Copernicus Ground-Based Observations for Validation service (GBOV): overview and latest updates for EO data Cal/Val

Abstract. Systematic evaluation of the carbon cycle physical and biological variables simulated in Earth System Model (ESM) participating in the Coupled Model Intercomparison Project Phase 6 (CMIP 6) is fundamental to the understanding of terrestrial ecosystems, as well as to future projections. Leaf Area Index (LAI), Gross Primary Productivity (GPP), Net Primary Productivity (NPP), Net Ecosystem Productivity (NEP) and Land Surface Temperature (LST) as key indicators of carbon cycle performance in ESM outputs, play a critical role in evaluating ecosystem functions. Assessing these metrics can provide valuable insights into the biases in model-simulated ecosystems and offer guidance for model improvement. In this study, we assessed the interannual trends performance of LAI, GPP, NPP, NEP and LST simulated by 12 CMIP6 ESMs during the historical period by using satellite LAI, NPP, NEP, LST and CSIF data as observations. The findings indicate that: (1) There are significant uncertainties in the overall trends and interannual variability in LAI, NPP, and LST captured by the CMIP6 ESM. Meanwhile, simulated GPP and NEP trends were lower than observations with discrepancies reaching 0.03 yr−1 for GPP and 2.46 gCm-2yr-1 for NEP. (2) Spatially, CMIP6 ESMs exhibited widespread underestimation of trends in LAI, GPP, NPP, and NEP across China. The MME underestimated these variables in 46.29 % (LAI), 43.47 % (GPP), 49.81 % (NPP), and 61.34 % (NEP) of the study area. Meanwhile, the simulated LST trend is underestimated in northern China, while its overestimations in western and southern China. (3) ESMs inadequate responsiveness to anthropogenic and environmental forcing and incomplete mechanistic representation of plant respiration pathways struggled accurate simulation of trends in LAI, GPP, NPP, NEP and LST.

Sandy range land refers to a major component of grassland area types in the semi-arid area of northern China. Monitoring of vegetation and land surface temperature (LST) using remote sensing technology can help determine the degree of desertification in a regional and/or sub regional scale, as in the Horqin Sandy Land selected in this study. Correlation analysis was performed to examine the relationship between the fractional vegetation coverage (FVC) and the LST within one growing season (from May to August 2017), at different spatial scales. The results showed that the FVC increased from 0.12 in May to 0.29 in August, and the LST increased first and then declined. The highest LST was 41.68 °C in July, while the lowest was 28.62 °C in August. At the grid scale, the LST increased first and then declined with the increase of the FVC on 25 May, 10 June, and 29 August; the FVC ranged from 0.29–0.38, 0.27–0.32, and 0.29–0.38 with the preference of the ‘turning point’, respectively. A negative correlation was identified between the FVC and the LST and without any ‘turning point’ in the fitting curve on 28 July. The correlation between FVC and LST complied with the grid scale at the sample area scale. The coupling analysis of landscape pattern expressed by FVC and LST showed that, the landscape evenness, Euclidean nearest neighbor distance, and landscape splitting degree all showed strong coupling correlation in any study period (P). The landscape aggregation of FVC and LST showed a good coupling at the relatively high and low air temperature conditions of P1 and P3. Landscape contagion showed a good coupling between FVC and LST at relatively moderate air temperature condition of P1 and P4. Air temperature conditions and characteristics of vegetation coverage should be considered for a more targeted analysis when analyzing the relationship between FVC and LST and attention should be paid to the timing and type of study area in practical application.

ABSTRACTModerate Resolution Imaging Spectroradiometer (MODIS) Land Surface Temperature (LST) data of MODIS 11A2 and Normalized Difference Vegetation Index (NDVI) products of MODIS 13Q1 were used to analyse the spatial and temporal changes of LST and its relationship with vegetation from 2001 to 2015 in the subtropical region of Fujian Province, China. The LST and NDVI products were reconstructed by Savitzky–Golay (S-G) filtering for abnormal values or missing data due to the influence of cloud contamination. The LST of Fujian decreases from its southeast coast to its northwest mountain area, and the annual average highest LST is greater than 30°C while the lowest LST is about 15°C. In the past 15 years, the average LST in Fujian has declined gradually due to an increase in fractional vegetation coverage (FVC), but the highest LST increased from 30.95°C to 33.05°C across south-east coastal zone cities due to rapid urbanization. From 2001 to 2015, the LST of Fujian Province trended slightly downward; the LST area, however, significantly decreased by 18.85% of the total area of the province and was twice that of the (significant) increase in LST. The changing trend distributions of LST are opposite to those of FVC in general, but LST decreases to greater extent as FVC increases when FVC is above 0.2 – every 10% increase in FVC is accompanied by about a 1.1°C decrease in LST.

Abstract. Thermal-based two-source energy balance modeling is essential to estimate the land evapotranspiration (ET) in a wide range of spatial and temporal scales. However, the use of thermal-derived land surface temperature (LST) is not sufficient to simultaneously constrain both soil and vegetation flux components. Therefore, assumptions (about either soil or vegetation fluxes) are commonly required. To avoid such assumptions, an energy balance model, TSEB-SM, was recently developed by Ait Hssaine et al. (2018b) in order to consider the microwave-derived near-surface soil moisture (SM), in addition to the thermal-derived LST and vegetation cover fraction (fc) normally used. While TSEB-SM has been successfully tested using in situ measurements, this paper represents its first evaluation in real life using 1 km resolution satellite data, comprised of MODIS (MODerate resolution Imaging Spectroradiometer) for LST and fc data and 1 km resolution SM data disaggregated from SMOS (Soil Moisture and Ocean Salinity) observations. The approach is applied during a 4-year period (2014–2018) over a rainfed wheat field in the Tensift basin, central Morocco. The field used was seeded for the 2014–2015 (S1), 2016–2017 (S2) and 2017–2018 (S3) agricultural seasons, while it remained unploughed (as bare soil) during the 2015–2016 (B1) agricultural season. The classical TSEB model, which is driven only by LST and fc data, significantly overestimates latent heat fluxes (LE) and underestimates sensible heat fluxes (H) for the four seasons. The overall mean bias values are 119, 94, 128 and 181 W m−2 for LE and −104, −71, −128 and −181 W m−2 for H, for S1, S2, S3 and B1, respectively. Meanwhile, when using TSEB-SM (SM and LST combined data), these errors are significantly reduced, resulting in mean bias values estimated as 39, 4, 7 and 62 W m−2 for LE and −10, 24, 7, and −59 W m−2 for H, for S1, S2, S3 and B1, respectively. Consequently, this finding confirms again the robustness of the TSEB-SM in estimating latent/sensible heat fluxes at a large scale by using readily available satellite data. In addition, the TSEB-SM approach has the original feature to allow for calibration of its main parameters (soil resistance and Priestley–Taylor coefficient) from satellite data uniquely, without relying either on in situ measurements or on a priori parameter values.

Vegetation plays a crucial role in the land surface water and energy balance modulating the interactions and feedback with climate at the regional to global scale. The availability of unprecedented Earth observation products covering recent decades (and extended up to real-time) are therefore of paramount importance to better represent the vegetation and its time evolution in the land surface models (LSMs) used for offline analysis/initialization and for the seasonal-to-decadal predictions. Here, we integrate realistic vegetation Leaf Area Index (LAI) variability from latest generation satellite campaigns, available through Copernicus Land Monitoring Service (CLMS), in three different LSMs that conducted the same coordinated set of offline land-only simulations forced by hourly atmospheric fields derived from the ERA5 atmospheric reanalysis. The experiment implementing realistic interannually-varying LAI (SENS) is compared with simulations utilizing a climatological LAI (CTRL) to quantify the vegetation feedback and the effects on the simulation of near-surface soil moisture.The results show that the inter-annually varying LAI considerably affects the simulation of near-surface soil moisture anomalies in all three models and over the same water-limited regions, but surprisingly the effects diverge among models: compared with ESA-CCI observations, the near-surface soil moisture anomalies significantly improve in  one of the three LSMs (HTESSEL-LPJGuess) while the other two (ECLand and ISBA-CTRIP) display opposite effects with significant worsening of the anomaly correlation coefficients. It is found that the enhanced simulation of near-surface soil moisture is enabled by the positive feedback that is activated by the effective vegetation cover (EVC) parameterization, implemented only in HTESSEL-LPJGuess. The EVC parameterization works such that the effective fraction of the bare soil being covered by vegetation does vary with LAI following an exponential function constrained by available satellite observations. The increased (reduced) soil-moisture limitation during dry (wet) periods produces negative (positive) LAI and therefore EVC anomalies, which in turn generate a dominating positive feedback on the near-surface soil moisture of HTESSEL-LPJGuess by exposing more (less) bare soil to direct evaporation from the sub-surface layer. On the other hand, in the EC-Land and ISBA-CTRIP models, EVC is fixed in time as it cannot vary with LAI and so the positive feedback described cannot be activated. The only feedback on near-surface soil moisture anomalies that operates  in these two models is negative and comes from the reduced (increased) transpiration related to the negative (positive) LAI anomalies.Simply prescribing observed vegetation data into LSMs does not guarantee the introduction of the correct coupling and feedback on climate. In this respect, this multi-model comparison experiment demonstrates the fundamental role of the inclusion of the underlying vegetation processes in LSMs. Ignoring the proper representation of the vegetation processes could lead to unrealistic (and even the opposite effects compared with observations) behaviour in reanalysis and climate predictions.

南方红壤典型水土流失区——福建省长汀县河田盆地区的大片山地曾由于植被稀少、山岭裸露、气候炎热,而被称之为"火焰山"。通过20多年的水土流失治理,该区的生态环境有了很大的改善。利用遥感技术,计算了长汀县河田盆地区在1988-2010年间的植被覆盖度变化及其伴随的地表温度变化。结果表明,通过近22a的水土流失治理,该区的平均植被覆盖度有了明显的升高,从1988年的48.83%上升到2010年的59.78%;而同期盆地内外的温度差异也随之下降,盆地裸露区和周边地区的温差降低了近1 ℃。定量研究表明,河田盆地区的植被覆盖度每增加10%,可降低地表温度0.6-1℃。;Soil erosion has caused severe ecological problems in China. Being a typical reddish soil erosion region in southtern China, the hilly areas of the Hetian Basin in County Changting, Fujian province, have been called flame mountainous area, due to lack of vegetation covers, extensively exposed bare soils, and hot weathers. After more than 20 years of soil-loss treatment, the local ecosystem and environment have been greatly improved. This study used remote sensing techniques and four scenes of Landsat Thematic Mapper (TM) images, enquired in 1988, 1998, 2004, and 2010, to study the fractional vegetation cover changes and accompanied land surface temperature variations in the Hetian basinal area during the period from 1988 to 2010. Two remote-sensing based models for estimating fractional vegetation cover have been compared in order to select a suitable model for the retrieval of the fractional vegetation cover in the study area. The two models compared are the Carlson-Ripley and Gutman-Ignatov models. The comparative analysis confirmed that the Carlson-Ripley model had a higher accuracy than the Gutman-Ignatov model as it has a lower systematic error value. Therefore, the Carlson-Ripley model was used to retrieve the fractional vegetation cover for the study area. While, the land surface temperature change during the study period was evaluated through the temperature difference between the areas inside and outside the basin in each study year. The results indicate that the 22-year effort for the treatment of the soil loss in the Hetian basinal area has resulted in the notably increase in fractional vegetation cover in the area, averagely from 48.83% in 1988 to 59.78% in 2010. Meanwhile, the difference in land surface temperature between the areas inside and outside the basin has also been reduced from 3.39℃ in 1988 to 2.48℃ in 2010, almost 1℃ declined. This suggests a lowering of the temperature in the area inside the basin. Obviously, the increase in fractional vegetation cover was accompanied with the lowering of regional temperature. Spatial and temporal analysis indicates that the changes in both fractional vegetation cover and land surface temperature mainly took place in the areas inside the Hetian Basin, while the areas outside the basin were rather consistent either in fractional vegetation cover or in land surface temperature. Quantitatively analysis suggests that each 10% increment in fractional vegetation cover would result in the lowering of local land surface temperature approximately by 0.6 to 1℃ based on regression models of the four study years.

ABSTRACTSun-induced chlorophyll fluorescence (SIF) is widely used to monitor vegetation physiological conditions. In this paper, SIF derived from the Global Ozone Monitoring Experiment–2 (GOME-2), and gross primary productivity (GPP), normalized difference vegetation index (NDVI), land surface temperature (LST), soil moisture and precipitation are used to investigate vegetation stress in six areas of Yunnan Province in China. The results illustrate that SIF, NDVI, and GPP all decrease in the condition of water stress and increase when recovering towards a healthy condition; LST increases under stress conditions and decreases gradually during recovery conditions. Then, analyses of long time-series for SIF, GPP, NDVI, and LST from 2010 to 2017 are conducted. These performances demonstrate SIF is much more sensitive than GPP, the NDVI, and LST during drought conditions. When vegetation is in the condition of water stress, SIF has earlier responses than other parameters. Last, the relationships between SIF and soil moisture in 2010–2016 yield significant results, with the correlation coefficient r = 0.56, 0.71, 0.75, 0.74, 0.62, 0.52 and 0.47, sequentially. The correlation coefficients are used to measure how strong a relationship is between two variables. This work demonstrates that SIF is a good way to monitor water condition change, and may play a great role in drought monitoring in the future.

• LAI prediction accuracy improves by integrating remote sensing VNIR and TIR data. • The relationship between LAI and LST is found to be insignificant. • LSE has a positive correlation with LAI. • Accurate measurement of the percentage of vegetation cover is crucial for LSE retrieval accuracy. • LAI prediction accuracy is higher with LST retrieved from a lower flight altitude. The leaf area index (LAI) is a crucial biophysical variable for remote sensing vegetation studies. LAI estimation through remote sensing data has mostly been investigated using visible and near-infrared (0.4–1.3 μm, VNIR) and Shortwave Infrared (1.4–3 μm, SWIR) data. However, Thermal Infrared (3–14 μm, TIR) data for LAI retrieval has rarely been explored. This study aims to predict LAI by integrating VNIR and TIR data from Unmanned Aerial Systems (UAS) in a mixed temperate forest, the Haagse Bos, Enschede, the Netherlands. The VNIR and TIR images were acquired in September 2020, in conjunction with fieldwork to collect LAI in situ data for 30 plots. TIR images were acquired at two heights (i.e., 85 m and 120 m above ground) to investigate the effect of flight height on the LAI prediction accuracy by means of UAS data. Land Surface Temperature (LST) and Land Surface Emissivity (LSE) were computed and extracted from the collected images. LAI was estimated using seven vegetation indices and Partial Least Squares Regression (PLSR). LAI prediction accuracy using VNIR reflectance spectra was compared to the accuracy achieved by integrating VNIR data with LST or LSE applying vegetation indices as well as PLSR. Among the applied vegetation indices, the Reduced Simple Ratio (RSR) gained the highest prediction accuracy using VNIR data (R 2 = 0.5815, RMSE = 0.6972); the prediction accuracy was not improved when LST was integrated with VNIR data but increased when LSE was included (RSR: R 2 = 0.7458, RMSE = 0.5081). However, when LST from 85 m altitude and VNIR data was applied as an input using PLSR (R 2 = 0.5565, RMSE CV = 0.7998), the LAI prediction accuracy was slightly increased compared to when only VNIR data was used (R 2 = 0.4452, RMSE CV = 0.8668). Integrating VNIR data with LSE significantly improved the LAI retrieval accuracy (R 2 = 0.7907, RMSE CV = 0.8351). These findings corroborate prior research indicating that combining LSE with VNIR data can increase the prediction accuracy of LAI. However, LST was found to be ineffective for this purpose. The relationship between LAI and LSE should be the subject of more investigation through various approaches to bridge the existing scientific gap.

The urbanization effects on land surface phenology (LSP) have been investigated by many studies, but few studies have focused on the temporal variations of urbanization effects on LSP. In this study, we used the Moderate-resolution Imaging Spectroradiometer (MODIS) Enhanced Vegetation Index (EVI), MODIS Land Surface Temperature (LST) data and China’s Land Use/Cover Datasets (CLUDs) to investigate the temporal variations of urban heat island intensity (UHII) and urbanization effects on LSP in Northeast China during 2001–2015. LST and phenology differences between urban and rural areas represented the urban heat island intensity and urbanization effects on LSP, respectively. A Mann–Kendall nonparametric test and Sen’s slope were used to evaluate the trends of urbanization effects on LSP and urban heat island intensity. The results indicated that the average LSP during 2001–2015 was characterized by high spatial heterogeneity. The start of the growing season (SOS) in old urban areas had become earlier and earlier compared to rural areas, and the differences in SOS between urbanized areas and rural areas changed greatly during 2001–2015 (−0.79 days/year, p < 0.01). Meanwhile, the length of the growing season (LOS) in urban and adjacent areas had become increasingly longer than rural areas, especially in urbanized areas (0.92 days/year, p < 0.01), but the differences in the end of the growing season (EOS) between urban and adjacent areas did not change significantly. Next, the UHII increased in spring and autumn during the whole study period. Moreover, the correlation analysis indicated that the increasing urban heat island intensity in spring contributed greatly to the increases of urbanization effects on SOS, but the increasing urban heat island intensity in autumn did not lead to the increases of urbanization effects on EOS in Northeast China.

<p>Moisture recycling is an important process in the hydrological system, as well as an important ecosystem service being responsible for more than 10% of precipitation in the majority of terrestrial areas. Changes in land use are known to affect this process, however, detailed understanding on how vegetation characteristics, i.e., plant traits, are seldom included in modeling this important process. To overcome this knowledge gap, we conduct a first order examination of the effect of plant traits on recycling, where we examine how variation in plant traits influences moisture recycling properties (average and standard deviation) in the Amazon Basin. More specifically, we used remotely-sensed estimates of trait values for: Specific Leaf Area (SLA), Leaf Dry Matter Content (LDMC), Leaf Phosphorus Content (LPC), Leaf Nitrogen Content (LNC), as well as information on Normalised Difference Vegetation Index (NDVI), and Leaf Area Index (LAI) for 10 years (2001-2010). We link this data on plant traits to six parameters that relevant for moisture recycling, namely Evapotranspiration (ET), Potential Evapotranspiration (PET), Land Surface Temperature (LST) Day and Night, Soil Moisture (SM) and Vapour Pressure Deficit (VPD). We used multivariate regression to analyse how plant traits explain the variance of moisture recycling parameters and find that NDVI (10- 40%), LAI (10-50%) and SLA (5-20%) exert the strongest effects on moisture recycling parameters suggesting that leaf gas exchange traits are most important in comparison to the other traits. We find, however, that the strength and the directionality of the effect while variable, it matches the expectations: NDVI positively correlates with ET, PET and negatively correlates with SM and LST Night; SLA positively correlates with VPD and LST Day. These results suggest that leaf gas exchange properties operate differently during the day and night-time, likely constrained by SM availability, and are linked with VPD and ET exchanges in the direction expected. We then examined whether these patterns were exacerbated or attenuated at the extremes of plant trait values using quantile regression (5th, 50th and 95th), to find that indeed some relationships became stronger (e.g. NDVI and LST Night, LAI and PET, ET and LST Day), while others became more attenuated (e.g. LPC and VPD, NDVI and ET). Finally, we examined whether the effect of traits would be related to the sub-basin processes due to the found control of SM on trait effects and founnd that nutrient and dry matter traits became more important, mostly for the extremes of trait distributions. These results show a promising first approach to include trait distributions in modeling hydrological processes. Indeed, we find some relationships in the direction expected, exacerbated in some cases at the extremes of trait distributions, and at local scale we show that different processes control hydrological parameters in comparison to the whole basin. While promising, more and better estimates of traits through remote sensing or in situ data acquisition are necessary to gain a better understanding of which traits might need to be managed to maintain this important ecosystem service and to understand its links with the overall hydrological cycle.</p>

The Advanced Scatterometer (ASCAT), Soil Moisture Active Passive (SMAP), and European Space Agency-Climate Change Initiative (ESA–CCI) soil moisture (SM) products are widely used in agricultural drought monitoring, water resource management, and climate analysis applications. However, the performance of these SM products varies significantly across regions and environmental conditions, due to in sensor characteristics, retrieval algorithms, and the lack of localized calibration. This study proposes a multi-source long short-term memory (MLSTM) for improving ASCAT, ESA–CCI, and SMAP SM products by combining in-situ SM measurements and four key auxiliary variables: precipitation (PRE), land surface temperature (LST), fractional vegetation cover (FVC), and evapotranspiration (ET). First, the in-situ measured data from four in-situ observation networks were corrected using the LSTM method to match the grid sizes of ASCAT (0.1°), ESA–CCI (0.25°), and SMAP (0.1°) SM products. The RPE, LST, FVC, and ET were used as inputs to the LSTM to obtain loss data against in-situ SM measurements. Second, the ASCAT, ESA–CCI, and SMAP SM datasets were used as inputs to the LSTM to generate loss data, which were subsequently corrected using LSTM-derived loss data based on in-situ SM measurements. When the mean squared error (MSE) loss values were minimized, the improvement for ASCAT, ESA–CCI, and SMAP products was considered the best. Finally, the improved ASCAT, ESA–CCI, and SMAP were produced and evaluated by the correlation coefficient (R), root mean square error (RMSE), and standard deviation (SD). The results showed that the RMSE values of the improved ASCAT, ESA–CCI, and SMAP products against the corrected in-situ SM data in the OZNET network were lower, i.e., 0.014 cm3/cm3, 0.019 cm3/cm3, and 0.034 cm3/cm3, respectively. Compared with the ESA–CCI and SMAP products, the ASCAT product was greatly improved, e.g., in the SNOTEL network, the Root Mean-Square Deviation (RMSD) values of 0.1049 cm3/cm3 (ASCAT) and 0.0662 cm3/cm3 (improved ASCAT). Overall, the MLSTM-based algorithm has the potential to improve the global satellite SM product.

This study presents a global reanalysis of land surface variables from 1981 to 2022 at a 0.25° x 0,25° spatial resolution, conducted using the ISBA land surface model within the SURFEX v9 modeling platform. Two experiments were performed: (1) an open-loop (OL) simulation with the ISBA land surface model and (2) a data assimilation analysis (ANA) experiment incorporating Leaf Area Index (LAI) observations in ISBA using the LDAS-Monde system. The simulations were forced offline with ERA5 reanalysis atmopsheric variables. The assimilated LAI dataset consists of THEIA AVHRR-derived observations from 1981 to 2018, and of Copernicus Land Monitoring Service (CLMS) GEOV2 from 2019 to 2022.The study analyzes the impact of LAI assimilation on surface variables, with a particular focus on soil temperature anomalies at deep layers. By comparing the OL and ANA runs, we assess how improved vegetation representation influences soil thermal dynamics and energy exchanges over multiple decades.To validate the impact of LAI assimilation, an evaluation is conducted over France, where in situ temperature observations at 1m depth are available from more than 70 automatic weather stations. This comparison provides insight into how LAI assimilation affects subsurface thermal conditions and helps quantify its added value in the reanalysis. Additionally, on a global scale, we assess the impact of LAI assimilation on Gross Primary Production (GPP), highlighting the improvements observed after integrating LAI observations into the model.This long-term global reanalysis, spanning over 40 years, provides a unique dataset for studying historical land surface dynamics and their interactions with climate variability. Having both OL and ANA simulations is crucial, as the OL experiment serves as a benchmark for understanding long-term trends driven by atmospheric forcing alone, while the ANA experiment allows us to quantify the impact of observational constraints on land surface processes. This dual approach is essential for improving our confidence in land surface modeling, enabling better representation of vegetation-atmosphere interactions, and providing a solid foundation for future climate studies, hydrological assessments, and ecosystem monitoring.

To solve the limitation of coarse spatial resolution of passive microwave surface soil moisture (SSM) product, many spatial downscaling methods has been proposed under the theoretic basis of the land surface temperature (LST)/vegetation index triangle space. However, in most studies, the topographic influences on the downscaling results is rarely analyzed but the impacts should be significant due to the strong effect of topographic changes on LST. To effectively solve this issue, a downscaling approach was developed in this study to disaggregate the Soil Moisture Active and Passive (SMAP) SSM product with the use of the Optical/Thermal infrared (TIR) observations from the Moderate-Resolution Imaging Spectro-radiometer (MODIS) onboard the Terra satellite for a typical mountain region located in the west of U.S. Two steps are included in the approach: (1) normalizing the terrain effect on LST and (2) downscaling passive SSM product based on a machine learning approach. The SNOTEL soil moisture observation network located in the study area was used to validate the downscaled SSM.

Strong positive direct impact of soil moisture on the growth of central asian grasslands

The process of the surface urban heat island (SUHI) varies with latitude, climate, topography and meteorological conditions. This study investigated the seasonal variability of SUHI in the Tehran metropolitan area, Iran, with respect to selected surface biophysical variables. Terra Moderate Resolution Imaging Spectroradiometer (MODIS) Land Surface Temperature (LST) was retrieved as nighttime LST data, while daytime LST was retrieved from Landsat 8 Thermal Infrared Sensor (TIRS) using the split-window algorithm. Both data covered the time period from September 2013 to September 2015. To assess SUHI intensity, we employed three SUHI indicators, i.e., the LST difference of urban-rural, that of urban-agriculture and that of urban-water. Physical and biophysical surface variables, including land use and land cover (LULC), elevation, impervious surface (IS), fractional vegetation cover (FVC) and albedo, were selected to estimate the relationship between LST seasonal variability and the surface properties. Results show that an inversion of the SUHI phenomenon (i.e., surface urban cool island) existed at daytime with the maximal value of urban-rural LST difference of −4 K in March; whereas the maximal value of SUHI at nighttime yielded 3.9 K in May. When using the indicators of urban-agriculture and urban-water LST differences, the maximal value of SUHI was found to be 8.2 K and 15.5 K, respectively. Both results were observed at daytime, suggesting the role of bare soils in the inversion of the SUHI phenomenon with the urban-rural indicator. Maximal correlation was observed in the relationship between night LST and elevation in spring (coefficient: −0.76), night LST and IS in spring (0.60), night LST and albedo in winter (−0.53) and day LST with fractional vegetation cover in summer (−0.41). The relationship between all surface properties with LST possessed large seasonal variations, and thus, using these relationships for SUHI modeling may not be effective. The only exception existed in the correlation between elevation and IS, which may be useful to simulate the SUHI at night. This study suggests that in semi-arid cities, such as Tehran, with the urban-rural indicator, a surface urban cool island may be observed in daytime while SUHI at nighttime; with other indicators, SUHI can be observed in both day and night. Thus, SUHI studies require the acquisition of remote sensing image data at both daytime and nighttime and careful selection of SUHI indicators.