Comment on essd-2022-335

Abstract. Land surface temperature (LST) is a key variable for monitoring and evaluating global long-term climate change. However, existing satellite-based twice-daily LST products only date back to 2000, which makes it difficult to obtain robust long-term temperature variations. In this study, we developed the first global historical twice-daily LST dataset (GT-LST), with a spatial resolution of 0.05∘, using Advanced Very High Resolution Radiometer (AVHRR) Level-1b Global Area Coverage (GAC) data from 1981 to 2021. The GT-LST product was generated using four main processes: (1) GAC data reading, calibration, and preprocessing using open-source Python libraries; (2) cloud detection using the AVHRR-Phase I algorithm; (3) land surface emissivity estimation using an improved method considering annual land cover changes; (4) LST retrieval based on a nonlinear generalized split-window algorithm. Validation with in situ measurements from Surface Radiation Budget (SURFRAD) sites and Baseline Surface Radiation Network sites showed that the overall root-mean-square errors (RMSEs) of GT-LST varied from 1.6 to 4.0 K, and nighttime LSTs were typically better than daytime LSTs. Intercomparison with the Moderate Resolution Imaging Spectroradiometer LST products (MYD11A1 and MYD21A1) revealed that the overall root-mean-square difference (RMSD) was approximately 3.0 K. Compared with MYD11A1 LST, GT-LST was overestimated, and relatively large RMSDs were obtained during the daytime, spring, and summer, whereas the significantly smaller positive bias was obtained between GT-LST and MYD21A1 LST. Furthermore, we compared our newly generated dataset with a global AVHRR daytime LST product at the selected measurements of SURFRAD sites (i.e., measurements of these two satellite datasets were valid), which revealed similar accuracies for the two datasets. However, GT-LST can additionally provide nighttime LST, which can be combined with daytime observations estimating relatively accurate monthly mean LST, with an RMSE of 2.7 K. Finally, we compared GT-LST with a regional twice-daily AVHRR LST product over continental Africa in different seasons, with RMSDs ranging from 2.1 to 4.3 K. Considering these advantages, the proposed dataset provides a better data source for a range of research applications. GT-LST is freely available at https://doi.org/10.5281/zenodo.7113080 (1981–2000) (Li et al., 2022a), https://doi.org/10.5281/zenodo.7134158 (2001–2005) (Li et al., 2022b), and https://doi.org/10.5281/zenodo.7813607 (2006–2021) (J. H. Li et al., 2023).

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 a variety of operational satellite LST products. In this study, a global 0.05∘×0.05∘ historical LST product is generated from NOAA advanced very-high-resolution radiometer (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; and (3) monthly averages of ODC LST. For an assumed maximum uncertainty in emissivity and column water vapor content of 0.04 and 1.0 g cm−2, respectively, evaluated against the simulation dataset, the RF-SWA method has a mean bias error (MBE) of less than 0.10 K and a standard deviation (SD) of 1.10 K. To compensate for 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 of 0.03 K with a range of −1.59–2.71 K, and SD is 1.18 K with a range of 0.84–2.76 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 to 3.01 K and SD is 3.57 K with a range of 2.34 to 3.69 K, indicating 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), and https://doi.org/10.5281/zenodo.3936641 for monthly averaged LST (Ma et al., 2020b).

Northern high latitudes have experienced pronounced warming throughout the last decades with particularly high temperatures during winter and spring. Due to Arctic Amplification, the Arctic region is warming thrice as fast as anywhere else. The warming affects the sensible ecosystem, vegetation dynamics and the cryosphere (sea ice, snow and permafrost). Permafrost, which is a crucial component of arctic ecosystems, is particularly sensitive to increasing air temperatures and changes in the snow regime. Climate change has a high impact in these regions because thawing affects the stability of the bedrock, damages infrastructures and releases massive quantities of organic carbon. Permafrost cannot directly be observed from space, but permafrost models link physical surface variables such as land surface temperature (LST) to the thermal ground regime. Models are an important addition to boreholes to monitor the status of the permafrost at hemispheric scale. On a global scale, observation of LST is only available from very few in-situ stations or climate models with coarse spatial resolution. Both data sources are not sufficient to model fine-scaled features. In contrast, LST information retrieved from satellite data has high spatiotemporal coverage.To compute LST on a hemispheric scale, we use the Advanced Very High Resolution Radiometer (AVHRR) Global Area Coverage (GAC) data set starting in 1981. The AVHRR on board the NOAA and MetOp satellite series now covers more than four decades. AVHRR’s two thermal infrared channels allow applying the split-window (SW) method to reduce the atmospheric effect and retrieve LST. Split-window algorithms (SWA) performances depend on the quality of SW coefficients. These are empirical coefficients, which are retrieved by fitting the SWA to a calibration database. Here, the calibration data is generated by running a radiative transfer (RT) model. The input profiles of the RT are selected to cover typical atmospheric conditions occurring in permafrost regions. The coefficients are adjusted for different water vapour and satellite viewing conditions. Cloud and water masks as well as fractional snow cover information from the ESA CCI snow project and emissivity data are included in the final LST retrieval algorithm. Besides, a machine learning algorithm was applied to improve the spatial resolution of the GAC data to generate a 40-year time series with a spatial resolution of 1km. The first validation results of the LST time series are shown.

To further verify the modified Becker’s split-window approach for retrieving land surface temperature (LST) from long-term Advanced Very High Resolution Radiometer (AVHRR) data, a cross-validation and a radiance-based (R-based) validation are performed and examined in this paper. In the cross-validation, 3481 LST data pairs are extracted from the AVHRR LST product retrieved with the modified Becker’s approach and compared with the Moderate Resolution Imaging Spectroradiometer (MODIS) LST product (MYD11A1) for the period 2002–2008, relative to the positions of 548 weather stations in China. The results show that in most cases, the AVHRR LST values are higher than the MYD11A1. When the AVHRR LSTs are adjusted with a linear regression, the values are close to the MYD11A1, showing a good linear relationship between the two datasets (R 2 = 0.91). In the R-based validation, comparison is made between AVHRR LST retrieved from the modified Becker’s approach and the inversed LST from the Moderate Resolution Transmittance Model (MODTRAN) consolidated with observed temperature and humidity profiles at four radiosonde stations. The results show that the retrieved AVHRR LST deviates from the MODTRAN inversed LST by–1.3 (–2.5) K when the total water vapor amount is less (larger) than 20 mm. This provides useful hints for further improvement of the LST retrieval algorithms’ accuracy and consistency.

Improving the urban thermal environment can enhance humans’ well-being. Nevertheless, it was not clear which land-use/land-cover (LU/LC) indices were optimal for explaining land surface temperatures (LSTs) and how they affected LSTs in cities in different climatic zones, especially during the nighttime. Thus, the Aqua/MODIS and Landsat/OLI data were mainly used to explore the optimal indices of building, vegetation, water and bare soil and to analyze their effects on LSTs in eleven urban centers in global distinct climatic regions. Results showed several LU/LC indices had high probabilities of being optimal indices to explain LSTs under different conditions. The daytime LSTs were usually significantly negatively correlated with vegetation indices and positively correlated with building and bare soil indices (p < 0.05). These relationships were stronger in the summer than winter. The nighttime LSTs were usually significantly positively and negatively correlated with building and vegetation indices in the summer, respectively (p < 0.05). These correlations were generally weaker during the nighttime than daytime. The nighttime LSTs were significantly positively and negatively correlated with water and bare soil indices, respectively (p < 0.05). Significant linear multiple regressions commonly existed between daytime and nighttime LSTs and four kinds of LU/LC indices (p < 0.05). These findings helped optimize urban thermal comfort, downscale city LSTs, etc.

Land surface temperature (LST) is a key variable in the determination of land surface energy exchange processes from local to global scales. Accurate ground measurements of LST are necessary for a number of applications including validation of satellite LST products or improvement of both climate and numerical weather prediction models. With the objective of assessing the quality of in situ measurements of LST and to evaluate the quantitative uncertainties in the ground-based LST measurements, intensive field experiments were conducted at NOAA’s Air Resources Laboratory (ARL)’s Atmospheric Turbulence and Diffusion Division (ATDD) in Oak Ridge, Tennessee, USA, from October 2015 to January 2016. The results of the comparison of LSTs retrieved by three narrow angle broadband infrared temperature sensors (IRT), hemispherical longwave radiation (LWR) measurements by pyrgeometers, forward looking infrared camera with direct LSTs by multiple thermocouples (TC), and near surface air temperature (AT) are presented here. The brightness temperature (BT) measurements by the IRTs agreed well with a bias of <0.23 °C, and root mean square error (RMSE) of <0.36 °C. The daytime LST(TC) and LST(IRT) showed better agreement (bias = 0.26 °C and RMSE = 0.67 °C) than with LST(LWR) (bias > 1.1 and RMSE > 1.46 °C). In contrast, the difference between nighttime LSTs by IRTs, TCs, and LWR were <0.47 °C, whereas nighttime AT explained >81% of the variance in LST(IRT) with a bias of 2.64 °C and RMSE of 3.6 °C. To evaluate the annual and seasonal differences in LST(IRT), LST(LWR) and AT, the analysis was extended to four grassland sites in the USA. For the annual dataset of LST, the bias between LST (IRT) and LST (LWR) was <0.7 °C, except at the semiarid grassland (1.5 °C), whereas the absolute bias between AT and LST at the four sites were <2 °C. The monthly difference between LST (IRT) and LST (LWR) (or AT) reached up to 2 °C (5 °C), whereas half-hourly differences between LSTs and AT were several degrees in magnitude depending on the site characteristics, time of the day and the season.

Assessing the role of Mediterranean evergreen oaks canopy cover in land surface albedo and temperature using a remote sensing-based approach

Optimizing the spatial distribution of urban functional zones (UFZs) effectively improves the thermal environment. This study utilized an enhanced regression tree model and relied on Ecosystem Spaceborne Thermal Radiometer Experiment data to analyze the relative contributions and marginal effects of 2-D/3-D urban morphological factors on the diurnal land surface temperature (LST) in Shenyang, China. The results showed that public and residential areas dominated Shenyang's UFZs. The temperature in industrial areas was the highest during the day, and residential and commercial functional areas are high-temperature concentration areas. Furthermore, the effects of the urban spatial morphology on the LST differed between diverse time points and UFZs. The digital elevation model and the normalized difference vegetation index contributed significantly to daytime and nighttime LSTs. Construction indicators, such as the normalized difference built-up index and the proportion of construction land, significantly impacted commercial services. Residential daytime LST had a large contribution value, and the sum of its contribution rates reached approximately 30%. Population greatly contributed to the nighttime LST of the industrial and residential zones, accounting for 16.77% and 22.06%, respectively. Vegetation contributed to the cooling effect on daytime LST in summer, especially in industrial areas, contributing 29.79%. In addition, 3-D indicators, such as building height and building density, contributed to diurnal LST. Finally, when the proportion of construction land reached approximately 45%, it negatively affected LST. In this study, the main factors affecting day and night LSTs were identified, and this work acts as a relevant strategic reference for alleviating the urban heat island effect.

Air temperature (Tair) near the ground surface is a fundamental descriptor of terrestrial environment conditions and one of the most widely used climatic variables in global change studies. The main objective of this study was to explore the possibility of retrieving high-resolution Tair from the Moderate Resolution Imaging Spectroradiometer (MODIS) land surface temperature (LST) products, covering complex terrain in Northeast China. The All Subsets Regression (ASR) method was adopted to select the predictors and build optimal multiple linear regression models for estimating maximum (Tmax), minimum (Tmin), and mean (Tmean) air temperatures. The relative importance of predictors in these models was evaluated via the Standardized Regression Coefficients (SRCs) method. The results indicated that the optimal models could estimate the Tmax, Tmin, and Tmean with relatively high accuracies (Model Efficiency ≥ 0.90). Both LST and day length (DL) predictors were important in estimating Tmax (SRCs: daytime LST = 0.53, DL = 0.35), Tmin (SRCs: nighttime LST = 0.74, DL = 0.23), and Tmean (SRCs: nighttime LST = 0.72, DL = 0.28). Models predicting Tmin and Tmean had better performance than the one predicting Tmax. Nighttime LST was better at predicting Tmin and Tmean than daytime LST data at predicting Tmax. Land covers had noticeable influences on estimating Tair, and even seasonal vegetation greening could result in temporal variations of model performance. Air temperature could be accurately estimated using remote sensing, but the model performance was varied across different spatial and temporal scales. More predictors should be incorporated for the purpose of improving the estimation of near surface Tair from the MODIS LST production.

Downscaling microwave soil moisture (SM) with optical/thermal remote sensing data has considerable application potential. Spatial correlations between SM and land surface temperature (LST) or LST-derived SM indexes (SMIs) are vital to the current optical/thermal and microwave fusion downscaling methods. In this study, the spatial correlations were evaluated at the same spatial scale using SMAPVEX12 SM data and MODIS day/night LST products. LST-derived SMIs was calculated using NLDAS-2 gridded meteorological data with conventional trapezoid and two-stage trapezoid models. Results indicated that (1) SM agrees better with daytime LST than the nighttime or the day-night differential LST; (2) the daytime LSTs on Aqua and Terra present very similar spatial agreement with SM and they have very similar performances as downscaling factors in simulating SM; (3) decoupling effect among SM, LST, and LST-derived SMIs occurs not only in very wet but also in very dry condition; and (4) the decoupling effect degrades the performance of LST as a downscaling factor. The future downscaling algorithms should consider net surface radiation and soil type to tackle the decoupling effect.

A large-scale afforestation project has been carried out since 1999 in the Loess Plateau of China. However, vegetation-induced changes in land surface temperature (LST) through the changing land surface energy balance have not been well documented. Using satellite measurements, this study quantified the contribution of vegetation restoration to the changes in summer LST and analyzed the effects of different vegetation restoration patterns on LST during both daytime and nighttime. The results show that the average daytime LST decreased by 4.3°C in the vegetation restoration area while the average nighttime LST increased by 1.4°C. The contributions of the vegetation restoration project to the changes in daytime LST and nighttime LST are 58% and 60%, respectively, which are far greater than the impact of climate change. The vegetation restoration pattern of cropland (CR) converting into artificial forest (AF) has a cooling effect during daytime and a warming effect at nighttime, while the conversion of CR to grassland has an opposite effect compared with the conversion of CR to AF. Our results indicate that increasing evapotranspiration caused by the vegetation restoration on the Loess Plateau is the controlling factor of daytime LST change, while the nighttime LST change is affected by soil humidity and air humidity.

Climate change is undoubtedly affecting the global weather of the Earth. Rapid human civilization has mainly caused this in the last few decades. This research examined the spatial and temporal land surface temperature (LST) in the United Arab Emirates (UAE) coastal cities located in an arid region that faced massive urbanization over the last 50 years. We estimated the LST using by-products of Landsat and MODIS images covering 2000 until 2020. The assessment of LST was performed in two contexts, i.e., spatially and temporally, covering daytime vs. nighttime during the summer and winter seasons. Additionally, a supervised classification technique was adopted to extract the land use and land cover in the study area from the late 1970s until 2018. Unexpectedly, the results indicated that daytime LST in districts near the coastlines (heavily urbanized areas) are lower than the ones far away from the coast (about 9 °C). This observation represents the spatial LST inversion in the study area. Nevertheless, this difference was not observed during nighttime LST temporally, the daytime LST did not increase significantly during either summer or winter seasons. However, the nighttime LST has increased temporally by about 17% since 2000 (the temporal LST inversion). Both LST inversions could be attributed to the uniqueness of the study area, given that bare land desert was replaced by vegetation, high-rise buildings, and industrial activities. Additionally, the wind breeze blowing from the gulf might potentially contribute to cooling the coastal urban area during the daytime. Furthermore, in-depth zonal statistics were conducted to visualize the effect of land use on LST. The study observed that fully built-up areas with vegetation have lower LST than built-up areas without vegetation or a combination of sand and vegetation. The research outcomes are invaluable for decision-makers and researchers in achieving sustainable urban development.

Globally peatlands are laterally extensive and represent important stores and sinks of atmospheric carbon. The cold humid island hypothesis proposes that damaged peatlands can be distinguished from functioning peatlands by their relatively dark, bare, dry soils with resulting relatively high daytime and low night-time land surface temperatures. Contrasts in bare soil, vegetation cover and land surface temperature can be readily observed by satellite and so we propose that Earth observation, and the cold humid island hypothesis, can be used to survey, manage and monitor peatlands. Using the NASA MODIS Earth Observation (EO) products allowed the study to directly assess both status and trajectory of peatlands over multi-decadal time at a national scale. The predictions of the cold humid island hypothesis means EO products (albedo, enhanced vegetation index - EVI, daytime land surface temperature, night-time land surface temperature, and the diurnal amplitude in the land surface temperature) can be used to directly assess peatlands without further calibration or correlation to other ecosystem variables. Knowledge of specific sites within a target region means it is possible to use controls to assess absolute and relative status. By considering the state and expected trajectory of the five EO products over British peatlands it was possible to combine the five EO products into a peat health index. When compared to control locations the majority (69%) of British peatlands showed that they were on a downward trajectory. This downward trajectory was primarily driven by changes in Land Surface Temperature (LST) and, crucially, by deviations from expected trends, as indicated by control variables.

&amp;lt;p&amp;gt;The present work deals with the time series analysis of remotely sensed Moderate Resolution Imaging Spectroradiometer (MODIS) Land Surface Temperature (LST). While many works have been published concerning the trends of nighttime and daytime LST at the regional or local scale, little attention has been paid to structural changes observed within the LST time series in various sub-periods. This could be of much interest not only for climate studies but also for unveiling the possible relation between natural disasters such as wildfires and global changes. In this work we tested the hypothesis of a constant trend in LST time series from 2000 to 2019 and highlighted the existence of periods with changing trends. The methodology was applied in an area of approximately 17.000 km&amp;lt;sup&amp;gt;2&amp;lt;/sup&amp;gt; located in NE Greece and South Bulgaria. The nighttime and daytime LST time series data were initially subjected to a gap filling algorithm to account for missing values and were then aggregated at the catchment level. Furthermore, LST time series were analyzed using the Breaks For Additive Season and Trend (BFAST) method. Results indicated that an abrupt change in both nighttime and daytime LST trends was observed in all examined time series, indicating a transition from a decreasing LST regime from 2002 to 2006 to an abrupt increasing thereafter until today. An initial comparison with the existing inventory of wildfires in the area for the last 20 years indicated an increase of wildfire events which coincides with the LST breakpoint, indicating thus possible connections between rising LST and wildfire events.&amp;lt;/p&amp;gt;

Land surface temperature (LST) is a key indicator of the land surface state and can provide information on surface-atmosphere heat and mass fluxes, vegetation water stress, and soil moisture. Split-window algorithms have been used with National Oceanic and Atmospheric Administration (NOAA) Advanced Very High Resolution Radiometer (AVHRR) data to estimate instantaneous LST for nearly 20 years. However, the low accuracy of LST retrievals associated with intractable variability has often hindered its wide use. In this study, we developed a six-year daily (day and night) NOAA-14 AVHRR LST dataset over continental Africa. By combining vegetation structural data available in the literature and a geometric optics model, we estimated the fractions of sunlit and shaded endmembers observed by AVHRR for each pixel of each overpass. Although our simplistic approach requires many assumptions (e.g., only four endmember types per scene), we demonstrate through correlation that some of the AVHRR LST variability can be attributed to angular effects imposed by AVHRR orbit and sensor characteristics, in combination with vegetation structure. These angular effects lead to systematic LST biases, including "hot spot" effects when no shadows are observed. For example, a woodland case showed that LST measurements within the "hot-spot" geometry were about 9 K higher than those at other geometries. We describe the general patterns of these biases as a function of tree cover fraction, season, and satellite drift (time past launch). In general, effects are most pronounced over relatively sparse canopies (tree cover <60%), at wet season sun-view angle geometries (principal plane viewing) and early in the satellite lifetime. These results suggest that noise in LST time series may be strongly reduced for some locations and years, and that long-term LST climate data records should be normalized to a single sun-view geometry, if possible. However, much work remains before these can be accomplished.