MODIS Land Surface Temperature Data Research Articles

Comparing the Evolution of Land Surface Temperature and Driving Factors between Three Different Urban Agglomerations in China

Increases in land surface temperature (LST) and the urban heat island effect have become major challenges in the process of urban development. However, few studies have examined variations in LST between different urban agglomerations (UAs). Based on MODIS LST data, we quantitatively analyzed the spatial and temporal evolution patterns of LST in three different UAs in China from 2000 to 2020—Beijing–Tianjin–Hebei (BTH) at the national level, the Shandong Peninsula (SP) at the regional level, and Central Shanxi (CS) at the city level—by employing urban agglomeration built-up area intensity (UABI), linear regression analyses, and geodetic detector models. The results showed the following: (1) The spatial and temporal evolution pattern of the LST in BTH was the most regularized; the spatial pattern of the LST in SP gradually evolved from “two points” to “a single branch”; and the LST of CS was easily influenced by the neighboring big cities. (2) The best-fitting coefficients for BTH, SP, and CS were R2BTH = 0.58, R2SP = 0.66, and R2CS = 0.58, respectively; every 10% increase in UABI warmed the LSTs in BTH, SP, and CS by 1.47 °C, 1.27 °C, and 1.83 °C, respectively. (3) The ranking of single-factor influence was DEM (digital elevation model) > UABI > NDVI > T2m (air temperature at 2 m) > POP (population). The UABI interacting with DEM had the strongest warming effect on LST, with the maximum value q(UABI ∩ DEM) BTH = 0.951. All factor interactions showed an enhancement of the LST in CS, but factors interacting with POP showed a weaker effect in BTH and SP, for which q(NDVI ∩ POP) BTH = 0.265 and q(T2m ∩ POP) SP = 0.261. As the development of UAs gradually matures, the interaction with POP might have a cooling effect on the environment to a certain degree.

Using enhanced vegetation index and land surface temperature to reconstruct the solar-induced chlorophyll fluorescence of forests and grasslands across latitude and phenology

IntroductionForest and grassland are the two main carbon-collecting terrestrial ecosystems, and detecting their solar-induced chlorophyll fluorescence (SIF) enables evaluation of their photosynthetic intensity and carbon-collecting capacity. Since SIF that is retrieved directly from satellite observations suffers from low spatial resolution, discontinuity, or low temporal resolution, some vegetation indexes (VIs) and meteorological factors are used as predictors to reconstruct SIF products. Yet, unlike VIs, certain meteorological factors feature a relatively low space resolution and their observations are not always accessible. This study aimed to explore the potential of reconstructing SIF from fewer predictors whose high-resolution observations are easily accessible.MethodsA total of six forest and grassland regions across low, mid, and high latitudes were selected, and the commonly used predictors-normalized difference vegetation index (NDVI), enhanced vegetation index (EVI), and land surface temperature (LST)—were compared for their correlation with SIF. Results show that the combination of EVI and LST is more strongly correlated with SIF, but each contributed differently to SIF at differing growth stages of forest and grassland. Accordingly, we proposed the idea of a combined sampling approach that considers both location and phenological phase, to explore the extent to which time and space coverage samples' span could enlarge the disparity of EVI data in particular regions at specific growth stages. To do that, three kinds of sample combination methods were proposed: monthly regression at a global scale, seasonal regression at a regional scale, and monthly regression at a regional scale. Following this, Sentinel-3 EVI and MODIS LST data were used to reconstruct 500 m SIF in the six regions by implementing the proposed methodology.Results and discussionThese results showed that the R2 values were ≥0.90 between the reconstructed SIF and MODIS GPP (gross primary productivity), 0.70 with GOME-2 SIF and 0.77 with GOSIF, thus proving the proposed methodology could produce reliable results for reconstruction of 500 m SIF. This proposed approach, which bypasses dependence of traditional SIF reconstruction model on numerous predictors not easy to obtain, can serve as a better option for more efficient and accurate high-resolution SIF reconstructions in the future.

Understanding the differences in the effect of urbanization on land surface temperature and air temperature in China: insights from heatwave and non-heatwave conditions

Accelerated urbanization and frequent heatwave events pose significant threats to human health. Analyses of the differences in air and land surface temperature (LST) under extreme climates can aid in understanding human-nature ecosystem coupling and the required adaptations to climate change. In this study, we quantified differences in urban and rural temperatures in China under heatwave (CHW) and non-heatwave periods (NHW) conditions and the influence of meteorological factors on these differences. Based on impervious surface data, 2421 urban and rural stations were dynamically classified from 2008 to 2017. Heatwaves were identified using relative thresholds, and differences were explored using meteorological data and MODIS LST data. For LST, urban–rural temperature difference (U-RTempdiff) was highest during the day, whereas air temperature peaks occurred at night, under both NHW and CHW conditions. During CHWs, the daytime U-RTempdiff was greater for LST than for air temperature, reaching 4.24 ± 3.38 °C. At night, U-RTempdiff was slightly lower (1.04 ± 1.41 °C). The proportion of air U-RTempdiff contributed by rural air temperature was significantly higher during CHW nights than during NHW nights, whereas the proportion of land surface and air U-RTempdiff remained relatively stable during daytime. Spatially, the daytime temperature difference in the north decreased with latitude, whereas the difference in the south was lower. Under CHWs, urbanization had a stronger effect on LST than on air temperature, with a slightly smaller difference (0.01 °C yr−1) during the day and a slightly larger difference (0.03 °C yr−1) at night. The contribution of urbanization to LST was higher than that to air temperature, particularly during the day (16.34%). The effects of wind speed and precipitation on the average air urban–rural temperature difference was greater than those of LST under CHW, accounting for 16.13%, with the effects of wind speed being more significant. These results show that a comprehensive perspective is needed to understand the risks associated with a temperature rise risk under extreme climate conditions and to formulate effective mitigation measures that will they improve human thermal comfort under climate change.

Surface urban heat island effect and its spatiotemporal dynamics in metropolitan area: a case study in the Zhengzhou metropolitan area, China

The deterioration of the urban surface thermal environment has seriously affected regional environments and human health, becoming a critical ecological problem faced by cities worldwide. This study focused on surface urban heat island effect in metropolitan area and selected the emerging metropolitan area of Zhengzhou, China, as a case study. Based on the MODIS land surface temperature data obtained from the Google Earth Engine the surface urban heat island intensity (SUHII) was calculated and its temporal and spatial dynamics were analyzed from 2003 to 2022. The main findings indicated that Zhengzhou, the core city of the metropolitan area, had the strongest urban heat island effect with day surface urban heat island intensity of 1.10°C and night SUHII of 1.39°C). Generally, the average annual SUHII was higher during the day than at night, and the maximum value was detected in summer (2.43°C). SUHII showed an increasing trend at night, especially in summer during the study period. It decreased obviously in urban centers during the day, while it increased obviously in the outer urban areas at night. The results of this study contributed to the understanding of the spatiotemporal dynamics of the urban heat island effect in the Zhengzhou metropolitan area.

Application of MODIS land surface temperature data on ENSO-based analysis in Kalimantan

El Nino Southern Oscillation as climate anomaly has been impacted Land Surface Temperature (LST) in Indonesia. Higher temperature during El Nino has been considered to give more risk in fire hazards in Kalimantan, thus, analysis of spatial and temporal distribution of LST became crucial. This study aims to analyze the spatial and temporal distribution of land surface temperature in Kalimantan based on the ENSO phases from 2014 to 2020. MODIS MOD11A1 images and ONI index were used as analysis materials. Image processing was conducted by selecting pixels using the QC band and then processing the LST values using a scale using RStudio software. The result shows the distribution of higher LST values on the southern side of Kalimantan, influenced by low elevation, peat soil, and land use change. The temporal distribution shows a tendency for higher temperatures in the dry months of August to October, with an increase of 4.5 percent during El Nino and a decrease of 0.9 percent during La Nina from 2014 to 2020. This study could contribute to management and mitigation for fire risk in certain area and period as ENSO occurred in Kalimantan.

Interaction of permafrost degradation and thermokarst lakes in the Qinghai–Tibet Plateau

The degradation of ice-rich permafrost can form thermokarst landforms such as thermokarst lakes. There are a large number of thermokarst lakes distributed in the ice-rich permafrost region of the Qinghai–Tibet Plateau. However, the effect of the spatial distribution of thermokarst lakes on permafrost degradation at a regional-scale in the Qinghai–Tibet Plateau is not fully understood. Time-series interferometric synthetic aperture radar is an established remotely-sensing tool to measure surface deformation in permafrost regions, and has been widely used in permafrost degradation investigation. In this study, we characterized spatial-temporal variations of surface deformation in the Beiluhe Basin in the Qinghai–Tibet Plateau with time series InSAR analysis, and extracted thermokarst lakes from Landset-8 images. Additionally, MODIS land surface temperature data and Stefan model were used to estimate the active layer thickness of permafrost in the basin. Results showed that the linear subsidence rate in the permafrost region within the basin generally exceeded 5 mm/yr, with a seasonal deformation amplitude of >10 mm, and the active layer thickness ranged from 2.1 m to 3.5 m. We found that permafrost degradation was concentrated in the areas with a high density of thermokarst lakes. Specifically, the density of thermokarst lakes was positively correlated with the linear subsidence rate and the seasonal deformation amplitude, and the larger active layer thickness clustered significantly in the area of thermokarst lakes. We also found that accelerated thermokarst collapse of the lake shore intensified the development of thermokarst lakes, and the lake expansion further aggravated the permafrost deformation. In addition, there is a strongly negative correlation (r = −0.78) between active layer thickness and Normalized Difference Vegetative Index in the basin, inferring that the degradation of permafrost may lead to the transformation from alpine meadow to alpine grassland and alpine desert.

Recent trends of land surface temperature in relation to the influencing factors using Google Earth Engine platform and time series products in megacities of India

Increasing land surface temperature (LST) is one of the significant anthropogenic issues that is threatening livelihoods in urban areas. Understanding the urban LST is essential for sustainable management of the urban landscape. Various studies focused on the spatial pattern and average trend of LST in relation to influencing factors, but little is known regarding the pixel-scale spatiotemporal trends and integration of multiple influencing factors in the different seasons using time series data. Therefore, we focused on the spatiotemporal trends of LST in relation to the influencing factors using the Google Earth Engine platform and time series products. MODIS land surface temperature data was used to estimate the annual and seasonal day-night LST from 2003 to 2020. Theil-Sen slope and Mann-Kendall statistics were used to examine the magnitude and significance of the spatiotemporal trend. All-subsets regression and hierarchical partitioning (HP) were applied to explore the driving factors of LST. Average LST and range varied between the cities, but were highest in Chennai and Delhi, respectively. Except for winter nighttime in Kolkata, all cities' annual and seasonal nighttime LST are increasing (0.027–0.075 °C/year) over a large part of the study area (33.934%–100% of areas), indicating that anthropogenic force is increasing in urban areas during nighttime. Annual and seasonal daytime LST is increasing in most parts (55.545%–94.604% of areas) of Chennai and Kolkata, but daytime LST is decreasing (33.602%–98.571% of areas) in Mumbai and Delhi due to the increasing concentration of absorbing aerosol over the city. Strength of independent factors and multiple influencing factors varied in the day-night time between the cities. But nighttime LST was well explained using the all-subsets regression compared to daytime LST, except for Mumbai. Also, nighttime light density and NDBI had consistent effects on nighttime, and daytime, respectively. These findings can help to take scientific adaptation and mitigation strategies for sustainable development.

Estimation of Urban-Rural Land Surface Temperature Difference at Different Elevations in the Qinling-Daba Mountains Using MODIS and the Random Forest Model.

Land surface temperature (LST) variations are very complex in mountainous areas owing to highly heterogeneous terrain and varied environment, which complicates the surface urban heat island (SUHI) in mountain cities. Previous studies on the urban heat island (UHI) effect mostly focus on the flat terrain areas; there are few studies on the UHI effect in mountainous areas, especially on the influence of elevation on the SUHI effect. To determine the SUHI in the Qinling–Daba mountains (China), MODIS LST data were first preprocessed and converted to the same elevations (1500 m, 2000 m, 2500 m, 3000 m, and 3500 m) using a digital elevation model and the random forest method. Then, the average LSTs in urban land, rural land, and cultivated land were calculated separately based on the ranges of the invariable urban, rural, and cultivated areas during 2010–2018, and the urban, rural, and cultivated land LST difference were estimated for the same elevations. Results showed that the accuracy of LST estimated using the random forest method is very high (R2 ≥ 0.9) at elevations of 1500 m, 2000 m, 2500 m, 3000 m and 3500 m. The difference in urban, rural, and cultivated lands’ LST has a trend of decrease with increasing elevation, meaning that the SUHI weakens at higher elevations. The average LST of urban areas is 0.52–0.59 °C (0.42–0.57 °C) higher than that of rural and cultivated areas at an elevation of 1500 m (2000 m). The average LST of urban areas is 0.10–1.25 °C lower than that of rural and cultivated areas at elevations of 2500 m, 3000 m, and 3500 m, indicating absence of the SUHI at those elevations.

Evaluating contributions of urbanization and global climate change to urban land surface temperature change: a case study in Lagos, Nigeria

This study develops a general method to evaluate the contributions of localized urbanization and global climate change to long-term urban land surface temperature (ULST) change. The method is based on the understanding that long-term annual ULST is controlled by three factors: (1) localized urbanization, (2) global climate change, and (3) interannual climate variation. Then the method removes the interannual climate fluctuations on long-term observed LST time series via linear regression and separates the contributions of urbanization and climate change to the impacts on long-term ULST via urban–rural comparison. The method is applied to Lagos, a fast-growing metropolis in the tropical West Africa, as an example for reference. Combined time-series daily daytime and nighttime MODIS Land Surface Temperature (LST) data over the years of 2003–2021 are used as the representation of land surface temperature. To avoid the potentioal interannual data biase due to uneven availability of data in the rainy seasons over years, only MODIS LST data from dry seasons are used in the study. The results are summarized as follows for Lagos: (1) long-term annual ULST is confirmed to be controlled by the three factors; (2) the proposed method can separate the contribution of the three factors to the ULST; (2) both localized urbanization and global warming are verified to contribute to the ULST increase with positive trends; (3) daytime ULST increased the most in the afternoon time at a mean rate of 1.429 °C per decade, with 0.985 °C (10 year)−1 contributed by urbanization and 0.444 °C (10 year)−1 contributed by climate warming; (4) nighttime ULST in Lagos increased the most after midnight at a rate of 0.563 °C (10 year)−1, with 0.56 °C (10 year)−1 contributed by urbanization and 0.003 °C (10 year)−1 contributed by climate warming; and (5) urbanization is generally responsible for around 60.97% of the urban warming in Lagos. Therefore, the increasing urbaniztion-induced urban heat island effect is the major cause for more heat-related health risks and climate extremes that many urban residents are suffering. The results of this study are of useful reference for both urbanization and climate change related issues in the geo-science field.

Heat wave-induced augmentation of surface urban heat islands strongly regulated by rural background

• SUHI and SUCI are both augmented in heat wave (HW) periods. • Augmentation of SUHI and SUCI is regulated by rural vegetation coverage. • SUHIs in north subtropical climate exhibit the largest augmentation. • SUHIs are strengthened with an intensified HW intensity in most climate zones. The impact of heat waves (HWs) on surface urban heat islands (SUHIs) has been widely studied, but the spatial pattern of SUHI responsiveness to HWs across various climates remains unclear, and the influence of HW intensity on SUHI responsiveness has not been systematically quantified. Using MODIS land surface temperature data, here we investigated the responsiveness of SUHI to HWs (quantified as ∆ I ) as well as its variations with HW intensity in 354 cities in seven climate zones across China. We find that during HW periods, the SUHI and surface urban cool island are augmented in the humid and arid regions of China, respectively. The inter-city heterogeneity in rural vegetation coverage accounts for such a spatial pattern. In eastern China, the ∆ I peaks in the north subtropical climate (0.72 ± 0.54 K for daytime and 0.29 ± 0.23 K for the nighttime) probably for its specific rural farming method. With the intensification of HWs, the augmentation effect can be further enhanced for the north subtropical, warm temperate, and arid temperate climates during the day and for almost all the climates at night. These findings can help advance the understanding of the responsiveness of SUHI to extreme climatic events.

Analysis of surface temperature variation of lakes in China using MODIS land surface temperature data

China has a great wealth of lake resources over a great spatial extent and these lakes are highly sensitive to climate changes through their heat and water budgets. However, little is known about the changes in lake surface water temperature (LSWT) across China under the climate warming conditions over the past few decades. In this study, MODIS land surface temperature (LST) data were used to examine the spatial and temporal (diurnal, intra-annual, and inter-annual) variations in LSWT of China’s lakes during 2001–2016. Our results indicated that 169 large lakes included in the study exhibited an overall increasing trend in LSWT, with an average rate of 0.26 °C/decade. The increasing rate of nighttime LSWT is 0.31 °C/decade, faster than that of daytime temperature (0.21 °C/decade). Overall, 121 (71.6%) lakes showed an increase in daytime temperature with a mean rate of 0.38 °C/decade, while the rest 48 (28.4%) lakes decreased in temperature with a mean rate of − 0.21 °C/decade. We also quantitatively analyzed the relationship of the lake surface temperature and diurnal temperature differences (DTDs) with geographical location, topography, and lake morphometry by utilizing multivariate regression analysis. Our analysis suggested that the geographical location (latitude and longitude) and topography (altitude) were primary driving factors in explaining the national lake water temperature variation (P < 0.001), which were also mediated by morphometric factors such as lake surface area and volume. Moreover, the diurnal lake temperature variations were significantly correlated with altitude, latitude, and lake surface area (R2 = 0.426, P < 0.001). Correlation analyses of LSWT trend and air temperature trend for each lake indicated that LSWT was positively correlated with air temperature in both daytime and nighttime for most lakes.

Modified Linear Scaling and Quantile Mapping Mean Bias Correction of MODIS Land Surface Temperature for Surface Air Temperature Estimation for the Lowland Areas of Peninsular Malaysia

MODIS land surface temperature data (MODIS Ts) products are quantified from the earth surface’s reflected thermal infrared signal via sensors onboard the Terra and Aqua satellites. MODIS Ts products are a great value to many environmental applications but often subject to discrepancies when compared to the air temperature (Ta) data that represent the temperature measured at 2 m above the ground surface. Although they are different in their nature, the relationship between Ts and Ta has been established by many researchers. Further validation and correction on the relationship between these two has enabled the estimation of Ta from MODIS Ts products in order to overcome the limitation of Ta that can only provide data in a point form with a very limited area coverage. Therefore, this study was conducted with the objective to assess the accuracy of MODIS Ts products, i.e., MOD11A1, MOD11A2, MYD11A1, and MYD11A2 against Ta and to identify the performance of a modified Linear Scaling using a constant and monthly correction factor (LS-MBC), and Quantile Mapping Mean Bias Correction (QM-MBC) methods for lowland area of Peninsular Malaysia. Furthermore, the correction factor (CF) values for each MBC were adjusted according to the condition set depending on the different bias levels. Then, the performance of the pre- and post-MBC correction for by stations and regions analysis were evaluated through root mean square error (RMSE), percentage bias (PBIAS), mean absolute error (MAE), and correlation coefficient (r). The region dataset is obtained by stacking the air temperature (Ta_r) and surface temperature (Ts_r) data corresponding to the number of stations within the identified regions. The assessment of pre-MBC data for both 36 stations and 5 regions demonstrated poor correspondence with high average errors and percentage biases, i.e., RMSE = 3.33–5.42 °C, PBIAS = 1.36–12.07%, MAE = 2.88–4.89 °C, and r = 0.16–0.29. The application of the MBCs has successfully reduced the errors and bias percentages, and slightly increased the r values for all MODIS Ts products. All post-MBC depicted good average accuracies (RMSE and MAE &lt; 3 °C and PBIAS between ±5%) and r between 0.18 and 0.31. In detail, for the station analysis, the LS-MBC using monthly CF recorded better performance than the LS-MBC using constant CF or the QM-MBC. For the regional study, the QM-MBC outperformed the others. This study illustrated that the proposed LS-MBC, in spite of its simplicity, managed to perform well in reducing the error and bias terms of MODIS Ts as much as the performance of the more complex QM-MBC method.

Extending the Spatio-Temporal Applicability of DISPATCH Soil Moisture Downscaling Algorithm: A Study Case Using SMAP, MODIS and Sentinel-3 Data

DISPATCH is a disaggregation algorithm of the low-resolution soil moisture (SM) estimates derived from passive microwave observations. It provides disaggregated SM data at typically 1 km resolution by using the soil evaporative efficiency (SEE) estimated from optical/thermal data collected around solar noon. DISPATCH is based on the relationship between the evapo-transpiration rate and the surface SM under non-energy-limited conditions and hence is well adapted for semi-arid regions with generally low cloud cover and sparse vegetation. The objective of this paper is to extend the spatio-temporal coverage of DISPATCH data by 1) including more densely vegetated areas and 2) assessing the usefulness of thermal data collected earlier in the morning. Especially, we evaluate the performance of the Temperature Vegetation Dryness Index (TVDI) instead of SEE in the DISPATCH algorithm over vegetated areas (called vegetation-extended DISPATCH) and we quantify the increase in coverage using Sentinel-3 (overpass at around 09:30 am) instead of MODIS (overpass at around 10:30 am and 1:30 pm for Terra and Aqua, respectively) data. In this study, DISPATCH is applied to 36 km resolution Soil Moisture Active and Passive SM data over three 50 km by 50 km areas in Spain and France to assess the effectiveness of the approach over temperate and semi-arid regions. The use of TVDI within DISPATCH increases the coverage of disaggregated images by 9 and 14% over the temperate and semi-arid sites, respectively. Moreover, including the vegetated pixels in the validation areas increases the overall correlation between satellite and in situ SM from 0.36 to 0.43 and from 0.41 to 0.79 for the temperate and semi-arid regions, respectively. The use of Sentinel-3 can increase the spatio-temporal coverage by up to 44% over the considered MODIS tile, while the overlapping disaggregated data sets derived from Sentinel-3 and MODIS land surface temperature data are strongly correlated (around 0.7). Additionally, the correlation between satellite and in situ SM is significantly better for DISPATCH (0.39–0.80) than for the Copernicus Sentinel-1-based (−0.03 to 0.69) and SMAP/S1 (0.37–0.74) product over the three studies (temperate and semi-arid) areas, with an increase in yearly valid retrievals for the vegetation-extended DISPATCH algorithm.

Estimation of the Urban Heat Island Intensity Change and Its Relationships with Driving Factors Across China Based on the Human Settlement Scale

Considering a background of global warming, the effect of urban heat island (UHI) has become a serious ecological and environmental problem that is commonly faced by modern cities worldwide, posing challenges to the physical and mental health of residents in urban areas, air quality, and the healthy growth of surface vegetation. Using time series MODIS land surface temperature data products from 2001 to 2018, we evaluated the urban heat island intensity (UHII) and its spatiotemporal patterns in 1232 major urban human settlement patches (area>10 km2) across China based on the consistency method. A random forest regression model (RF) was also used to reveal the driving mechanism of urban heat island intensity in China due to human activities, urban morphology, vegetation, topography, and meteorological factors. The results show that more than 90% of cities and towns in China have exhibited significant diurnal heat island phenomena on the scale of human settlements. The diurnal UHII reaches up to (0.75±0.6)℃ and (0.81±0.53)℃, respectively. The daytime UHII in summer was significantly higher than that in winter, while there was little difference in nighttime UHII between summer and winter. During the past 18 years, the daytime UHII in China has shown a slight downward trend, while the nighttime UHII has increased significantly, with no significant change in summer and on mean annual scales. In terms of spatial distribution, the eastern coastal provinces have higher daytime UHII than those of the western or plateau provinces, while the nighttime UHII shows the opposite pattern. The RF regression results show that the average annual rainfall and latitudinal position of cities and towns are the two most important factors controlling the daytime and nighttime UHII in China. Narrowing the differences in vegetation growth between urban and rural environments will have a mitigating effect on the daytime UHII, and controlling the expansion of small and medium-sized towns will also play a positive role in the continuously rising nighttime UHII.

A Machine Learning Approach for Remote Sensing Data Gap-Filling with Open-Source Implementation: An Example Regarding Land Surface Temperature, Surface Albedo and NDVI

Satellite remote sensing has now become a unique tool for continuous and predictable monitoring of geosystems at various scales, observing the dynamics of different geophysical parameters of the environment. One of the essential problems with most satellite environmental monitoring methods is their sensitivity to atmospheric conditions, in particular cloud cover, which leads to the loss of a significant part of data, especially at high latitudes, potentially reducing the quality of observation time series until it is useless. In this paper, we present a toolbox for filling gaps in remote sensing time-series data based on machine learning algorithms and spatio-temporal statistics. The first implemented procedure allows us to fill gaps based on spatial relationships between pixels, obtained from historical time-series. Then, the second procedure is dedicated to filling the remaining gaps based on the temporal dynamics of each pixel value. The algorithm was tested and verified on Sentinel-3 SLSTR and Terra MODIS land surface temperature data and under different geographical and seasonal conditions. As a result of validation, it was found that in most cases the error did not exceed 1 °C. The algorithm was also verified for gaps restoration in Terra MODIS derived normalized difference vegetation index and land surface broadband albedo datasets. The software implementation is Python-based and distributed under conditions of GNU GPL 3 license via public repository.

A global terrestrial ecosystem respiration dataset (2001-2010) estimated with MODIS land surface temperature and vegetation indices

ABSTRACT This paper describes how a validated semi-empirical, but physiologically based, remote sensing model – Ensemble_all – was up-scaled using MODIS land surface temperature data (MOD11C2), enhanced vegetation indices (MOD13C1) and land-cover data (MCD12C1) to produce a global terrestrial ecosystem respiration data set (Reco) for January 2001–December 2010. The temporal resolution of this data set is 1 month, the spatial resolution is 0.05°, and the range is from 55°S to 65°N and 180°W to 180°E (crop and natural vegetation mosaic is not included). After cross-validating our data set using in-situ observations as well as Reco outputs from an empirical variable_Q10 model, a LPJ_S1 process model and a machine learning method model, we found that our data set performed well in detecting both temporal and spatial patterns in Reco’s simulation in most ecosystems across the world. This data set can be found at http://www.dx.doi.org/10.11922/sciencedb.934.

Extrapolation of daily air temperatures of Egypt from MODIS LST data

The limited and poor spatial distribution of meteorological stations is inadequate to reconstruct accurate regional air temperature maps, which are important for environmental planning and resources management. This study aimed to generate a gridded dataset of max/min daily surface air temperatures for Egypt based on remotely sensed data acquired from the MODIS land surface temperature (LST) product. Linear regression analysis was performed between observed air temperatures from ground weather stations and LST records from satellite images. Regression coefficients were as high as 0.8 between min air temperature and night LST data, compared to 0.77 for max air temperature and day LST. Statistical analysis reveals significant correlations (p < .01) for both regression relationships. MODIS data showed a satisfactory performance in producing regional monthly and annual as well as diurnal surface air temperatures maps for Egypt. MODIS data could also highlight the influence of topography, vegetation and lithology on air temperatures.

Spatiotemporal characterization of the urban sprawl and its impacts on urban island in China with DMSP/OLS and MODIS measurements

The rapid development of cities has led to the urban sprawl to the suburbs. Studies have shown that urban sprawl leads to the increasing intensity of heat island effect. This paper, based on the DMSP/OLS nighttime light data and the MODIS land surface temperature data during the years 2000, 2005, and 2013, discussed the spatial and temporal characteristics of urban sprawl, urban heat island intensity in China, and their relationship, respectively. The results show that (1) the national urban expansion index showed an upward trend. The urban area was 104,000 km2 in 2000, 169,000 km2 in 2005, and 312,000 km2 in 2013.The average annual growth rate was 10.2% in 2000–2005, 8% in 2000–2013, and 8.8% in 2000–2013. (2) The national heat island index also showed an upward trend; total heat island intensity was 716.73 °C in 2000, 812.84 °C in 2005, and 995.46 °C in 2013. The annual average growth rate was 2.55% in 2000–2005, 2.57% in 2005–2013, and 2.56% in 2000–2013. (3) There is a positive correlation between urban spatial expansion and heat island intensity in China. The urban and suburban range of China can be objectively extracted from the DMSP/OLS nighttime light data every year and superimposed with the MODIS land surface temperature data of the same period, which ensures the correctness of the urban heat island intensity based on the actual changes of suburban areas and main urban areas in each city, and overcomes the calculation error of the traditional method, which neglects that some suburbs have become urban areas and are still regarded as suburbs. These results provide spatiotemporal changes characterization of urban area in China during recent decades, which are helpful in decision-making of land management and planning for sustainable development.

Including Sentinel-1 radar data to improve the disaggregation of MODIS land surface temperature data

The use of land surface temperature (LST) for monitoring the consumption and water status of crops requires data at fine spatial and temporal resolutions. Unfortunately, the current spaceborne thermal sensors provide data at either high temporal (e.g. MODIS: Moderate Resolution Imaging Spectro-radiometer) or high spatial (e.g. Landsat) resolution separately. Disaggregating low spatial resolution (LR) LST data using ancillary data available at high spatio-temporal resolution could compensate for the lack of high spatial resolution (HR) LST observations. Existing LST downscaling approaches generally rely on the fractional green vegetation cover (fgv) derived from HR reflectances but they do not take into account the soil water availability to explain the spatial variability in LST at HR. In this context, a new method is developed to disaggregate kilometric MODIS LST at 100 m resolution by including the Sentinel-1 (S-1) backscatter, which is indirectly linked to surface soil moisture, in addition to the Landsat-7 and Landsat-8 (L-7 & L-8) reflectances. The approach is tested over two different sites – an 8 km by 8 km irrigated crop area named “R3” and a 12 km by 12 km rainfed area named “Sidi Rahal” in central Morocco (Marrakech) – on the seven dates when S-1, and L-7 or L-8 acquisitions coincide with a one-day precision during the 2015–2016 growing season. The downscaling methods are applied to the 1 km resolution MODIS-Terra LST data, and their performance is assessed by comparing the 100 m disaggregated LST to Landsat LST in three cases: no disaggregation, disaggregation using Landsat fgv only, disaggregation using both Landsat fgv and S-1 backscatter. When including fgv only in the disaggregation procedure, the mean root mean square error in LST decreases from 4.20 to 3.60 °C and the mean correlation coefficient (R) increases from 0.45 to 0.69 compared to the non-disaggregated case within R3. The new methodology including the S-1 backscatter as input to the disaggregation is found to be systematically more accurate on the available dates with a disaggregation mean error decreasing to 3.35 °C and a mean R increasing to 0.75.

Relationships between field-measured hydrometeorological variables and satellite-based land surface temperature in a hemiboreal raised bog

Temperature regime is one of the main controlling factors of greenhouse gas (GHG) emissions from peat bogs. Remotely sensed land surface temperature (LST) has a potential to become an efficient instrument in environmental monitoring of carbon dioxide and methane emissions from peat bogs. This paper examines the relationships between field-measured hydrometeorological variables and MODIS LST data in a hemiboreal raised bog for a period from May to September (2008–2016). The Pearson product-moment correlation was used to reveal the relationship between the field-measured parameters and LST over years and months. A multiple linear regression was chosen to model relationships between the hydrometeorological variables and LST by month. It was found that the relationships between the studied parameters and LST were year- and month-dependent. The main factor of LST was air temperature, and the correlation between LST and air temperature was the strongest during the entire period of study. This study has shown that the hydrometeorological factors of LST can explain 67%–81 % of the variance in LST in a hemiboreal raised bog. The relationships between the hydrometeorological variables and LST may be implemented in more accurate GHG emissions estimation from bogs.