Towards an operative land surface temperature in-situ measurements system for remote sensing models validations

2005 ◽  
Author(s):  
A. Chahboun ◽  
N. Raissouni ◽  
M. Essaaid
Keyword(s):  
Remote Sensing ◽  
Surface Temperature ◽  
Land Surface Temperature ◽  
Land Surface ◽  
In Situ Measurements

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

Global Land Surface Temperature from historical NOAA AVHRR datasets (1981-2000)

2020 ◽  
Author(s):  
Jin Ma ◽  
Ji Zhou ◽  
Frank-Michael Göttsche ◽  
Shaofei Wang
Keyword(s):  
Climate Change ◽  
Remote Sensing ◽  
Surface Temperature ◽  
Spatial Resolution ◽  
Land Surface Temperature ◽  
Land Surface ◽  
Microwave Remote Sensing ◽  
Community Land Model ◽  
Noaa Avhrr

<p>As one of the most important indicators in the energy exchange between land and atmosphere, Land Surface Temperature (LST) plays an important role in the research of climate change and various land surface processes. In contrast to <em>in-situ</em> measurements, satellite remote sensing provides a practical approach to measure global and local land surface parameters. Although passive microwave remote sensing offers all-weather observation capability, retrieving LST from thermal infra-red data is still the most common approach. To date, a variety of global LST products have been published by the scientific community, e.g. MODIS and (A)ASTR /SLSTR LST products, and used in a broad range of research fields. Several global and regional satellite retrieved LSTs are available since 1995. However, the temporal-spatial resolution before 2000 is generally considerably lower than that after 2000. According to the latest IPCC report, 1983 – 2012 are the warmest 30 years for nearly 1400 years. Therefore, for global climate change research, it is meaningful to extend the time series of global LST products with a relatively higher temporal-spatial resolution to before 2000, e.g. that of NOAA AVHRR. In this study, global daily NOAA AVHRR LST products with 5-km spatial resolution were generated for 1981-2000. The LST was retrieved using an ensemble of RF-SWAs (Random Forest and Split-Window Algorithm). For a maximum uncertainty in emissivity and water vapor content of 0.04 and 1.0 g/cm<sup>2</sup>, respectively, the training and testing with simulated datasets showed a retrieval accuracy with MBE of less than 0.1 K and STD of 1.1 K. The generated RF-SWA LST product was also evaluated against <em>in-situ</em> measurements: for water sites of the National Data Buoy Center (NDBC) between 1981 and 2000, it showed an accuracy similar to that for the simulated data, with a small MBE of less than 0.1 K and a STD between 0.79 K and 1.02 K. For SURFRAD data collected between 1995 and 2000, the MBE is -0.03 K with a range of -1.20 K – 0.54 K and a STD with a mean of 2.55 K and a range of 2.08 K – 3.0 K (site dependent). As a new global historical dataset, the RF-SWA LST product can help to close the gap in long-term LST data available to climate research. Furthermore, the data can be used as input to land surface process models, e.g. the Community Land Model (CLM). In support of the scientific research community, the RF-SWA LST product will be freely available at the National Earth System Science Data Center of China (http://www.geodata.cn/).</p>

scholarly journals Sensitivity Analysis and Validation of Daytime and Nighttime Land Surface Temperature Retrievals from Landsat 8 Using Different Algorithms and Emissivity Models

2020 ◽  
Vol 12 (17) ◽  
pp. 2776 ◽  
Author(s):  
Aliihsan Sekertekin ◽  
Stefania Bonafoni
Keyword(s):  
Surface Temperature ◽  
Land Surface Temperature ◽  
Land Surface ◽  
The United States ◽  
In Situ Measurements ◽  
Surface Radiation ◽  
Radiation Budget ◽  
Sensitivity Analyses ◽  
Landsat 8

Land Surface Temperature (LST) is a substantial element indicating the relationship between the atmosphere and the land. This study aims to examine the efficiency of different LST algorithms, namely, Single Channel Algorithm (SCA), Mono Window Algorithm (MWA), and Radiative Transfer Equation (RTE), using both daytime and nighttime Landsat 8 data and in-situ measurements. Although many researchers conducted validation studies of daytime LST retrieved from Landsat 8 data, none of them considered nighttime LST retrieval and validation because of the lack of Land Surface Emissivity (LSE) data in the nighttime. Thus, in this paper, we propose using a daytime LSE image, whose acquisition is close to nighttime Thermal Infrared (TIR) data (the difference ranges from one day to four days), as an input in the algorithm for the nighttime LST retrieval. In addition to evaluating the three LST methods, we also investigated the effect of six Normalized Difference Vegetation Index (NDVI)-based LSE models in this study. Furthermore, sensitivity analyses were carried out for both in-situ measurements and LST methods for satellite data. Simultaneous ground-based LST measurements were collected from Atmospheric Radiation Measurement (ARM) and Surface Radiation Budget Network (SURFRAD) stations, located at different rural environments of the United States. Concerning the in-situ sensitivity results, the effect on LST of the uncertainty of the downwelling and upwelling radiance was almost identical in daytime and nighttime. Instead, the uncertainty effect of the broadband emissivity in the nighttime was half of the daytime. Concerning the satellite observations, the sensitivity of the LST methods to LSE proved that the variation of the LST error was smaller than daytime. The accuracy of the LST retrieval methods for daytime Landsat 8 data varied between 2.17 K Root Mean Square Error (RMSE) and 5.47 K RMSE considering all LST methods and LSE models. MWA with two different LSE models presented the best results for the daytime. Concerning the nighttime accuracy of the LST retrieval, the RMSE value ranged from 0.94 K to 3.34 K. SCA showed the best results, but MWA and RTE also provided very high accuracy. Compared to daytime, all LST retrieval methods applied to nighttime data provided highly accurate results with the different LSE models and a lower bias with respect to in-situ measurements.

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 Long Term Validation of Land Surface Temperature Retrieved from MSG/SEVIRI with Continuous in-Situ Measurements in Africa

2016 ◽  
Vol 8 (5) ◽  
pp. 410 ◽  
Author(s):  
Frank-M. Göttsche ◽  
Folke-S. Olesen ◽  
Isabel Trigo ◽  
Annika Bork-Unkelbach ◽  
Maria Martin
Keyword(s):  
Surface Temperature ◽  
Land Surface Temperature ◽  
Land Surface ◽  
In Situ Measurements

scholarly journals Urban Land Surface Temperature Monitoring and Surface Thermal Runoff Pollution Evaluation Using UAV Thermal Remote Sensing Technology

2021 ◽  
Vol 13 (20) ◽  
pp. 11203
Author(s):  
Shanshan Xu ◽  
Kun Yang ◽  
Yuanting Xu ◽  
Yanhui Zhu ◽  
Yi Luo ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Surface Water ◽  
Surface Temperature ◽  
Water Temperature ◽  
Land Surface Temperature ◽  
Land Surface ◽  
Urban Surface ◽  
Surface Water Temperature ◽  
Before And After

With the continuous advancement of urbanization, the impervious surface expands. Urbanization has changed the structure of the natural land surface and led to the intensification of the urban heat island (UHI) effect. This will affect the surface runoff temperature, which, in turn, will affect the surface water temperature of urban lakes. This study will use UAS TIR (un-manned aerial system thermal infrared radiance) remote sensing and in situ observation technology to monitor the urban space surface temperature and thermal runoff in Kunming, Yunnan, in summer; explore the feasibility of UAS TIR remote sensing to continuously observe urban surface temperature during day and night; and analyze thermal runoff pollution. The results of the study show that the difference between UAS TIR LSTs and in situ LSTs (in situ air temperature 10 cm above the ground.) varies with the type of land covers. Urban surface thermal runoff has varying degrees of impact on water bodies. Based on the influence of physical factors such as vegetation and buildings and meteorological factors such as solar radiation, the RMSE between UAS LSTs and in situ LSTs varies from 1 to 5 °C. Land cover types such as pervious bricks, asphalt, and cement usually show higher RMSE values. Before and after rainfall, the in situ data of the lake surface water temperature (LSWT) showed a phenomenon of first falling and then rising. The linear regression analysis results show that the R2 of the daytime model is 0.92, which has high consistency; the average R2 at night is 0.38; the averages R2 before and after rainfall are 0.50 and 0.83, respectively; and the average RMSE is 1.94 °C. Observational data shows that thermal runoff quickly reaches thermal equilibrium with the land surface temperature about 30 min after rainfall. The thermal runoff around the lake has a certain warming effect on LSWT.

scholarly journals Uncertainty and Sensitivity Analysis of a Remote-Sensing-Based Penman–Monteith Model to Meteorological and Land Surface Input Variables

2021 ◽  
Vol 13 (5) ◽  
pp. 882
Author(s):  
Nobuhle P. Majozi ◽  
Chris M. Mannaerts ◽  
Abel Ramoelo ◽  
Renaud Mathieu ◽  
Wouter Verhoef
Keyword(s):  
Remote Sensing ◽  
Surface Temperature ◽  
Land Surface Temperature ◽  
Land Surface ◽  
Temporal Frequency ◽  
Vapour Pressure Deficit ◽  
Remote Sensing Data ◽  
Area Index ◽  
Input Variables

This study analysed the uncertainty and sensitivity of core and intermediate input variables of a remote-sensing-data-based Penman–Monteith (PM-Mu) evapotranspiration (ET) model. We derived absolute and relative uncertainties of core measured meteorological and remote-sensing-based atmospheric and land surface input variables and parameters of the PM-Mu model. Uncertainties of important intermediate data components (i.e., net radiation and aerodynamic and surface resistances) were also assessed. To estimate the instrument measurement uncertainties of the in situ meteorological input variables, we used the reported accuracies of the manufacturers. Observational accuracies of the remote sensing input variables (land surface temperature (LST), land surface emissivity (εs), leaf area index (LAI), land surface albedo (α)) were derived from peer-reviewed satellite sensor validation reports to compute their uncertainties. The input uncertainties were propagated to the final model’s evapotranspiration estimation uncertainty. Our analysis indicated relatively high uncertainties associated with relative humidity (RH), and hence all the intermediate variables associated with RH, like vapour pressure deficit (VPD) and the surface and aerodynamic resistances. This is in contrast to other studies, which reported LAI uncertainty as the most influential. The semi-arid conditions and seasonality of the regional South African climate and high temporal frequency of the variations in VPD, air and land surface temperatures could explain the uncertainties observed in this study. The results also showed the ET algorithm to be most sensitive to the air-land surface temperature difference. An accurate assessment of those in situ and remotely sensed variables is required to achieve reliable evapotranspiration model estimates in these generally dry regions and climates. A significant advantage of the remote-sensing-based ET method remains its full area coverage in contrast to classic-point (station)-based ET estimates.

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