scholarly journals Evaluation of Collection-6 MODIS Land Surface Temperature Product Using Multi-Year Ground Measurements in an Arid Area of Northwest China

2018 ◽  
Vol 10 (11) ◽  
pp. 1852 ◽  
Author(s):  
Lei Lu ◽  
Tingjun Zhang ◽  
Tiejun Wang ◽  
Xiaoming Zhou
Keyword(s):  
Surface Temperature ◽  
Land Surface Temperature ◽  
Land Surface ◽  
Northwest China ◽  
Bare Soil ◽  
Vegetation Monitoring ◽  
Modis Land Surface Temperature ◽  
Global Circulation Models ◽  
In Situ Data

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.

Evaluating the potential of MODIS-LST for monitoring ground surface temperatures in the Maritime Antarctic (Barton Peninsula, King George Island, Antarctic). 

2021 ◽  
Author(s):  
Alejandro Corbea-Pérez ◽  
Gonçalo Vieira ◽  
Carmen Recondo ◽  
Joana Baptista ◽  
Javier F.Calleja ◽  
...  
Keyword(s):  
Surface Temperature ◽  
Land Surface Temperature ◽  
Land Surface ◽  
Ground Temperature ◽  
King George Island ◽  
Maritime Antarctic ◽  
In Situ Data ◽  
Surface Temperatures ◽  
Barton Peninsula

<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>

scholarly journals Validation of Collection 6 MODIS land surface temperature product using in situ measurements

2019 ◽  
Vol 225 ◽  
pp. 16-29 ◽  
Author(s):  
Si-Bo Duan ◽  
Zhao-Liang Li ◽  
Hua Li ◽  
Frank-M. Göttsche ◽  
Hua Wu ◽  
...  
Keyword(s):  
Surface Temperature ◽  
Land Surface Temperature ◽  
Land Surface ◽  
In Situ Measurements ◽  
Modis Land Surface Temperature

Evaluation of MODIS land surface temperature with in-situ snow surface temperature from CREST-SAFE

2017 ◽  
Vol 38 (16) ◽  
pp. 4722-4740 ◽  
Author(s):  
Carlos L. Pérez-Díaz ◽  
Tarendra Lakhankar ◽  
Peter Romanov ◽  
Jonathan Muñoz ◽  
Reza Khanbilvardi ◽  
...  
Keyword(s):  
Surface Temperature ◽  
Land Surface Temperature ◽  
Land Surface ◽  
Snow Surface ◽  
Modis Land Surface Temperature ◽  
Snow Surface Temperature

Long-term trends of surface reflectance derived from models, satellite and in-situ observations over polar areas

2020 ◽  
Author(s):  
Christian Lanconelli ◽  
Fabrizio Cappucci ◽  
Bernardo Mota ◽  
Nadine Gobron ◽  
Amelie Driemel ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Surface Temperature ◽  
Land Surface Temperature ◽  
Land Surface ◽  
Kendall Test ◽  
Surface Radiation ◽  
In Situ Data ◽  
In Situ Observations

<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>

scholarly journals A New Approach to Defining Uncertainties for MODIS Land Surface Temperature

2019 ◽  
Vol 11 (9) ◽  
pp. 1021 ◽  
Author(s):  
Darren Ghent ◽  
Karen Veal ◽  
Tim Trent ◽  
Emma Dodd ◽  
Harjinder Sembhi ◽  
...  
Keyword(s):  
Standard Deviation ◽  
Surface Temperature ◽  
Land Surface Temperature ◽  
Land Surface ◽  
Uncertainty Budget ◽  
In Situ Data ◽  
Uncertainty Model ◽  
Absolute Bias ◽  
Moderate Resolution Imaging Spectroradiometer

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.

scholarly journals Reconstruction of the Daily MODIS Land Surface Temperature Product Using the Two-Step Improved Similar Pixels Method

2021 ◽  
Vol 13 (9) ◽  
pp. 1671
Author(s):  
Junlei Tan ◽  
Tao Che ◽  
Jian Wang ◽  
Ji Liang ◽  
Yang Zhang ◽  
...  
Keyword(s):  
Land Cover ◽  
Surface Temperature ◽  
Land Surface Temperature ◽  
Land Surface ◽  
Water Cycle ◽  
Ancillary Data ◽  
Land Cover Types ◽  
Modis Land Surface Temperature ◽  
Yearly Average

The MODIS land surface temperature (LST) product is one of the most widely used data sources to study the climate and energy-water cycle at a global scale. However, the large number of invalid values caused by cloud cover limits the wide application of the MODIS LST. In this study, a two-step improved similar pixels (TISP) method was proposed for cloudy sky LST reconstruction. The TISP method was validated using a temperature-based method over various land cover types. The ground measurements were collected at fifteen stations from 2013 to 2018 during the Heihe Watershed Allied Telemetry Experimental Research (HiWATER) field campaign in China. The estimated theoretical clear-sky temperature indicates that clouds cool the land surface during the daytime and warm it at nighttime. For bare land, the surface temperature shows a clear seasonal trend and very similar across stations, with a cooling amplitude of 4.14 K in the daytime and a warming amplitude of 3.99 K at nighttime, as a yearly average. The validation result showed that the reconstructed LST is highly consistent with in situ measurements and comparable with MODIS LST validation accuracy, with a mean bias of 0.15 K at night (−0.43 K in the day), mean RMSE of 2.91 K at night (4.41 K in the day), and mean R2 of 0.93 at night (0.90 in the day). The developed method maximizes the potential of obtaining quality MODIS LST retrievals, ancillary data, and in situ observations, and the results show high accuracy for most land cover types.

scholarly journals Comprehensive In Situ Validation of Five Satellite Land Surface Temperature Data Sets over Multiple Stations and Years

2019 ◽  
Vol 11 (5) ◽  
pp. 479 ◽  
Author(s):  
Maria Martin ◽  
Darren Ghent ◽  
Ana Pires ◽  
Frank-Michael Göttsche ◽  
Jan Cermak ◽  
...  
Keyword(s):  
Land Cover ◽  
Surface Temperature ◽  
Land Surface Temperature ◽  
Satellite Data ◽  
Land Surface ◽  
Quantitative Information ◽  
Data Availability ◽  
Data Sets ◽  
In Situ Data

Global land surface temperature (LST) data derived from satellite-based infrared radiance measurements are highly valuable for various applications in climate research. While in situ validation of satellite LST data sets is a challenging task, it is needed to obtain quantitative information on their accuracy. In the standardised approach to multi-sensor validation presented here for the first time, LST data sets obtained with state-of-the-art retrieval algorithms from several sensors (AATSR, GOES, MODIS, and SEVIRI) are matched spatially and temporally with multiple years of in situ data from globally distributed stations representing various land cover types in a consistent manner. Commonality of treatment is essential for the approach: all satellite data sets are projected to the same spatial grid, and transformed into a common harmonized format, thereby allowing comparison with in situ data to be undertaken with the same methodology and data processing. The large data base of standardised satellite LST provided by the European Space Agency’s GlobTemperature project makes previously difficult to perform LST studies and applications more feasible and easier to implement. The satellite data sets are validated over either three or ten years, depending on data availability. Average accuracies over the whole time span are generally within ±2.0 K during night, and within ± 4.0 K during day. Time series analyses over individual stations reveal seasonal cycles. They stem, depending on the station, from surface anisotropy, topography, or heterogeneous land cover. The results demonstrate the maturity of the LST products, but also highlight the need to carefully consider their temporal and spatial properties when using them for scientific purposes.

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