant differences are found when common sea surface temperature (SST) algorithms are applied to the BT data. The original dual view ERS-1 ATSR skin SST (SSST) algorithm has a 0.6K cool bias relative to the in situ observations, which is consistent with other in situ validation studies. New SSST coefficients derived using the same atmospheric transmission model show that, when the appropriate pixel noise contribution is considered in the algorithm derivation, substantially improved SSST is derived from the ERS-1 ATSR. Comparing the NOAA-14 AVHRR non-linear SST (NLSST) and multi-channel SST (MCSST) algorithms to the aircraft data, the non-linear nature of the NLSST algorithm results in a small bias of 0.3 deg K compared to a substantial cool bias of 1 deg K in the MCSST case. This result highlights deficiencies in the MCSST.     

Estimation of sea surface temperature from SEVIRI data: algorithm testing and comparison with AVHRR products

Three surface temperature (ST) algorithms for Spinning Enhanced Visible and InfraRed Imager (SEVIRI) data are developed and tested. A general split window algorithm for ST estimation, a sea surface temperature (SST) algorithm and a nonlinear algorithm (NLSST) developed for SEVIRI data. The test was carried out by comparing SEVIRI data with two types of data: (a) in situ and (b) obtained with the NLSST algorithm applied to Advanced Very High Resolution Radiometer (AVHRR). The field campaign was carried out over sea using a thermal radiometer. The algorithms were applied to SEVIRI images in coincidence with the field campaign and the results show an rms error lower than 0.7 K. The comparison with AVHRR data was carried out in six test regions and provided an rms error lower than 1.3 K. The best results were obtained for the SST algorithm proposed.     

Validation of land surface temperature derived from MSG/SEVIRI with in situ measurements at Gobabeb,Namibia

Land surface temperature (LST) derived from Meteosat Second Generation/?Spinning-Enhanced Visible and Infrared Imager MSG/SEVIRI data is an operational product of the Land Surface Analysis Satellite Applications Facility (LSA SAF). The LST has a temporal resolution of 15 minutes, a sampling distance of 3 km at nadir, and a targeted accuracy of better than 2 K. Gobabeb (Namibia) is one of Karlsruhe Institute of Technology's (KIT's) four dedicated stations for LST validation. In March 2010, a field survey was performed to characterize the Gobabeb site more closely. SAF LST and in situ LST obtained over a period of 3 days from additional measurements with a telescopic mast on the Namib gravel plains were in good agreement with each other (bias 1.0 K). For the same period, the bias between SAF LST and Gobabeb main station LST was even smaller (0.4 K). A mobile measurement system was set up by fixing the telescopic mast to a four-wheel drive. Around solar noon, LST from in situ measurements along a 40 km track and LST from Gobabeb main station had a bias of 0.4 K and a standard deviation of 1.2 K, which means that in situ LSTs at Gobabeb main station are representative for large parts of the gravel plains. Exploiting this relationship, 2 years of LST from MSG/SEVIRI were compared with in situ LST from Gobabeb main station. The magnitude of the monthly biases between the two data sets was generally less than 1.0 K and root mean square errors were below 1.5 K. Furthermore, the bias appears to exhibit a seasonality, which could be accounted for in future validation work.     

A comparison of surface temperature derived from HCMM infrared measurements with field data

HCMM surface temperatures were compared to field data obtained in the Mississippi River in the vicinity of St. Louis, Missouri, and in the oceans in the vicinity of the Nantucket Shoals and in the eastern Gulf of Mexico. It was found that, on the average, the difference between the HCMM surface temperature corrected for atmospheric attenuation and the in situ temperature at the same location was ?4.6°C. Previous calibration results (Barnes and Price, 1980) indicated that the difference was +5.2°C. That calibration study used data collected in the first few months after HCMM was launched, suggesting that there was a calibration problem at that time. As a result of that study, the HCMM data were adjusted so that they were 5.2°C lower. The results of this study, which used data collected after 1 June 1978, suggest that the adjustment was no longer necessary and that 5.2°C must now be added to the HCMM surface temperatures to obtain the correct value. Calibrated and atmospherically corrected HCMM surface temperature data were compared with the ocean surface data. Fifty-seven data pairs were compared over the temperature range from 10°C to 25°C. The RMSD was ±1.0°C, and the linear correlation coefficient was 0.97.     

Estimation of sea surface temperature from AVHRR data: reply to some comments by J. R. Eyre

Abstract

This paper refers to a previous comment by J. R. Eyre on an earlier paper of ours in which we tried to make the best possible use of 4-channel AVHRR/1 data for the determination of sea surface temperatures in situations where data from the 5-channel AVHRR/2 were not available. Here we clarify certain points regarding the philosophy of our earlier paper and also respond to some specific points made by Eyre.     

Retrieval and validation of land surface temperature (LST) from NOAA AVHRR thermal images of Gujarat,India

A study was undertaken to retrieve land (soil-vegetation complex) surface temperature (LST) over a 100 km 2 100 km area in Gujarat (India) using thermal bands (channel 4 and 5) and estimated emissivity from atmospherically corrected NDVI, derived from NOAA-14 AVHRR data. The LST values were compared with near synchronous soil and air temperature measurements over five sites in December and May 1997 during Land Surface Processes Experiment (LASPEX) in Gujarat, India. The estimated LST of a semi-arid mixed agricultural barren landscape at 10.00 GMT was found to vary from 302 to 305.6 K on 13 December 1997 (winter) and from 317.5 to 328.5 K at 08.30 GMT on 15 May 1997 (Summer). During December, the LST values were near midway between air temperature (AT) and soil surface temperature (ST) with mean bias of m 2.9 K and 7.0 K respectively. However, in May, the LST values were found to be closer to ST, which may be due to lower fractional vegetation cover and NDVI.     

Estimates of sea surface nitrate concentrations from sea surface temperature and chlorophyll concentration in upwelling areas: A case study for the Benguela system

The knowledge of nitrate fields at global or regional scales in the ocean is fundamental for the study of oceanic biogeochemical processes, particularly those linked to new primary production. The estimate of nitrate concentrations from space is generally based on empirical inverse relationships between sea surface temperature (SST) and nitrate concentrations. These relationships, however, are often highly variable spatially and temporally, and hardly applicable to large areas (i.e., larger than a few degrees in latitude). In this paper we propose a new approach specifically developed for areas influenced by upwelling processes. It relates the nitrate concentration to the difference between SST and the estimated temperature of the upwelled water (variable with latitude and season), δT, which is an indicator of the time elapsed since upwelling. This approach is tested for the Benguela upwelling system, and algorithms are developed using in situ data provided by the World Ocean Database 2005 of the NOAA-NESDIS-National Oceanographic Data Center. The results reveal a significant improvement compared to the NO₃-SST relationships, and a single algorithm can be applied to the whole upwelling area (15 to 35°S). Further improvement is gained by coupling this approach with a method that derives sea surface nitrate concentrations from SST and surface chlorophyll a concentration using multiple regression analyses, as proposed by Goes et al. [Goes, Saino, Oaku, Jiang, (1999). Method for estimating sea surface nitrate concentrations from remotely sensed SST and chlorophyll a: A case study for the North Pacific Ocean using OCTS/ADEOS data. IEEE Transactions on Geoscience and Remote Sensing, 37, no. 3 II, 1633-1644].     

Seasonal and interannual variability of oceanographic processes in the Gulf of Guinea: An investigation using AVHRR sea surface temperature data

The Gulf of Guinea is situated in a critical position for understanding Atlantic equatorial dynamics. This study investigates seasonal and interannual variability in sea surface temperature (SST) throughout this region, focusing on dynamical ocean processes. A 10.5-year time series of remotely sensed SST data with 4 km spatial resolution from the Advanced Very High Resolution Radiometer (AVHRR) were used for this investigation, as they are sufficient to resolve shelf processes. Firstly, patterns of cloud cover were assessed, then spatio-temporal variability in SST patterns was investigated. Features identified in climatological SST images were the Senegalese upwelling influence, coastal upwelling, tropical surface water, river run-off and fronts. Of particular interest is a shelf-edge cooling along the coast of Liberia and Sierra Leone in February. Interannual variability, assessed using annual mean images, time series decomposition and spectral analysis, showed a quasi-cyclic pattern of warm and cool years, perhaps related to El Niño-type forcing. The results of this study show the usefulness of infrared remote sensing for tropical oceanography, despite high levels of cloud cover and atmospheric water vapour contamination, and they provide evidence for theories of westward movement of the upwelling against the Guinea current and remote forcing of the upwelling.     

Seasonal variation of sea surface temperature in the Bay of Bengal during 1992 as derived from NOAA-AVHRR SST data

Monthly maps of sea surface temperature (SST) derived from NOAA (National Oceanic and Atmospheric Administration)-AVHRR (Advanced Very High Resolution Radiometer) data during 1992 for the Bay of Bengal are analysed and compared with the available/compiled monthly seatruth (bucket thermometer) data of this region. It was noticed that the computed SST bias (AVHRR SST minus Seatruth SST), in general, varied between 2.0 and 2.5 C with smaller bias values (1.5 to 1.5 C) during January-June and December. Larger bias values were noticed in the south-eastern Bay in July and in the Andaman Sea in October. The large SST biases suggested the necessity for improvement of SST algorithms by properly removing the clouds. The spatial variation of Standard Deviation of SST bias was particularly high (0.7) in the western Bay when compared to other parts of the Bay of Bengal. The monthly maps of AVHRR SST clearly depicted the seasonal cycle of SST showing the well known bi-modal SST distribution of the study region with winter cooling, summer heating, monsoonal cooling and post-monsoon warming phases. The seasonal cycle of SST further revealed the persistence of Warm Pool (SST 28 C) in the Bay of Bengal from March through October.     

Comparison of land surface emissivity and radiometric temperature derived from MODIS and ASTER sensors

This study compared surface emissivity and radiometric temperature retrievals derived from data collected with the MODerate resolution Imaging Spectroradiometer (MODIS) and Advanced Spaceborne Thermal Emission Reflection Radiometer (ASTER) sensors, onboard the NASA's Earth Observation System (EOS)-TERRA satellite. Two study sites were selected: a semi-arid area located in northern Chihuahuan desert, USA, and a Savannah landscape located in central Africa. Atmospheric corrections were performed using the MODTRAN 4 atmospheric radiative transfer code along with atmospheric profiles generated by the National Center for Environmental Predictions (NCEP). Atmospheric radiative properties were derived from MODTRAN 4 calculations according to the sensor swaths, which yielded different strategies from one sensor to the other. The MODIS estimates were then computed using a designed Temperature-Independent Spectral Indices of Emissivity (TISIE) method. The ASTER estimates were derived using the Temperature Emissivity Separation (TES) algorithm. The MODIS and ASTER radiometric temperature retrievals were in good agreement when the atmospheric corrections were similar, with differences lower than 0.9 K. The emissivity estimates were compared for MODIS/ASTER matching bands at 8.5 and 11 μm. It was shown that the retrievals agreed well, with RMSD ranging from 0.005 to 0.015, and biases ranging from −0.01 to 0.005. At 8.5 μm, the ranges of emissivities from both sensors were very similar. At 11 μm, however, the ranges of MODIS values were broader than those of the ASTER estimates. The larger MODIS values were ascribed to the gray body problem of the TES algorithm, whereas the lower MODIS values were not consistent with field references. Finally, we assessed the combined effects of spatial variability and sensor resolution. It was shown that for the study areas we considered, these effects were not critical.     

NOAA/AVHRR数据的雪盖信息提取与复合

在对NOAA/AVHRR数据特征与雪冰波谱特性分析的基础上，对各种提取雪盖信息的方法进行了比较，指出了各种方法的优劣，认为在实时的雪灾监浏与评估系统中，直方图分割的方法快速有效。另一方面，通过雪盖影像与GIS中各种矢量图形的复合配准实验，指出宜先对AVHRR影像进行点位计算，然后利用控制点、进行精校正，所产生的图像才能达到与矢量图形的准确配准。     

Development and analysis of a 12-year daily 1-km forest fire dataset across North America from NOAA/AVHRR data

Fires in boreal and temperate forests play a significant role in the global carbon cycle. While forest fires in North America (NA) have been surveyed extensively by U.S. and Canadian forest services, most fire records are limited to seasonal statistics without information on temporal evolution and spatial expansion. Such dynamic information is crucial for modeling fire emissions. Using the daily Advanced Very High Resolution Radiometer (AVHRR) data archived from 1989 to 2000, an extensive and consistent fire product was developed across the entire NA forest regions on a daily basis at 1-km resolution. The product was generated following data calibration, geo-referencing, and the application of an active fire detection algorithm and a burned area mapping algorithm. The spatial-temporal variation of forest fire in NA is analyzed in terms of (1) annual and monthly patterns of fire occurrences in different eco-domains, (2) the influence of topographic factors (elevation zones, aspect classes, and slope classes), and (3) major forest types and eco-regions in NA. It was found that 1) among the 12 years analyzed, 1989 and 1995 were the most severe fire years in NA; 2) the majority of burning occurred during June-July and in low elevation zones (< 500 m) with gentle slopes (< 10°), except in the dry eco-domain where more fires occurred in higher elevation zones (> 2000 m); 3) most fires occurred in the polar eco-domain, sub-arctic eco-division, and in the taiga ( boreal forests), forest-tundras and open woodlands eco-provinces in the boreal forests of Canada. The tendency for multiple burns to occur increases with elevation and slope until about 2500 m elevation and 24° slope, and decreases therefore. In comparison with ground observations, the omission and commission errors are on the order of 20%.     

Assessment of multi-resolution and multi-sensor data for urban surface temperature retrieval

Data from three thermal sensors with different spatial resolution were assessed for urban surface temperature retrieval over the Yokohama City, Japan. The sensors are Thermal Airborne Broadband Imager (TABI), Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) and MODerate resolution Imaging Spectroradiometer (MODIS). Two algorithms were developed for land surface temperature (LST) retrieval from TABI image and ASTER thermal infrared (TIR) channels 13 and 14. In addition, ASTER LST and MODIS LST products were also collected. All the LST images were assessed by analyzing the relationship between LST and normalized difference vegetation index (NDVI) and by spatial distributions of LST profiles, derived from typical transects over the LST images. In this study, a strong negative relationship between LST and NDVI has been demonstrated although the degree of correlation between NDVI and LST varies slightly among the different LST images. Cross-validation among the LST images retrieved from the three thermal sensors of different spatial resolutions indicates that the LST images retrieved from the 2 channel ASTER data and a single band TABI thermal image using our developed algorithms are reliable. The LST images retrieved from the three sensors should have different potential to urban environmental studies. The MODIS thermal sensor can be used for the synoptic overview of an urban area and for studying urban thermal environment. The ASTER, with its TIR subsystem of 90-m resolution, allows for a more accurate determination of thermal patterns and properties of urban land use/land cover types, and hence, a more accurate determination of the LST. In consideration of the high heterogeneity of urban environment, the TABI thermal image, with a high spatial resolution of 2 m, can be used for rendering and assessing complex urban thermal patterns and detailed distribution of LST at the individual house level more accurately.     

Retrieval of high-resolution sea surface temperature data for Sendai Bay, Japan, using the Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER)

We studied sea surface temperature (SST) retrieval algorithms for Sendai Bay, using output from the thermal-infrared channels of the Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) on board Terra. While the highest resolutions of other satellite SST products are about 1 km, the ASTER thermal-infrared channels provide 90-m spatial resolution. To develop the ASTER algorithm, we employed statistical methods in which SSTs retrieved from the thermal-infrared measurements were tuned against the Moderate Resolution Imaging Spectroradiometer (MODIS) SST product with a 1-km spatial resolution. Terra also carries a MODIS sensor, which observed the same area as the ASTER sensor at the same time. The MODIS SST was validated around Sendai Bay, revealing a bias of −0.15 °C and root mean-square difference (RMSD) of 0.67 °C against in situ SSTs. Taking into account the spatial-resolution difference between ASTER and MODIS, match-up was generated only if the variability of ASTER brightness temperatures (T₁₃) was small in a pixel of MODIS SST (MP). The T₁₃ within one MP was about 121 pixels. The standard deviation (σ₁₃) of T₁₃ was calculated for each cloud-free MP, and the threshold of σ₁₃ for choosing match-up MPs was decided by analyzing the σ₁₃ histogram of one ASTER image. The 15 synchronous pairs of ASTER/MODIS images are separated into two groups of 8 pairs called set (A) and 7 pairs called set (B). Using the common procedure, the match-ups are generated for set (A) and set (B). The former is used for developing the ASTER Multi-Channel SST (MCSST) algorithm, and the latter for validation of the developed ASTER SST. Analysis of the whole 15 pairs indicated that ASTER SST does not depend on the satellite zenith angle. We concluded that, using Akaike's information criterion with set (A) match-ups, the multiple regression formula with all five thermal-infrared channels was adequate for the ASTER SST retrieval. Validation of ASTER SST using match-up set (B) indicated a bias of 0.101 °C and RMSD of 0.455 °C.     

Influence of lake morphology and clarity on water surface temperature as measured by EOS ASTER

Relationships between lake morphometric parameters and nighttime lake surface temperatures were investigated in North American temperate lakes using the ASTER kinetic temperature (AST08) product. Nighttime ASTER kinetic temperature measurements were found to be a good analogue for nighttime surface temperatures. Linear regression between ASTER and buoy-measured temperatures in a test lake were better during the evening (R² = 0.98) than the day (R² = 0.90), presumably due to the greater influence of radiation and latent heat fluxes during daylight hours. Nighttime lake surface temperatures measured in three ASTER scenes were significantly correlated to logarithm of lake area, maximum lake depth, Secchi depth (a measure of lake clarity) and lake order (a measure of lake connection with surface drainage), during October and November. Nighttime lake surface temperatures were significantly correlated only with lake area in July. We hypothesize that morphology was more strongly related to surface temperature in the fall months due to lake turnover during that season. This study suggests that satellite derived thermal data may be useful for calculation of lake heat budgets and evaporation rates, provided surface temperatures are measured in well-mixed lakes.     

Identification of land surface temperature and albedo trends in AVHRR Pathfinder data from 1982 to 2005 for northern Siberia

The arctic regions are highly vulnerable to climate change. Climate models predict an increase in global mean temperatures for the upcoming century. The arctic environment is subject to significant changes of the land surface. Especially the changes of vegetation pattern and the phenological cycle in the taiga–tundra transition area are of high importance in climate change research. This study focuses on time series and trend analysis of land surface temperature, albedo, snow water equivalent, and normalized difference vegetation index information in the time period of 1982–2005 for northern Siberia. The findings show strong dependencies between these parameters and their inter-annual dynamics, which indicate changes in vegetation growing period. We found a strong negative correlation between land surface temperature and albedo conditions for the beginning (60–90%) of the growing season for selected hot spot trend regions in northern Siberia.     

A continuous measure of gross primary production for the conterminous United States derived from MODIS and AmeriFlux data

The quantification of carbon fluxes between the terrestrial biosphere and the atmosphere is of scientific importance and also relevant to climate-policy making. Eddy covariance flux towers provide continuous measurements of ecosystem-level exchange of carbon dioxide spanning diurnal, synoptic, seasonal, and interannual time scales. However, these measurements only represent the fluxes at the scale of the tower footprint. Here we used remotely sensed data from the Moderate Resolution Imaging Spectroradiometer (MODIS) to upscale gross primary productivity (GPP) data from eddy covariance flux towers to the continental scale. We first combined GPP and MODIS data for 42 AmeriFlux towers encompassing a wide range of ecosystem and climate types to develop a predictive GPP model using a regression tree approach. The predictive model was trained using observed GPP over the period 2000-2004, and was validated using observed GPP over the period 2005-2006 and leave-one-out cross-validation. Our model predicted GPP fairly well at the site level. We then used the model to estimate GPP for each 1 km × 1 km cell across the U.S. for each 8-day interval over the period from February 2000 to December 2006 using MODIS data. Our GPP estimates provide a spatially and temporally continuous measure of gross primary production for the U.S. that is a highly constrained by eddy covariance flux data. Our study demonstrated that our empirical approach is effective for upscaling eddy flux GPP data to the continental scale and producing continuous GPP estimates across multiple biomes. With these estimates, we then examined the patterns, magnitude, and interannual variability of GPP. We estimated a gross carbon uptake between 6.91 and 7.33 Pg C yr^− 1 for the conterminous U.S. Drought, fires, and hurricanes reduced annual GPP at regional scales and could have a significant impact on the U.S. net ecosystem carbon exchange. The sources of the interannual variability of U.S. GPP were dominated by these extreme climate events and disturbances.     

Antarctic ice sheet and sea ice regional albedo and temperature change, 1981-2000, from AVHRR Polar Pathfinder data

Spring-summer (November, December, January) ice sheet and sea ice regional surface albedo, surface temperature, sea ice concentration and sea ice extent averages and trends from 1981 to 2000 have been calculated for the Antarctic area. In this research the AVHRR Polar Pathfinder 5-km EASE-Grid Composites and the combined SMMR and SSMI data sets from the National Snow and Ice Data Center (NSIDC), Boulder, Colorado have been employed. A regional analysis has been made for five longitudinal sectors around Antarctica: the Weddell Sea (WS), the Indian Ocean (IO), the Pacific Ocean (PO), the Ross Sea (RS) and the Bellingshausen-Amundsen Sea (BS). The IO and PO sectors show ice sheet albedos of 0.85 and temperatures of − 25 °C. The corresponding values in the RS and BS sectors are 0.80 and − 16 °C respectively. The sea ice albedo is about 0.60 in the RS, BS and WS sectors and 0.55 in the IO and PO sectors. The average sea ice temperature varies around − 12 °C. All the sectors show slight increasing spring-summer albedo trends and decreasing spring-summer temperature trends and similar interannual variability in albedo and surface temperature. The steepest ice sheet albedo trend of 0.0019 ± 0.0009/yr is found in the RS sector. The steepest sea ice albedo trend of 0.0044 ± 0.0017 /yr occurs in the PO sector. The steepest temperature trends for both the ice sheet and sea ice occur in the BS sector, having values of − 0.075 ± 0.040 °C/yr and − 0.107 ± 0.027 °C/yr respectively. The sea ice concentration shows slight increasing trends, the highest being in the PO sector (0.3 ± 0.12%/yr), whereas the sea ice extent trends are near zero with the exception of the RS sector (14,700 ±8600 km²/yr) and the BS sector (− 13,000 ± 6400 km²/yr).