Land Surface Temperature Products Research Articles

Medium-Resolution Mapping of Evapotranspiration at the Catchment Scale Based on Thermal Infrared MODIS Data and ERA-Interim Reanalysis over North Africa

Accurate quantification of evapotranspiration (ET) at the watershed scale remains an important research challenge for managing water resources in arid and semiarid areas. In this study, daily latent heat flux (LE) maps at the kilometer scale were derived from the two-source energy budget (TSEB) model fed by the MODIS leaf area index (LAI), land surface temperature (LST) products, and meteorological data from ERA-Interim reanalysis from 2001 to 2015 on the Tensift catchment (center of Morocco). As a preliminary step, both ERA-Interim and predicted LE at the time of the satellite overpass are evaluated in comparison to a large database of in situ meteorological measurements and eddy covariance (EC) observations, respectively. ERA-Interim compared reasonably well to in situ measurements, but a positive bias on air temperature was highlighted because meteorological stations used for the evaluation were mainly installed on irrigated fields while the grid point of ERA-Interim is representative of larger areas including bare (and hot) soil. Likewise, the predicted LE was in good agreement with the EC measurements gathered on the main crops of the region during 15 agricultural seasons with a correlation coefficient r = 0.70 and a reasonable bias of 30 W/m2. After extrapolating the instantaneous LE estimates to ET daily values, monthly ET was then assessed in comparison to monthly irrigation water amounts provided by the local agricultural office added to CRU precipitation dataset with a reasonable agreement; the relative error was more than 89% but the correlation coefficient r reached 0.80. Seasonal and interannual evapotranspiration was analyzed in relation to local climate and land use. Lastly, the potential use for improving the early prediction of grain yield, as well as detecting newly irrigated areas for arboriculture, is also discussed. The proposed method provides a relatively simple way for obtaining spatially distributed daily estimates of ET at the watershed scale, especially for not ungauged catchments.

Continental-scale evaluation of three ECOSTRESS land surface temperature products over Europe and Africa: Temperature-based validation and cross-satellite comparison

High spatial resolution land surface temperature (LST, <100 m) is crucial for agricultural water management, crop water stress monitoring, fire mapping, urban heat island study and volcano eruption detection. LST retrievals from the ECOsystem Spaceborne Thermal Radiometer Experiment on Space Station (ECOSTRESS) launched in June 2018, together with the Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER, launched in 1999) and the Landsat series (since 1972), comprise the state-of-the-art high spatial resolution LST datasets publicly accessible. Recently, we generated the ECOSTRESS LST product over Europe and Africa using both the temperature and emissivity separation (TES) and split-window (SW) algorithms under the European ECOSTRESS Hub (EEH). Here, we validated the official Jet Propulsion Laboratory (JPL) TES (Collection 1), EEH TES and EEH SW ECOSTRESS LST products over Europe and Africa between August 1, 2018 and December 31, 2021 by comparing against the in-situ measurements at 9 sites over a wide variety of land cover types. Meanwhile, the validation results were compared with those obtained for ASTER and Landsat LST at the same sites for a thorough understanding of the consistency among these high spatial resolution LST products. The results reveal that the three ECOSTRESS LST products have consistent performances, with an overall RMSE around 2 K. A cold bias around 1 K exists for all three ECOSTRESS LST, which is presumably originated from the radiometric calibration of the sensor in Collection 1 data. The Landsat LST shows a similar accuracy, with an RMSE of 2.20 K and bias of 0.54 K. The EEHSW LST show the highest consistency with Landsat LST, possibly due to the identical emissivity correction process. The performance of ASTER LST is also similar, with an RMSE of 1.98 K and bias of 0.9 K. The precisions of all the LST products are around 1.5 K. Future recalibration of the ECOSTRESS Level 1 radiance data in Collection 2 is expected to further improve the accuracy of ECOSTRESS LST. Overall, this study supports the adaptation of LST retrieval algorithms for the future thermal missions.

Evaluation of the Bias in the Use of Clear-Sky Compared with All-Sky Observations of Monthly and Annual Daytime Land Surface Temperature

Abstract Satellite-derived observations of land surface temperature (LST) are being utilized in a growing number of land surface studies; however, these observations are generally obtained from optical sensors that exclude cloudy observations of the land surface. The impact of using only clear-sky observations of land surfaces on monthly and annual estimates of daytime LST over two U.S. Climate Reference Network (USCRN) sites was evaluated over five years with daily in situ LST observations available for all-sky (clear and cloudy) conditions. The in situ LST observations were obtained for the nominal daytime observations associated with the MODIS sensors on board the Terra and Aqua satellites and were identified as all-sky or clear-sky conditions by utilizing cloud information provided with the MODIS LST product. Both monthly/annual mean and monthly/annual maximum values of daytime LST were significantly different when only clear-sky values were utilized, in comparison with all-sky values. Monthly averaged differences between the mean clear- and all-sky daytime LST (dLST) values ranged from −0.1° ± 1.5°C for January to 5.6° ± 1.8°C for May. Annually averaged dLST values, over the five years of the study, were 2.58°C, and differences between the maximum values of clear- and all-sky daytime LST values were −1.03°C. Although significant differences between mean annual clear-sky and all-sky daytime LST values were more frequent than differences observed for the annual maximum daytime LST values, the results suggest that the exclusive use of either mean or maximum clear-sky daytime LST values is not advisable for applications in which the use of daytime all-sky LST values would be more applicable.

Heterogeneous warming impacts of desert wind farms on land surface temperature and their potential drivers in Northern China

To address rapid climate change, wind energy has been widely developed in China in the last two decades. However, wind farm (WF) turbulence effects can change the local climate by redistributing temperature, humidity, and heat fluxes. Previous studies indicate that WFs can significantly increase nighttime land surface temperature (LST); however, their conclusions are mainly derived from individual WFs and ignore heterogeneous impacts among multi-WFs in China. Another large source of uncertainty is that the WFs used in previous studies are mainly located in croplands or grasslands, which may obscure direct WF impacts because of the interactions between vegetation and the atmosphere. In this study, we detect impacts with MODIS LST products during 2001–2018 at sixteen WFs in the desert of northern China. The results suggest that the averaged warming impacts of WFs on LST are similar between nighttime (0.237 °C) and daytime (0.250 °C). However, the uncertainty is much greater for daytime (SD = 0.519 °C) than for nighttime (SD = 0.146 °C) due to spatially heterogeneous impacts of desert WFs on LST. Optimal structural equation models suggest that wind speed, precipitation, and distribution patterns of wind turbines mainly explain the spatial heterogeneity of the desert WF impacts on nighttime LST. Given the rapid development of WFs globally, the local warming impacts of WFs and their corresponding mechanisms should be highlighted as a high priority in the fields of energy and climate.

Modeling MODIS LST at European scale

Background To harmonize exposure assessment of temperature in multi-cohort studies, models require to cover large temporal and geographical extends at high resolution. The daily 1km Land Surface Temperature (LST) product from MODIS instrument is an important satellite predictor in ambient temperature models. However, LST products present an uneven data distribution due to cloud and snow cover. Interpolating LST &amp;#x22;gaps” can become computationally expensive and require performance indicators to evaluate spatial and temporal stability and overfitting. Traditional (global) performance indicators might not be sensitive to changes in performance resulting from local data density and autocorrelation structures. This study aims (1) to evaluate different model configurations to understand how LST spatial and temporal data density patterns affect model performance at global and local levels, and (2) to identify the most stable models. Methods: We aggregated observations into 100km² spatial blocks, trained random forest models and evaluated performance through 5-fold block cross-validation. We tested different model configurations based on the number of predictor variables (latitude, longitude, elevation, date, NDVI &amp;#x26; climatic variables), the model’s temporal extent and the number of sampled instances per block for Europe. Results: Adding predictor variables and increasing the temporal extent dramatically increased time, but not performance. Sampling 25% of the maximum amount of observations per block show the largest drops in RMSE, while keeping the computational times low. We showed some evidence of overfitting when training large extents and sample sizes due to limited representativeness at blocks with less instances. Our best model showed a CV-RMSE of 2.4 Kelvin with 90% of mean RMSE at the block level below 0.93 Kelvin. Conclusions: Using a block sampling approach can balance sample size, thus limiting overfitting and computational time. Keywords: Europe wide, temperature modelling, block cross-validation, Land surface temperature

Reconstruction of land surface temperature under cloudy conditions from Landsat 8 data using annual temperature cycle model

Land surface temperature (LST) is an important parameter in the processes of energy exchange and water cycle between the land surface and the atmosphere. The impact of cloud cover leads to spatially incomplete of thermal infrared (TIR)-based LST products, which seriously hinders the applications of LST products in various fields. Several methods have been developed to reconstruct LST under cloudy conditions in previous studies, but there is a lack of an effective method for the reconstruction of cloudy LST at the spatial resolution of Landsat pixel (30 m). In this study, a novel method was proposed to reconstruct LST under cloudy conditions from Landsat 8 data. The LST reconstruction method includes four main steps: (1) identification of cloud-free, cloud-shadow, cloud-obscured, and cloud-covered pixels by integrating the Fmask method with a cloud-shape matching method; (2) calculation of annual temperature cycle (ATC)-based reference LST by fitting an ATC model to all available Landsat 8 LST product during 2013-2020; (3) estimation of LST residual from spatially adjacent similar pixels; and (4) estimation of reconstructed LST in terms of the sum of ATC-based reference LST and LST residual. The performance of the LST reconstruction method was evaluated using Landsat 8 LST images under clear-sky conditions as reference data. The root mean squared error (RMSE) between reconstructed LST and Landsat 8 reference LST ranges from 0.9 K to 2.5 K. The LST reconstruction method was further applied to reconstruct actual Landsat 8 LST images under cloudy conditions. Compared with original Landsat 8 LST images, the spatial distribution of reconstructed LST images is more complete. The pattern of reconstructed LST images reflects the spatial variability of LST well. The accuracy of the LST reconstruction method was validated against in situ LST measurements at six SURFRAD (Surface Radiation Budget Network) sites. The overall bias and RMSE between reconstructed LST and in situ LST at all sites are approximately −0.3 K and 3.5 K, respectively. The LST reconstruction method has great potentials to improve the applications of Landsat LST product in urban thermal environment monitoring and crop water stress monitoring.

Assessment of the TsHARP method for spatial downscaling of land surface temperature over urban regions

Nowadays, acquiring high spatial and temporal land surface temperature (LST) is one of the significant factors in urban climate studies. This study aims to assess the sharpening thermal imagery (TsHARP) method, which is developed for determining the LST in urban regions. Whereas the original TsHARP method correlates the Normalized Difference Vegetation Index (NDVI) with the LST, the current study seeks to evaluate the performance of the TsHARP method by correlating between the LST and NDVI over urban areas. Additionally, it is intended to investigate the relationship between the impervious surfaces (IS) and LST. At this point, the Advanced Spaceborne Thermal Emission and Reflection Radiometer ASTER image of the year 2003 was used to retrieve the IS as well as the LST of the Kuala Lumpur city, Malaysia. To retrieve the main LST, IS and NDVI, ASTER Level1B data were used. On the other hand, ASTER land surface temperature products (AST08) was utilized to evaluate the downscaled LST (DLST) image. With selection of the ~1/4 of the pixels with lowest coefficient of variation (CV), the LST has more correlation with the IS in comparison with the NDVI, while the correlation between the IS and LST was increased in this study. The IS index illustrates the most optimal correlation for the urban LST (R2 = 0.59). Furthermore, the TsHARPIS-based method depicted a good correlation between the DLST and the ASTER LST product (RMSE = 1.5 °C, R2 = 0.83) compared to the NDVI (RMSE = 4 °C, R2 = 0.73). Consequently, it is found that the application of TsHARPIS-based method to ASTER remote sensing data is a robust/low-cost approach for spatial downscaling of the LST over urban regions.

A New Method to Map Spring Irrigated Areas Using MODIS LST Products and Ancillary Data in an Agricultural District of Northwest China

Irrigation alleviates drought in croplands and maintains or increases crop yields. The accurate monitoring of irrigated areas is important to regional water resource management, food security, climate change, drought monitoring, and emergency disaster relief. Based on field experiments that demonstrate the feasibility of irrigated area mapping using land-surface temperature, we propose a method to map spring irrigation areas using historical meteorological data, main crop phenological characteristics, irrigation regimes, and MODIS land-surface temperature (LST) products. The distribution of irrigation intensity, spring irrigated areas (SIA, considering the irrigation intensity), and total area of spring irrigation (STIA, regardless of irrigation intensity) were monitored by the proposed method for the Donglei Irrigated District (Phase II) in northwestern China from 2011 to 2018. The spring irrigation of the study area was divided into three periods (16 January–23 February, 24 February–24 March, and 25 March–31 May). Then, the temperature threshold of the irrigated area in each period was determined by the diurnal temperature range (DTR) of the rain-fed plots and precipitation data; for the three periods, this was 12 °C, 15 °C, and 11 °C, respectively. The results showed that most of the croplands in the study area were irrigated once or twice. The SIA in most years varied between 55,900 and 73,100 ha, but in 2016, the irrigation area reached 100,200 ha. The STIA accounted for 60–70% of the irrigated area except 2016. The average accuracy of SIA monitoring was satisfactory and above 94% for years when sufficient and reliable data was available.

Warming lake surface water temperatures in Lake Qiandaohu, China: Spatiotemporal variations, influencing factors and implications for the thermal structure

Long-term lake surface water temperature (LSWT) products are valuable for understanding the responses of lake ecosystems to climate warming and for proposing suitable policies to protect lake ecosystems. Here, using Landsat satellite data and various in situ data, we documented 36 years (1986–2021) of spatiotemporal variations in LSWT in Lake Qiandaohu, a subtropical deep-water lake in China, and explored the potential driving factors of these variations. We validated the performances of the practical single-channel (PSC) algorithm, the generalized single-channel algorithm and the Landsat Level 2 land surface temperature product on Lake Qiandaohu with long-term in situ buoy data. Overall, the PSC algorithm had the best performance, with a mean absolute percent error (MAPE) of 7.5% and root mean square difference (RMSE) of 1.7°C. With 36 years of Landsat data and the PSC algorithm, the spatiotemporal variations in LSWT were constructed. The Landsat-derived 36-year mean LSWT in Lake Qiandaohu ranged from 18.2 to 23.1°C, with a mean value of 20.2°C. The northeast and southwest subsegments had the minimum (19.7°C) and maximum (20.6°C) mean LSWT values, respectively. The spatial variations in LSWT could be explained in part by the water depth. From 1986 to 2021, a significant warming trend was observed in Lake Qiandaohu, with a warming rate of 0.07°C/year. The warming rate of Lake Qiandaohu was faster than that of the local air temperature (warming rate = 0.04°C/year). The LSWT warming in Lake Qiandaohu can mainly be attributed to the warming air temperatures. Lake warming has increased the thermal stability in Lake Qiandaohu and has had negative impact on the lake ecosystem. Our work highlights the importance of using satellite data to understand the responses of lake ecosystems to climate change.

Predicting spatial and temporal changes in surface urban heat islands using multi-temporal satellite imagery: A case study of Tehran metropolis

Nowadays, predicting spatial and temporal changes of surface urban heat islands (SUHIs) is both a demanding challenge and a subject of interest for researchers working on urban thermal environments. This study aimed to predict the spatial-temporal changes of SUHI in Tehran city. The research data consisted of Landsat multi-temporal images, MODIS-derived water vapor and land surface temperature (LST) products, as well as climatic data. The cellular automata (CA)-Markov model was used for predicting the spatial and temporal land cover changes. Afterward, based on the statistical and spatial information of the impact of land cover changes on LST changes, maps of the LST and SUHII were procured for future years. The results showed that the area of built-up lands increased by 88% from 1985 to 2019. The built-up lands area is expected to increase to 446.3, 463.9, and 480.1 km2 by 2026, 2032, and 2038, respectively. The mean LST of Tehran city in 1985, 1992, 2000, 2009, and 2019 was 36.45, 41.95, 43.15, 44.95, and 47.25 °C, respectively. By 2026, 2032, and 2038, the mean LST may reach 48.65, 49.95, and 51.15 °C, respectively. Analysis of the SUHI ratio index in Tehran showed an increase from 0.01 in 1985 to 0.34 in 2019, and is expected to reach 0.38, 0.45, and 0.51 by 2026, 2032, and 2038, respectively.

Mapping of Land Degradation Vulnerability in the Semi-Arid Watershed of Rajasthan, India

Global soils are under extreme pressure from various threats due to population expansion, economic development, and climate change. Mapping of land degradation vulnerability (LDV) using geospatial techniques play a significant role and has great importance, especially in semi-arid climates for the management of natural resources in a sustainable manner. The present study was conducted to assess the spatial distribution of land degradation hotspots based on some important parameters such as land use/land cover (LULC), Normalized Difference Vegetation Index (NDVI), terrain characteristics (Topographic Wetness Index and Multi-Resolution Index of Valley Bottom Flatness), climatic parameters (land surface temperature and mean annual rainfall), and pedological attributes (soil texture and soil organic carbon) by using Analytical Hierarchical Process (AHP) and GIS techniques in the semi-arid region of the Bundi district, Rajasthan, India. Land surface temperature (LST) and NDVI products were derived from time-series Moderate-Resolution Imaging Spectroradiometer (MODIS) datasets, rainfall data products from Climate Hazards Group InfraRed Precipitation with Station data (CHIRPS), terrain characteristics from Shuttle Radar Topography Mission (SRTM), LULC from Landsat 9, and pedological variables from legacy soil datasets. Weights derived for thematic layers from the AHP in the studied area were as follows: LULC (0.38) &gt; NDVI (0.23) &gt; ST (0.15) &gt; LST (0.08) &gt; TWI (0.06) &gt; MAR (0.05) &gt; SOC (0.03) &gt; MRVBF (0.02). The consistency ratio (CR) for all studied parameters was &lt;0.10, indicating the high accuracy of the AHP. The results show that about 20.52% and 23.54% of study area was under moderate and high to very high vulnerability of land degradation, respectively. Validation of LDV zones with the help of ultra-high-resolution Google Earth imageries indicates good agreement with the model outputs. The research aids in a better understanding of the influence of land degradation on long-term land management and development at the watershed level.

Offset of MODIS land surface temperatures from in situ air temperatures in the upper Kaskawulsh Glacier region (St. Elias Mountains) indicates near-surface temperature inversions

Abstract. Remote sensing data are a crucial tool for monitoring climatological changes and glacier response in areas inaccessible for in situ measurements. The Moderate Resolution Imaging Spectroradiometer (MODIS) land surface temperature (LST) product provides temperature data for remote glaciated areas where air temperature measurements from weather stations are sparse or absent, such as the St. Elias Mountains (Yukon, Canada). However, MODIS LSTs in the St. Elias Mountains have been found in prior studies to show an offset from available weather station measurements, the source of which is unknown. Here, we show that the MODIS offset likely results from the occurrence of near-surface temperature inversions rather than from the MODIS sensor’s large footprint size or from poorly constrained snow emissivity values used in LST calculations. We find that an offset in remote sensing temperatures is present not only in MODIS LST products but also in Advanced Spaceborne Thermal Emissions Radiometer (ASTER) and Landsat temperature products, both of which have a much smaller footprint (90–120 m) than MODIS (1 km). In all three datasets, the offset was most pronounced in the winter (mean offset &gt;8 ∘C) and least pronounced in the spring and summer (mean offset &lt;2 ∘C). We also find this enhanced seasonal offset in MODIS brightness temperatures, before the incorporation of snow surface emissivity into the LST calculation. Finally, we find the MODIS LST offset to be consistent in magnitude and seasonal distribution with modeled temperature inversions and to be most pronounced under conditions that facilitate near-surface inversions, namely low incoming solar radiation and wind speeds, at study sites Icefield Divide (60.68∘N, 139.78∘ W; 2,603 m a.s.l) and Eclipse Icefield (60.84∘ N, 139.84∘ W; 3017 m a.s.l.). Although these results do not preclude errors in the MODIS sensor or LST algorithm, they demonstrate that efforts to convert MODIS LSTs to an air temperature measurement should focus on understanding near-surface physical processes. In the absence of a conversion from surface to air temperature based on physical principles, we apply a statistical conversion, enabling the use of mean annual MODIS LSTs to qualitatively and quantitatively examine temperatures in the St. Elias Mountains and their relationship to melt and mass balance.

Detection of geothermal anomalies in the Northern Lake Abaya geothermal field, Main Ethiopian Rift

The detection of geothermal anomalies using Thermal Infrared (TIR) remote sensing data is challenging because of how sensor specifications (such as the infrared wavelength used for the measurement, spectral dependence of the emissivity, angle at which the measurement is made, state of the surface and height of the sensor above the surface) and physical parameters (such as solar radiation, topography, albedo, soil compaction and coherence of rocks) affect Land Surface Temperature (LST) retrieval and analysis. This work tests whether TIR remote sensing measurements with thorough spatial and temporal sampling can improve LST retrievals. Multi-temporal TIR data from 2000 through 2019 from Landsat 7 and 8 TIR instruments and the Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) were used to detect geothermal areas in the geologically active region of the southern Main Ethiopian Rift. In addition, field-based temperature data from 19 sites were evaluated for comparison to the remotely detected geothermal anomaly areas. We have used the single-channel algorithm and Normalized Difference Vegetation Index (NDVI) method of emissivity retrieval to derive LST for each year. The result shows that the mean LST is highest in 2003 (320.1 K) and lowest in 2019 (303.1 K). The change in mean LST was between −9 K to 13 K. These LST results from ASTER images were validated with MODIS LST products and showed a correlation coefficient >0.6. LST of the year 2003 has been much closer to the actual temperature value from field data. Fifteen sites (79%) fit with the identified geothermal anomaly areas. LST values in known geothermal activity sites show no correlation (< 0.5) with time attesting. That is, even though LST varies with time (e.g., day and night and seasonal changes), the LST of areas with geothermal potential remain more or less constant on yearly basis.

A novel TIR-derived three-source energy balance model for estimating daily latent heat flux in mainland China using an all-weather land surface temperature product

A reliable thermal-derived method for spatially and temporally continuous latent heat flux (LE) estimates is vital for agricultural water resource management. In this study, we proposed a novel three-source LE model (TSLEM) derived by all-weather thermal infrared (TIR) land surface temperature (LST) to generate all-sky daily LE in mainland China. In this approach, LE was partitioned into the energy fluxes from canopy transpiration, soil evaporation, and interception water evaporation, respectively. Importantly, a new strategy was used to decompose radiational temperature into soil temperature (Ts), canopy temperature (Tc) and interception water temperature (Ti). Then soil evaporation was estimated by Penman-Monteith (PM) equation that parameterizes a soil resistance as a function of the normalized difference temperature index (NDTI) derived from Ts. A simplified MOD16 algorithm framework was used to estimate canopy transpiration and a Priestley-Taylor (PT) model to estimate interception evaporation. The proposed method was validated using 26 eddy covariance (EC) tower sites in mainland China across various vegetation types and applied to generate spatial continuous daily LE in mainland China. The results show that TSLEM accurately yielded daily LE with an average coefficient of determination (R2) of 0.53 (p<0.01) and root-mean-square-error (RMSE) of 27.37 W/m2, indicating TSLEM is a promising method for generating daily LE using all-weather LST at the regional scale.

Assessment of the Paris urban heat island in ERA5 and offline SURFEX-TEB (v8.1) simulations using the METEOSAT land surface temperature product

Abstract. Cities concentrate people, wealth, emissions, and infrastructure, thus representing a challenge and an opportunity for climate change mitigation and adaptation. This urgently demands for accurate urban climate projections to help organizations and individuals to make climate-smart decisions. However, most of the large ensembles of global and regional climate model simulations do not include sophisticated urban parameterizations (e.g., EURO-CORDEX; CMIP5/6). Here, we explore this shortcoming in ERA5 (the latest generation reanalysis from the European Centre for Medium-Range Weather Forecasts) and in a simulation with the SURFEX (Surface Externalisée) land surface model employing the widely used bulk bare rock approach. The city of Paris is considered as a case study. Subsequently, we apply a more complex urban scheme – SURFEX coupled to the Town Energy Balance (TEB) urban canopy model to assess its benefits on characterizing the Paris urban climate. Both simulations and ERA5 were compared to the LSA SAF (Satellite Application Facility on Land Surface Analysis) land surface temperature product to evaluate the simulation of Parisian surface urban heat island (SUHI). Our results show a significant added value of SURFEX-TEB in reproducing the SUHI during the daytime and the UHI during both the daytime and nighttime (with overall reductions in the bias and root mean square error and improvements in the representation of the statistics of the SUHI/UHI displayed by the Perkins skill score or S score). The improvement in the simulated SUHI is lower during the nighttime due to the lack of land–atmosphere feedbacks in the proposed offline framework. Nonetheless, the offline SURFEX-TEB framework applied here clearly demonstrates the added value of using more comprehensive parameterization schemes to simulate the urban climate and, therefore, allowing the improvement of urban climate projections.

Assessing and modeling diurnal temperature buffering and evapotranspiration dynamics in forest restoration using ECOSTRESS thermal imaging

Restoration of temperate forests alters the diurnal dynamics of land surface temperature (LST) and evapotranspiration (ET) through increases in biomass, diversity, and complexity. The ECOsystem Spaceborne Thermal Radiometer Experiment on Space Station (ECOSTRESS) is the first space-based thermal imaging instrument that allows for a study of diurnal LST and ET dynamics at a moderate spatial resolution (70 × 70 m).We quantified, compared, and modeled the LST and ET dynamics of two groups of forest restoration sites (43 sites in total, between 2 and 75 ha) in Southern Ontario, Canada that were initially restored from mainly agriculture between 2007 and 2019. We included all useable LST (n = 29) and ET (n = 9) ECOSTRESS image products from a full growing season in 2020 (June 1st to September 30th). Forest restoration sites were compared by age and against agriculture (pre-restoration state), mature forest (post-restoration state), and suburban residential sites (competing land use).The ability of forest restoration sites to buffer temperatures was highest in the afternoon, around 14:00 local time (EDT). As predicted, restoration sites were significantly cooler (x̄ 4.4–7.4 °C) than residential and agricultural areas and significantly warmer (x̄1.6–2.9 °C) compared to mature reference forest sites at both groups of sites. Relative diurnal (24-h) LST variability of forest and restoration sites was also significantly lower (0.9–2.9 °C) compared to agriculture and residential sites (3.3–5.2 °C). Daytime LST decreased significantly by 0.1 °C (3.1%), per year since restoration for one of the groups of sites relative to nearby mature reference forest sites as per a linear mixed effects regression model. It would take these sites ~30 years to reach the same buffering as mature forests. In characterizing ET dynamics of a subset of sites, we found that more recently restored sites had a statistically significant higher overall ET than older ones and that daytime relative instantaneous ET decreased with years since restoration. The variation explained by the ET model was however low.Our study provides insights into how diurnal forest ecosystem energy conversion and storage dynamics changes over time after restoration. These diurnal thermal dynamics impact wildlife habitat as well as human wellbeing. The change in thermal buffering over time could be used to assess the pace and trajectory of restoration by managers. The thermal buffering provided by restored forest can also be quantified as an ecosystem service. As such, the study demonstrates the utility and also limitations of novel thermal remote sensing methods, using free and publicly available imagery from ECOSTRESS.

Seasonal and diurnal surface urban heat islands in China: an investigation of driving factors with three-dimensional urban morphological parameters

ABSTRACT Surface urban heat island (SUHI) can considerably influence the urban environment and the quality of life. It is vital to examine how underlying surface properties impact seasonal and diurnal SUHIs. However, the influence of three-dimensional (3D) urban morphological parameters (UMPs) on SUHIs has not been thoroughly studied under varying climatic settings. To fill this knowledge gap, the present study investigated seasonal and diurnal changes in SUHI intensities (ΔT) in 208 cities in China from 2014 to 2016 using moderate-resolution imaging spectroradiometer (MODIS) land surface temperature products. In addition, the influence of potential factors in urban surface energy balance, including two-dimensional (2D) and 3D UMPs, socio-economic indices, urban greening, and surface albedo, on seasonal and diurnal ΔT were assessed under different climatic settings and with different city sizes using the method of Geographically Weighted Regression (GWR). Results show that negative summer daytime ΔT was observed in some cities under dry climates. Generally, in summer, the ΔT during daytime was higher than at nighttime. The 3D UMPs (i.e. building height and volume) yielded more decisive influences on ΔT than 2D UMP (i.e. building coverage). This is particularly true for the summer diurnal cycle and under dry climatic settings. Building height was found to be negatively correlated with surface temperatures, while building volume was positively correlated. Additionally, the 3D UMPs yielded more influences on winter ΔT than summer ΔT. The capability of vegetation to regulate ΔT was more potent in dry climates than in wet climates and in small cities than in large cities. Varying climates and city sizes can modify the significance of the 2D and 3D UMPs on the urban surface energy balance, suggesting that urban thermal mitigation should consider climate background and population size.

Uncertainty quantification in land surface temperature retrieved from Himawari-8/AHI data by operational algorithms

Himawari-8, a new-generation geostationary satellite, can retrieve sub-hourly land surface temperatures (LSTs) with moderate spatial resolution, providing a new scale for monitoring the thermal environment in Asia and Oceania. This study evaluated uncertainties of LSTs retrieved by three operational algorithms from Advanced Himawari Imager (AHI) data. We compared two nonlinear split-window algorithms (SOB and WAN algorithms) and one nonlinear three-band algorithm (YAM algorithm). First, the error characteristics of the retrieved LSTs caused by the input parameter errors were simulated under various land-atmospheric conditions using an atmospheric radiative transfer model. Thereafter, retrieved LSTs from actual AHI data were evaluated using in-situ observations from AsiaFlux and OzFlux networks and the ECOsystem Spaceborne Thermal Radiometer Experiment on Space Station (ECOSTRESS) LSTs. The simulated results showed that the YAM algorithm maintained the highest accuracy, whereas the WAN algorithm had the highest robustness to input errors. The YAM algorithm had the smallest total error including input errors over a wide range of retrieval conditions. Validation of the three algorithms via in-situ LSTs from 12 sites revealed nighttime mean RMSEs for all sites of ∼1.7 °C, and daytime mean RMSEs for semi-arid and humid sites of approximately 3.0 °C and 2.0 °C, respectively. These are comparable to the accuracies reported for LST products with higher spatial resolutions, such as the Moderate Resolution Imaging Spectroradiometer and Landsat. Within the Himawari-8 disk, the estimation error of the YAM algorithm was ∼1.0 °C lower than those of the SOB and WAN algorithms in regions with extremely high viewing angle, temperature, and humidity (e.g., northern China, Australia, and Southeast Asia). Furthermore, AHI LSTs showed closer agreement with ECOSTRESS compared to in-situ LSTs, suggesting the usefulness of ECOSTRESS for assessing the diurnal LSTs derived from geostationary satellites. The resulting LST products and the knowledge of their error characteristics have the potential to improve the collective understanding of terrestrial energy and water cycles based on improved accuracy and robustness.

Spatiotemporal Variation of Land Surface Temperature in Henan Province of China from 2003 to 2021

Land surface temperature (LST) is a key parameter closely related to various land surface processes and surface-atmosphere interactions. Analysis of spatiotemporal variation of time-series LST may provide useful information to understand eco-climatic characteristics. In this study, the spatiotemporal pattern of LST and its trend characteristics in Henan Province were examined based on MODIS LST products from 2003 to 2021. In addition, the influences of land cover types, Nighttime Light data (NTL) and Normalized Difference Moisture Index (NDMI) on LST variation were analyzed. The results indicated that: (i) The LST showed slight and rapid decreasing trend for 2004–2010 and 2018–2020, respectively, whereas an obvious increasing and slight increasing trend occurred for 2010–2013 and 2014–2018. In terms of spatial pattern, high-temperature, and sub-high-temperature were mainly distributed in the central part of the province with higher level of industrialization and urbanization at the annual, spring, summer, and daytime scales. While in fall, winter, and the nighttime, the spatial distribution of LST exhibited decreased trend from the southern part to the northern part of the province, the largest Standard Deviation (STD) was observed in summer. (ii) The interannual variation rate of LST was 0.08 °C/Y. The increasing trend mainly occurred in urban and built-up areas. At the seasonal scales, the rising rate decreased sequentially in the order of fall, winter, spring, and summer. In addition, the rising rate in the daytime was higher than that in the nighttime. (iii) LST increased along with the expansion of urban and built-up lands, except in winter. At the annual scales, 84.69% of areas with NTL data exhibited a positive correlation with LST, and NDMI in the western part with high elevation presented a significantly positive correlation to LST, while a significantly negative correlation occurred in urban and built-up areas. The cooling effect of NDMI on LST in the daytime was greater than that in the nighttime. In cropland areas, LST showed a non-significant correlation with NDMI at the annual scale, and a significantly negative correlation with NDMI in spring, summer, and fall. The influence mechanism of cropland on the variation of LST at different timescales needs to be further explored. These findings might provide some hints to understand climate change and its causes in the province.

Land surface temperature downscaling in the karst mountain urban area considering the topographic characteristics

To obtain high-spatial-resolution land surface temperature (LST) in karst areas, it is necessary to select a downscale regression model with a better simulation effect and the scale factors that can best represent the topographic characteristics of karst mountainous areas. In Guiyang, a typical karst mountain city, two areas are selected as the study area, which is dominated by natural surface and construction land. Based on the data of Landsat-8 Thermal Infrared Sensor (TIRS), Sentinel-2, Advanced Land Observing Satellite Digital Elevation Model (ALOS DEM), and meteorological stations, the scale factors representing bare land: bare soil index and topographic relief: mountain shadow (hillshade), relief degree of land surface (RDLS), solar incident angle, and sky view factor (SVF) are added on the basis of the conventional factors. At the same time, random forest (RF) and extreme gradient boosting (XGBoost) models are used to construct an LST downscaling method that is more suitable for karst mountain cities. After the above steps, the LST product with a spatial resolution of 10 m is finally estimated. The results show that, due to the characteristics of large elevation variation, fragmentation, and high heterogeneity of surface landscape in karst areas, digital elevation model (DEM), RDLS, and SVF factors need to be considered in the downscaling of surface temperature, and the contribution rates of these factors are all more than 6% in the model. In terms of accuracy evaluation of ground temperature, XGBoost model has the highest accuracy with an average absolute error of 1.67K, RF model has an average error of 1.90K, and thermal image sharpening has the worst accuracy with an average error of 2.41K. In terms of accuracy evaluation of ascending scale, the XGBoost model also shows higher accuracy and richer texture details. The research results can provide basic data for the acquisition of high-resolution LST and its intermediate parameters in this area and also provide a method reference for the reduction of high-resolution LST in similar areas.