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>

Global long-term land surface temperature for NOAA AVHRR: extension from 1981-2000 to 1981-2020

2021 ◽  
Author(s):  
Jin Ma ◽  
Ji Zhou
Keyword(s):  
Climate Change ◽  
Surface Temperature ◽  
Land Surface Temperature ◽  
Land Surface ◽  
Global Scale ◽  
Mean Bias Error ◽  
Bias Error ◽  
Important Indicator ◽  
Noaa Avhrr

<p>As an important indicator of land-atmosphere energy interaction, land surface temperature (LST) plays an important role in the research of climate change, hydrology, and various land surface processes. Compared with traditional ground-based observation, satellite remote sensing provides the possibility to retrieve LST more efficiently over a global scale. Since the lack of global LST before, Ma et al., (2020) released a global 0.05 ×0.05  long-term (1981-2000) LST based on NOAA-7/9/11/14 AVHRR. The dataset includes three layers: (1) instantaneous LST, a product generated based on an ensemble of several split-window algorithms with a random forest (RF-SWA); (2) orbital-drift-corrected (ODC) LST, a drift-corrected version of RF-SWA LST at 14:30 solar time; and (3) monthly averages of ODC LST. To meet the requirement of the long-term application, e.g. climate change, the period of the LST is extended from 1981-2000 to 1981-2020 in this study. The LST from 2001 to 2020 are retrieved from NOAA-16/18/19 AVHRR with the same algorithm for NOAA-7/8/11/14 AVHRR. The train and test results based on the simulation data from SeeBor and TIGR atmospheric profiles show that the accuracy of the RF-SWA method for the three sensors is consistent with the previous four sensors, i.e. the mean bias error and standard deviation less than 0.10 K and 1.10 K, respectively, under the assumption that the maximum emissivity and water vapor content uncertainties are 0.04 and 1.0 g/cm<sup>2</sup>, respectively. The preliminary validation against <em>in-situ</em> LST also shows a similar accuracy, indicating that the accuracy of LST from 1981 to 2020 are consistent with each other. In the generation code, the new LST has been improved in terms of land surface emissivity estimation, identification of cloud pixel, and the ODC method in order to generate a more reliable LST dataset. Up to now, the new version LST product (1981-2020) is under generating and will be released soon in support of the scientific research community.</p>

scholarly journals Earth Observations-Based Evapotranspiration in Northeastern Thailand

2019 ◽  
Vol 11 (2) ◽  
pp. 138 ◽  
Author(s):  
Chaolei Zheng ◽  
Li Jia ◽  
Guangcheng Hu ◽  
Jing Lu
Keyword(s):  
Remote Sensing ◽  
Soil Moisture ◽  
Surface Temperature ◽  
Land Surface Temperature ◽  
Land Surface ◽  
Microwave Remote Sensing ◽  
Spatiotemporal Pattern ◽  
Monsoon Climate ◽  
Thermal Remote Sensing ◽  
Clear Sky

Thailand is characterized by typical tropical monsoon climate, and is suffering serious water related problems, including seasonal drought and flooding. These issues are highly related to the hydrological processes, e.g., precipitation and evapotranspiration (ET), which are helpful to understand and cope with these problems. It is critical to study the spatiotemporal pattern of ET in Thailand to support the local water resource management. In the current study, daily ET was estimated over Thailand by ETMonitor, a process-based model, with mainly satellite earth observation datasets as input. One major advantage of the ETMonitor algorithm is that it introduces the impact of soil moisture on ET by assimilating the surface soil moisture from microwave remote sensing, and it reduces the dependence on land surface temperature, as the thermal remote sensing is highly sensitive to cloud, which limits the ability to achieve spatial and temporal continuity of daily ET. The ETMonitor algorithm was further improved in current study to take advantage of thermal remote sensing. In the improved scheme, the evaporation fraction was first obtained by land surface temperature—vegetation index triangle method, which was used to estimate ET in the clear days. The soil moisture stress index (SMSI) was defined to express the constrain of soil moisture on ET, and clear sky SMSI was retrieved according to the estimated clear sky ET. Clear sky SMSI was then interpolated to cloudy days to obtain the SMSI for all sky conditions. Finally, time-series ET at daily resolution was achieved using the interpolated spatio-temporal continuous SMSI. Good agreements were found between the estimated daily ET and flux tower observations with root mean square error ranging between 1.08 and 1.58 mm d−1, which showed better accuracy than the ET product from MODerate resolution Imaging Spectroradiometer (MODIS), especially for the forest sites. Chi and Mun river basins, located in Northeast Thailand, were selected to analyze the spatial pattern of ET. The results indicate that the ET had large fluctuation in seasonal variation, which is predominantly impacted by the monsoon climate.

scholarly journals Advances in Quantitative Earth Remote Sensing: Past, Present and Future

Sensors ◽  
2019 ◽  
Vol 19 (24) ◽  
pp. 5399 ◽  
Author(s):  
Ghassem R. Asrar
Keyword(s):  
Remote Sensing ◽  
Soil Moisture ◽  
Surface Temperature ◽  
Land Surface Temperature ◽  
Land Surface ◽  
Microwave Remote Sensing ◽  
Polar Regions ◽  
Earth System ◽  
Earth Remote Sensing ◽  
Earth’S System

A combination of multispectral visible, infra-red and microwave sensors on the constellation of international Earth-observing satellites are providing unprecedented observations for all Earth domains over multiple decades (i.e., atmosphere, land, oceans and polar regions). This Special Issue of Sensors is dedicated to papers that describe such advances in the field of Earth remote sensing and their applications to advance understanding of Earth’s planetary system and applying the resulting knowledge and information to meet the societal needs during recent decades. The papers accepted and published in this issue convey the exciting scientific and technical challenges and opportunities for remote sensing of all domains of Earth system, including terrestrial, aquatic and coastal ecosystems; bathymetry of coasts and islands; oceans and lakes; measurement of soil moisture and land surface temperature that affects both water resources and food production; and advances in use of sun-induced fluorescence (SIF) in measuring and monitoring the contribution of terrestrial vegetation in the cycling of carbon in Earth’s system. Measurements of SIF, for example, has had a profound impact on the field of terrestrial ecosystems research and modelling. The Earth Polychromatic Imaging Camera (EPIC) instrument on the Deep Space Climate Observatory (DSCVR) satellite located at the Sun–Earth Lagrange Point One, about 1.5 million miles away from Earth, is providing unique observations of the Earth’s full sun-lit disk from pole-to-pole and minute-by-minute, which overcomes a major limitation in temporal coverage of Earth by other polar-orbiting Earth-observing satellites. Active and passive microwave remote sensing instruments allow all-weather measurements and monitoring of clouds, weather phenomena, land-surface temperature and soil moisture by overcoming the presence of clouds that affect measurements by visible and infrared sensors. The use of powerful in-space lasers is allowing scientists and engineers to measure and monitor rapidly changing ice sheets in polar regions and mountain glaciers. These sensors and their measurements that are deployed on major space-based observatories and small- and micro-satellites, and the scientific knowledge they provide, are enhancing our understanding of planet Earth and development of Earth system models that are used increasingly to project future conditions due to Earth’s rapidly changing environmental conditions. Such knowledge and information are benefiting people, businesses and governments worldwide.

Application of ECOSTRESS multispectral LWIR images to assess topsoil properties: preliminary results on agricultural test sites in Central Italy

2020 ◽  
Author(s):  
Anvesh Rangisetty ◽  
Raffaele Casa ◽  
Victoria Ionca ◽  
Giovanni Laneve ◽  
Simone Pascucci ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Organic Carbon ◽  
Soil Organic Carbon ◽  
Surface Temperature ◽  
Spatial Resolution ◽  
Land Surface Temperature ◽  
Land Surface ◽  
Central Italy ◽  
Preliminary Results ◽  
Medium Resolution

<p>Ecosystem Spaceborne Thermal Radiometer Experiment on Space Station (ECOSTRESS) is a thermal infrared sensor, developed by NASA-JPL, launched in June 2018. ECOSTRESS acquires five LWIR spectral channels between 8 and 12 μm, with 70 m of spatial resolution at different times of the day and night.</p><p>The availability of multispectral TIR bands allows the retrieval of Land Surface Temperature (LST) and Land Surface Emissivity (LSE) by using well known procedures, like Temperature and Emissivity Separation (TES). The availability of LSE images in the LWIR atmospheric window at a medium resolution allows to estimate some topsoil/rock properties, for example those related to quartz diagnostic absorption features.</p><p>Furthermore, recent studies have shown that multispectral data in the LWIR region allows to retrieve quantitative information on topsoil properties, such as texture, carbon and nitrogen content, especially when applying multivariate statistical models [1] [2]. This study intends to verify the potential of night and day ECOSTRESS images for topsoil properties estimation.</p><p>To this aim, on specific experimental fields in Central Italy, soil sampling campaigns have been conducted to assess the topsoil properties like soil texture (clay, silt, sand) and soil organic carbon (SOC).</p><p>First, on these experimental fields, ECOSTRESS archive images were explored to identify the images in which the sampled fields are ploughed (i.e. bare soil conditions). Second, the ECO2LSTE products [3], containing the land surface temperature and emissivity, were downloaded from the USGS web site (https://ecostress.jpl.nasa.gov/data) and atmospherically corrected. Third, the TES algorithm was applied providing emissivity images at a spatial resolution of 70 m.</p><p>Last, the emissivity images were used to define a prediction model (calibration and validation) by using both Partial Least Squares Regression (PLSR) and Random Forest (RF).</p><p>The preliminary results seem to confirm: i) the potential of ECOSTRESS LWIR data to retrieve topsoil properties valuable for agronomical purposes at the regional scale, ii) the preliminary result of the multivariate analysis like PLSR and RF to derive model for topsoil properties (mainly clay and organic content) prediction  at a medium resolution scale.</p><p>References</p><ul><li>[1] Notesco, G., Weksler, S., & Ben-Dor, E. (2019). Mineral Classification of Soils Using Hyperspectral Longwave Infrared (LWIR) Ground-Based Data. Remote Sensing, 11(12), 1429.</li> <li>[2] Pascucci, S., Casa, R., Belviso, C., Palombo, A., Pignatti, S., & Castaldi, F. (2014). Estimation of soil organic carbon from airborne hyperspectral thermal infrared data: A case study.European journal of soil science, 65(6), 865-875.</li> <li>[3] Silvestri, M., Romaniello, V., Hook, S., Musacchio, M., Teggi, S., & Buongiorno, M. F. (2020). First Comparisons of Surface Temperature Estimations between ECOSTRESS, ASTER and Landsat 8 over Italian Volcanic and Geothermal Areas. Remote Sensing, 12(1), 184.</li> </ul>

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 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 A global long-term (1981–2000) land surface temperature product for NOAA AVHRR

2020 ◽  
Author(s):  
Jin Ma ◽  
Ji Zhou ◽  
Frank-Michael Göttsche ◽  
Shunlin Liang ◽  
Shaofei Wang ◽  
...  
Keyword(s):  
Surface Temperature ◽  
Land Surface Temperature ◽  
Land Surface ◽  
Radiation Budget ◽  
Mean Bias Error ◽  
Data Set ◽  
Noaa Avhrr ◽  
Orbital Drift

Abstract. Land Surface Temperature (LST) plays an important role in the research of climate change and various land surface processes. Before 2000, global LST products with relatively high temporal and spatial resolutions are scarce, despite of a variety of operational satellite LST products. In this study, a global 0.05° × 0.05° historical LST product is generated from NOAA AVHRR data (1981–2000), which includes three data layers: (1) instantaneous LST, a product generated by integrating several Split-Window Algorithms with a Random Forest (RF-SWA); (2) orbital drift corrected (ODC) LST, a drift corrected version of RF-SWA LST; (3) monthly averages of ODC LST. For an assumed maximum uncertainty in emissivity and column water vapour content of 0.04 and 1.0 g/cm2, respectively and evaluated against the simulation data set, the RF-SWA method has a Mean Bias Error (MBE) of less than 0.10 K and a Standard Deviation (STD) of 1.10 K. To compensate the influence of orbital drift on LST, the retrieved RF-SWA LST was normalized with an improved ODC method. The RF-SWA LST were validated with in-situ LST from Surface Radiation Budget (SURFRAD) sites and water temperatures obtained from the National Data Buoy Center (NDBC). Against the in-situ LST, the RF-SWA LST has a MBE 0.03 K with a range of −1.59 K–2.71 K and STD is 1.18 K with a range of 0.84 K–2.76 K. Since water temperature only changes slowly, the validation of ODC LST was limited to SURFRAD sites, for which the MBE is 0.54 K with a range of −1.05 K to 3.01 K and STD is 3.57 K with a range of 2.34 K to 3.69 K, indicating a good product accuracy. As global historical datasets, the new AVHRR LST products are useful for filling the gaps in long-term LST data. Furthermore, the new LST products can be used as input to related land surface models and environmental applications. Furthermore, in support of the scientific research community, the datasets are freely available at https://doi.org/10.5281/zenodo.3934354 for RF-SWA LST (Ma et al., 2020a); https://doi.org/10.5281/zenodo.3936627 for ODC LST (Ma et al., 2020c); https://doi.org/10.5281/zenodo.3936641 for monthly averaged LST (Ma et al., 2020b).

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