Localized land surface temperature retrieval from the MODIS Level-1b data using water vapor and in situ data

Author(s):  
Kai Wang ◽  
Qiang Liu ◽  
Qinhuo Liu
2021 ◽  
Author(s):  
Alejandro Corbea-Pérez ◽  
Gonçalo Vieira ◽  
Carmen Recondo ◽  
Joana Baptista ◽  
Javier F.Calleja ◽  
...  

<p>Land surface temperature is an important factor for permafrost modelling as well as for understanding the dynamics of Antarctic terrestrial ecosystems (Bockheim et al. 2008). In the South Shetland Islands the distribution of permafrost is complex (Vieira et al. 2010) and the use of remote sensing data is essential since the installation and maintenance of an extensive network of ground-based stations are impossible. Therefore, it is important to evaluate the applicability of satellites and sensors by comparing data with in-situ observations. In this work, we present the results from the analysis of land surface temperatures from Barton Peninsula, an ice-free area in King George Island (South Shetlands). We have studied the period from March 1, 2019 to January 31, 2020 using data from the Moderate Resolution Imaging Spectroradiometer (MODIS) Land Surface Temperature (LST) and in-situ data from 6 ground temperature loggers. MOD11A1 and MYD11A1 products, from TERRA and AQUA satellites, respectively, were used, following the application of MODIS quality filters. Given the scarce number of high-quality data as defined by MODIS, all average LST with error ≤ 2K were included. Dates with surface temperature below -20ºC, which are rare in the study area, and dates when the difference between MODIS and in-situ data exceeded 10ºC were also examined. In both cases, those days on which MOD09GA/MYD09GA products showed cloud cover were eliminated. Eight in-situ ground temperature measurements per day were available, from which the one nearest to the time of satellite overpass was selected for comparison with MODIS-LST. The results obtained show a better correlation with daytime data than with nighttime data. Specifically, the best results are obtained with daytime data from AQUA (R<sup>2</sup> between 0.55 and 0.81). With daytime data, correlation between MODIS-LST and in-situ data was verified with relative humidity (RH) values provided by King Sejong weather station, located in the study area. When RH is lower, the correlation between LST and in-situ data improves: we obtained correlation coefficients between 0.6 - 0.7 for TERRA data and 0.8 - 0.9 for AQUA data with RH values lower than 80%. The results suggest that MODIS can be used for temperature estimation in the ice-free areas of the Maritime Antarctic.</p><p>References:</p><p>Bockheim, J. G., Campbell, I. B., Guglielmin, M., and López- Martınez, J.: Distribution of permafrost types and buried ice in ice free areas of Antarctica, in: 9th International Conference on Permafrost, 28 June–3 July 2008, Proceedings, University of Alaska Press, Fairbanks, USA, 2008, 125–130.</p><p>Vieira, G.; Bockheim, J.; Guglielmin, M.; Balks, M.; Abramov, A. A.; Boelhouwers, J.; Cannone, N.; Ganzert, L.; Gilichinsky, D. A.; Goryachkin, S.; López-Martínez, J.; Meiklejohn, I.; Raffi, R.; Ramos, M.; Schaefer, C.; Serrano, E.; Simas, F.; Sletten, R.; Wagner, D. Thermal State of Permafrost and Active-layer Monitoring in the Antarctic: Advances During the International Polar Year 2007-2009. Permafr. Periglac. Process. 2010, 21, 182–197.</p><p> </p><p>Acknowledgements</p><p>This work was made possible by an internship at the IGOT, University of Lisbon, Portugal, funded by the Principality of Asturias (code EB20-16).</p><p> </p>


2018 ◽  
Vol 10 (11) ◽  
pp. 1852 ◽  
Author(s):  
Lei Lu ◽  
Tingjun Zhang ◽  
Tiejun Wang ◽  
Xiaoming Zhou

Moderate Resolution Imaging Spectroradiometer (MODIS) land surface temperature (LST) products are widely used in ecology, hydrology, vegetation monitoring, and global circulation models. Compared to the collection-5 (C5) LST products, the newly released collection-6 (C6) LST products have been refined over bare soil pixels. This study aims to evaluate the C6 MODIS 1-km LST product using multi-year in situ data covering barren surfaces. Evaluation using all in situ data shows that the MODIS C6 LSTs are underestimated with a root-mean-square error (RMSE) of 2.59 K for the site in the Gobi area, 3.05 K for the site in the sand desert area, and 2.86 K for the site in the desert steppe area at daytime. For nighttime LSTs, the RMSEs are 2.01 K, 2.88 K, and 1.80 K for the three sites, respectively. Both biases and RMSEs also show strong seasonal signals. Compared to the error of C5 1-km LSTs, the RMSE of C6 1-km LST product is smaller, especially for daytime LSTs, with a value of 2.24 K compared to 3.51 K. The large errors in the sand desert region are presumably due to the lack of global representativeness of the magnitude of emissivity adjustment and misclassification for the barren surface causing error in emissivities. It indicates that the accuracy of the MODIS C6 LST product might be further improved through emissivity adjustment with globally representative magnitude and accurate land cover classification. From this study, the MODIS C6 1-km LST product is recommended for applications.


2020 ◽  
Author(s):  
Christian Lanconelli ◽  
Fabrizio Cappucci ◽  
Bernardo Mota ◽  
Nadine Gobron ◽  
Amelie Driemel ◽  
...  

<div> <p>Nowadays, an increasingly amount of remote sensing and in-situ data are extending over decades. They contribute to increase the relevance of long-term studies aimed to deduce the mechanisms underlying the climate change dynamics. The aim of this study is to investigate the coherence between trends of different long-term climate related variables including the surface albedo (A) and land surface temperature (LST) as obtained by remote sensing platforms, models and in-situ observations. </p> </div><div> <p>Directional-hemispherical and bi-hemispherical broadband surface reflectances as derived from MODIS-MCD43 (v006) and MISR, and the analogous products of the Copernicus Global Land (CGLS) and C3S services derived from SPOT-VEGETATION, PROBA-V and AVHRR (v0 and v1), have been harmonized and, together with the ECMWF ERA-5 model, assessed with respect ground data taken over polar areas, over a temporal window spanning the last 20 years.  </p> </div><div> <p>The benchmark was established using in-situ measurements provided from the Baseline Surface Radiation Network (BSRN) over four Arctic and four Antarctic sites. The 1-minute resolution datasets of broadband upwelling and down-welling radiation, have been reduced to directional- and bi-hemispherical reflectances, with the same time scale of satellite products (1-day, 10-days, monthly).  </p> </div><div> <p>A similar approach was used to investigate the fitness for purpose of Land Surface Temperature as derived by MODIS (MOD11), ECMWF ERA-5, with respect to the brightness temperature derived using BSRN measurements over the longwave band.  </p> </div><div> <p>The entire temporal series are decomposed into seasonal and residual components, and then the presence of monotonic significant trends are assessed using the non-parametric Kendall test. Preliminary results shown a strong correlation between negative albedo trends and positive LST trends, especially in arctic regions. </p> </div>


2019 ◽  
Vol 11 (9) ◽  
pp. 1021 ◽  
Author(s):  
Darren Ghent ◽  
Karen Veal ◽  
Tim Trent ◽  
Emma Dodd ◽  
Harjinder Sembhi ◽  
...  

The accuracy of land surface temperature (LST) observations is critical to many applications. Any observation of LST is subject to incomplete knowledge, so an accurate assessment of the uncertainty budget is critical. We present a comprehensive and consistent approach to determining an uncertainty budget for LST products. We apply this approach to the Moderate Resolution Imaging Spectroradiometer (MODIS) instrument on-board the Aqua satellite. In order to generate the uncertainty model, a new implementation of the generalised split-window algorithm is applied, in which retrieval coefficients are categorised by viewing angle and water vapour. Validation of the LST against in situ data shows a mean absolute bias of 0.37 K for daytime and 0.73 K for nighttime. The average standard deviation per site is 1.53 K for daytime and 1.21 K for nighttime. Uncertainties from the implemented model are estimates in their own right and are also validated. We do this by comparing the standard deviation of the differences between the satellite and in situ LSTs, and the total uncertainties of the validation matchups. We show that the uncertainty model provides a good fit. Our approach offers a framework for quantifying uncertainties for LST that is equally applicable across different sensors and different retrieval approaches.


Sign in / Sign up

Export Citation Format

Share Document