MODIS Land Surface Temperature Product Research Articles

Land Surface Temperature Based Soil Moisture Dynamics Modeling for Chinese Mainland

Since remotely sensed land surface temperature (LST) and LST-derived indexes such as surface-to-air temperature gradient (Δ T) and day-to-night LST gradient (Δ LST) all contain important soil moisture (SM) information, it is meaningful to utilize easily available and near-real-time LST data for modeling the spatiotemporal SM dynamics. However, the optimal LST-derived index to appropriately quantify SM dynamics on a large scale remains to be studied. Considering the complex and diverse climate conditions and land cover types in the Chinese mainland, this letter proposes to evaluate Z-score indexes from LST-based SM dynamic modeling for the Chinese Mainland. Monthly LST and SM during April-October in 2000-2019 years are derived from the MOD11C3 (MODIS LST product) and ERA5-Land (the global reanalysis dataset), respectively. The Pearson correlation coefficients (Rs) between ZSM (Z-score of SM) and ZLST (Z-score of LST), ZΔ T (Z-score of Δ T), as well as ZΔLST (Z-score of Δ LST) are calculated. The average R between ZSM and ZLST is 0.44 over the whole domain. It is up to 0.7 for cultivated land and grassland in semi-arid and semi-humid areas. The R between ZSM and ZLST is stronger than the ones between ZSM and ZΔ T and ZΔ LST. Overall, ZLST can be viewed as a relatively robust and easy-to-calculate indicator for modeling SM dynamics in a large region. Even if the approach used is simple, its results are encouraging because it makes sense to actually use LST to capture SM dynamics in the Chinese mainland.

Spatio-Temporal Distribution Characteristics of Global Annual Maximum Land Surface Temperature Derived from MODIS Thermal Infrared Data From 2003 to 2019

Land surface temperature (LST) is an important parameter in the physical processes of energy and water balance at the local and global scales. Annual maximum composite of LST provides important information about ecosystem exposure patterns to extreme LST. It has the ability to characterize the changes associated with extreme climatic events and significant land-cover changes. In this study, the spatio-temporal distribution characteristics of global annual maximum LST extracted from the MODIS LST product (MYD11A1) during the period 2003-2019 were investigated. The results indicate that the spatial pattern of annual maximum LST is associated with the biophysical and biogeographic factors of Earth's ecosystems. The inter-annual variability of annual maximum LST during the period 2003-2019 is relatively small. The change trend of annual maximum LST during the period 2003-2019 in the globe is 0.1°C/decade. The annual maximum LST data was applied to detect thermal anomalies, including drought, heat wave, and ice melting. Some significant thermal anomaly events were well identified using the annual maximum LST data with standardized anomaly index larger than 2.5.

Evaluation of Landsat 8-like Land Surface Temperature by Fusing Landsat 8 and MODIS Land Surface Temperature Product

High-spatiotemporal-resolution land surface temperature (LST) is a crucial parameter in various environmental monitoring. However, due to the limitation of sensor trade-off between the spatial and temporal resolutions, such data are still unavailable. Therefore, the generation and verification of such data are of great value. The spatiotemporal fusion algorithm, which can be used to improve the spatiotemporal resolution, is widely used in Landsat and MODIS data to generate Landsat-like images, but there is less exploration of combining long-time series MODIS LST and Landsat 8 LST product to generate Landsat 8-like LST. The purpose of this study is to evaluate the accuracy of the long-time series Landsat 8 LST product and the Landsat 8-like LST generated by spatiotemporal fusion. In this study, based on the Landsat 8 LST product and MODIS LST product, Landsat 8-like LST is generated using Spatial and Temporal Adaptive Reflectance Fusion Model (STARFM), Enhanced STARFM (ESTARFM), and the Flexible Spatiotemporal DAta Fusion (FSDAF) algorithm, and tested and verified in the research area located in Gansu Province, China. In this process, Landsat 8 LST product was verified based on ground measurements, and the fusion results were comprehensively evaluated based on ground measurements and actual Landsat 8 LST images. Ground measurements verification indicated that Landsat 8 LST product was highly consistent with ground measurements. The Root Mean Square Error (RMSE) was 2.862 K, and the coefficient of determination R2 was 0.952 at All stations. Good fusion results can be obtained for the three spatiotemporal algorithms, and the ground measurements verified at All stations show that R2 was more significant than 0.911. ESTARFM had the best fusion result (R2 = 0.915, RMSE = 3.661 K), which was better than STARFM (R2 = 0.911, RMSE = 3.746 K) and FSDAF (R2 = 0.912, RMSE = 3.786 K). Based on the actual Landsat 8 LST images verification, the fusion images were highly consistent with actual Landsat 8 LST images. The average RMSE of fusion images about STARFM, ESTARFM, and FSDAF were 2.608 K, 2.245 K, and 2.565 K, respectively, and ESTARFM is better than STARFM and FSDAF in most cases. Combining the above verification, the fusion results of the three algorithms were reliable and ESTARFM had the highest fusion accuracy.

Determination of global land surface temperature using data from only five selected thermal infrared channels: Method extension and accuracy assessment

Land surface temperature (LST) is an essential input for modeling the processes of energy exchange and balance of the earth's surface. Thermal infrared (TIR) remote sensing is considered to be the most efficient way to obtain accurate LST, both regionally and globally. Currently, many LST retrieval algorithms have been developed, including the up-to-date SW-TES (SW: split window; TES: temperature-emissivity separation) method, which is claimed to be able to accurately derive LST without the need for atmospheric information and land surface emissivity (LSE) based on the selected multiple TIR channel configuration. However, this hybrid method is actually not applicable to observations with large viewing angles and was only preliminarily evaluated in Australia. In this study, this method was extended for application to global TIR measurements with different viewing angles. Additionally, the performance of this extended SW-TES method was assessed globally for different seasons by using the MODIS LST product as a reference, and was also validated using in-situ LST measurements from the SURFRAD (SURFace RADiation budget network) sites. The results showed that the LST retrievals using the extended SW-TES method were comparable to the MODIS LST product, with discrepancies of <2.7 K and < 1.8 K for global daytime and nighttime observations, respectively. Validations based on the SURFRAD in-situ LST measurements indicated that the extended method could be used to retrieve LST accurately with a root-mean-square error (RMSE) of approximately 3.6 K during the daytime and 2.4 K during the nighttime. However, special attention should be paid when applying the extended method to daytime observations on grasslands and shrublands during hot seasons, considering the relatively large discrepancy when using this method compared with that obtained with the MODIS LST product (>4.0 K). Overall, in this study, the SW-TES method was extended, and the performance was comprehensively evaluated at the global scale, which may help in facilitating its potential applications.

Effects of Mosaic Representation of Land Use/Land Cover on Skin Temperature and Energy Fluxes in Noah‐MP Land Surface Model Over China

AbstractThe representations of land use/land cover (LULC) play an important role in land surface models (LSMs) for the simulation of the energy flux partition, soil moisture redistribution, and runoff generation. This study was designed to investigate the regional effects of mosaic LULC representations on skin temperature (Ts) and energy fluxes over China at three horizontal resolutions and how these effects changed with climate regimes, using Noah with multiparameterization (Noah‐MP) LSM. The current officially released Noah‐MP only considered the most abundant LULC type within one model grid. In this study, the mosaic method considering all the LULC types existing in one model grid was implemented into Noah‐MP. Against the reference data (including MODIS land surface temperature products, FLUXCOM energy flux data and Numerical Terra dynamic Simulation Group evapotranspiration data), the mosaic method generally performed better than the default method and reduced the root‐mean‐squared‐error of Ts and energy fluxes significantly over urban region. The mosaic method affected the Ts and energy fluxes by changing leaf area index and soil moisture, mainly by the former. The warm (monthly mean air temperature larger than 10 °C) and relatively humid climate (annual total precipitation larger than 200 mm) could enlarge the effect of mosaic method on Ts and energy fluxes. The mosaic method reduced discrepancies of Ts and energy fluxes among three horizontal resolutions (0.0625°, 0.25°, and 0.50°), especially over the heterogeneous vegetated and urban region.

Beyond intensity of urban heat island effect: A continental scale analysis on land surface temperature in major Chinese cities

Intensity of urban heat island (UHII) is a key indicator to quantify the magnitude of UHI effect. Change in UHII, however, can be affected by change in urban or rural temperature, or both. Numerous studies have investigated the change of UHII, but how it relates to change in urban and rural temperature is poorly understood. Here we present a continental scale analysis on the change in UHII from 2000 to 2015 in 31 major cities in China, and further explored whether such change was caused by urban warming. We used time series analysis based on MODIS land surface temperature product (MOD11A2) in summer. We found: (1) A majority of the cities (25 out of 31) had an increasing trend in UHII from 2000 to 2015, among which 44% were significant, suggesting a significantly intensified UHI effects. (2) Intensified UHII does not necessarily mean deterioration of urban thermal environment, and vice versa. For the 25 cities with an increasing trend in UHII, 11 cities had a decreasing trend of LST in both urban and rural areas. Meanwhile, for the 6 cities with a decreasing trend in UHII, three had warming trend in both urban and rural areas. (3) The attribution of changes in UHII to that of urban and rural LST indicated that it is necessary to quantify the change of LST in both urban and rural areas to fully quantify the impact of urbanization on UHI effect.

Reconstruction of the Daily MODIS Land Surface Temperature Product Using the Two-Step Improved Similar Pixels Method

The MODIS land surface temperature (LST) product is one of the most widely used data sources to study the climate and energy-water cycle at a global scale. However, the large number of invalid values caused by cloud cover limits the wide application of the MODIS LST. In this study, a two-step improved similar pixels (TISP) method was proposed for cloudy sky LST reconstruction. The TISP method was validated using a temperature-based method over various land cover types. The ground measurements were collected at fifteen stations from 2013 to 2018 during the Heihe Watershed Allied Telemetry Experimental Research (HiWATER) field campaign in China. The estimated theoretical clear-sky temperature indicates that clouds cool the land surface during the daytime and warm it at nighttime. For bare land, the surface temperature shows a clear seasonal trend and very similar across stations, with a cooling amplitude of 4.14 K in the daytime and a warming amplitude of 3.99 K at nighttime, as a yearly average. The validation result showed that the reconstructed LST is highly consistent with in situ measurements and comparable with MODIS LST validation accuracy, with a mean bias of 0.15 K at night (−0.43 K in the day), mean RMSE of 2.91 K at night (4.41 K in the day), and mean R2 of 0.93 at night (0.90 in the day). The developed method maximizes the potential of obtaining quality MODIS LST retrievals, ancillary data, and in situ observations, and the results show high accuracy for most land cover types.

Comparing different space-borne sensors and methods for the retrieval of land surface temperature

The importance of land surface temperature (LST) is increasingly recognized, and various methodologies have been proposed for the retrieval of LST using space-borne thermal infrared data. However, the selection of LST retrieval from Thermal Infrared Sensor (TIRS) of Landsat-8 based on different methods and the readily available MODIS LST products is still a challenging topic for local and global environmental studies. In this study, the potential of three different methods for retrieving LST using Landsat-8 TIRS data, including Radiative Transfer Equation (RTE), Single Channel (SC), and Split Window (SW) method in comparison with MODIS MOD11A1 LST product was evaluated. For accuracy assessment, 0 cm ground surface temperature (LSTGST) data was used. Our results almost showed same accuracy for RTEB10 with RMSE = 0.35 °C, followed by MODIS with RMSE = 0.36 °C, and SCB10 with RMSE = 0.38 °C. Secondly, SCmean (Mean of B10 and B11), and RTEmean (Mean of B10 and B11) generate nearly the same accuracy with RMSE = 0.53 °C, and RMSE = 0.54 °C, respectively. The other methods viz., SCB11, RTEB11, and SW method slightly showed lower accuracy with RMSE = 0.87 °C, RMSE = 0.88 °C, and RMSE = 0.91 °C, respectively. We found all the methods highly accurate and can be used successfully by climatologists, environmentalists, hydrologists, and urban planners concerning planning, and monitoring of the ever-increasing LST at local and global scale studies.

Spring Frost Damage to Tea Plants Can Be Identified with Daily Minimum Air Temperatures Estimated by MODIS Land Surface Temperature Products

Tea (Camellia sinensis) is one of the most dominant economic plants in China and plays an important role in agricultural economic benefits. Spring tea is the most popular drink due to Chinese drinking habits. Although the global temperature is generally warming, spring frost damage (SFD) to tea plants still occurs from time to time, and severely restricts the production and quality of spring tea. Therefore, monitoring and evaluating the impact of SFD to tea plants in a timely and precise manner is a significant and urgent task for scientists and tea producers in China. The region designated as the Middle and Lower Reaches of the Yangtze River (MLRYR) in China is a major tea plantation area producing small tea leaves and low shrubs. This region was selected to study SFD to tea plants using meteorological observations and remotely sensed products. Comparative analysis between minimum air temperature (Tmin) and two MODIS nighttime land surface temperature (LST) products at six pixel-window scales was used to determine the best suitable product and spatial scale. Results showed that the LST nighttime product derived from MYD11A1 data at the 3 × 3 pixel window resolution was the best proxy for daily minimum air temperature. A Tmin estimation model was established using this dataset and digital elevation model (DEM) data, employing the standard lapse rate of air temperature with elevation. Model validation with 145,210 ground-based Tmin observations showed that the accuracy of estimated Tmin was acceptable with a relatively high coefficient of determination (R2 = 0.841), low root mean square error (RMSE = 2.15 °C) and mean absolute error (MAE = 1.66 °C), and reasonable normalized RMSE (NRMSE = 25.4%) and Nash–Sutcliffe model efficiency (EF = 0.12), with significantly improved consistency of LST and Tmin estimation. Based on the Tmin estimation model, three major cooling episodes recorded in the "Yearbook of Meteorological Disasters in China" in spring 2006 were accurately identified, and several highlighted regions in the first two cooling episodes were also precisely captured. This study confirmed that estimating Tmin based on MYD11A1 nighttime products and DEM is a useful method for monitoring and evaluating SFD to tea plants in the MLRYR. Furthermore, this method precisely identified the spatial characteristics and distribution of SFD and will therefore be helpful for taking effective preventative measures to mitigate the economic losses resulting from frost damage.

Validation of non-linear split window algorithm for land surface temperature estimation using Sentinel-3 satellite imagery: Case study; Tehran Province, Iran

In recent years, land surface temperature (LST) has become critical in environmental studies and earth science. Remote sensing technology enables spatiotemporal monitoring of this parameter on large scales. This parameter can be estimated by satellite images with at least one thermal band. Sentinel-3 SLSTR data provide LST products with a spatial resolution of 1 km. In this research, direct and indirect validation procedures were employed to evaluate the Sentinel-3 SLSTR LST products over the study area in different seasons from 2018 to 2019. The validation method was based on the absolute (direct) evaluation of this product with field data and comparison (indirect) evaluation with the MODIS LST product and the estimated LST using the non-linear split-window (NSW) algorithm. Also, two emissivity estimation methods, (1) NDVI thresholding method (NDVI-THM) and (2) classification-based emissivity method (CBEM), were used to estimate the LST using the NSW method according to the two thermal bands of Sentinel-3 images. Then, the accuracy of these methods in estimating LST was evaluated using field data and temporal changes of vegetation, which the NDVI-THM method generated better results. For indirect evaluation between the Sentinel-3 LST product, MODIS LST product, and LST estimated using NSW, four filters based on spatial and temporal separates between pairs of pixels and pixel quality were used to ensure the accuracy and consistency of the compared pairs of a pixel. In general, the accuracy results of the LST products of MODIS and Sentinel-3, and LST estimated using NSW showed a similar trend for LST changes during the seasons. With respect to the two absolute and comparative validations for the Sentinel-3 LST products, summer with the highest values of bias (−1.24 K), standard deviation (StDv = 2.66 K), and RMSE (2.43 K), and winter with the lowest ones (bias of 0.14 K, StDv of 1.13 K, and RMSE of 1.12 K) provided the worst and best results for the seasons in the period of 2018–2019, respectively. According to both absolute and comparative evaluation results, the Sentinel-3 SLSTR LST products provided reliable results for all seasons on a large temporal and spatial scale over our studied area.

Spatio-temporal Variation of Water Heat Flux Using MODIS Land Surface Temperature Product over Hulun Lake, China During 2001–2018

Heat flux is important for studying interactions between atmosphere and lake. The heat exchange between air-water interfaces is one of the important ways to govern the temperature of the water surface. Heat exchange between the air-water interfaces and the surrounding environment is completed by solar radiation, conduction, and evaporation, and all these processes mainly occur at the air-water interface. Hulun Lake was the biggest lake which is also an important link and an indispensable part of the water cycle in Northeast China. This study mapped surface energy budget to better understand spatial and temporal variations in Hulun Lake in China from 2001 to 2018. Descriptive statistics were computed to build a historical time series of mean monthly heat flux at daytime and nighttime from June to September during 2001–2018. Remote sensing estimation methods we used was suitable for Hulun Lake (R2 = 0.81). At month scale, shortwave radiation and latent heat flux were decrease from June to September. However, the maximum sensible heat flux appeared in September. Net longwave radiation was the largest in August. The effective heat budget showed that Hulun Lake gained heat in the frost-free season with highest value in June (686.31 W/m2), and then steadily decreased to September (439.76 W/m2). At annual scale, net longwave radiation, sensible heat flux and latent heat flux all show significant growth trend from 2001 to 2018 (P < 0.01). Wind speed had the well correlation on sensible heat flux and latent heat flux. Water surface temperature showed the highest coefficient in sensitivity analysis.

The spatial and temporal impact of agricultural crop residual burning on local land surface temperature in three provinces across China from 2015 to 2017

China has suffered from severe crop residue burning (CRB) for a long time. As a type of biomass burning, CRB leads to a huge alteration in climate due to the emission of greenhouse gases and particulates in the atmosphere and damages to surface characteristics on land. At present, a growing body of research focuses on the impact of biomass burning (BB) (e.g., forest fire, grass fire, and CRB) on climate change from the aspect of atmospheric process. Meanwhile, a small number of research studies have started to pay attention on the damage caused by BB (e.g. forest fire) on land surface and consequent changes in the land surface temperature (LST). However, at present there is no study concerning the effect of CRB on the surface temperature. Considering its large incidence, highly seasonal concentration, and large spatial scale in China, this study attempted to reveal the impact of CRB on LST. Specifically, we identified the influence of CRB on the LST in surrounding areas based on MODIS Thermal Anomalies/Fire product and MODIS LST product during the CRB season for three provinces of China: Heilongjiang, Hebei, and Guangxi from 2015 to 2017. The results showed that there was a strong positive correlation between daily CRB spots and daily LST (R between 0.30 and 0.61) in Heilongjiang (20,184–24,902 spots of CRB from 2015 to 2017). On the other hand, in Hebei and Guangxi provinces, where the total CRB number was less (2,367–2,754 spots of CRB from 2015 to 2017 in Hebei, and 701 to 653 spots of CRB from 2015 to 2017 in Guangxi), even if R was only 0.36–0.53 and 0.11–0.53, respectively, the peaks of CRB spots and peaks of daily LST were highly matched. Furthermore, the spatial analysis showed that LST on agricultural land in 10–15 km distance from the center of CRB was higher (1–3 °C) than that in other regions in Heilongjiang province. On the other hand, the influence scale of CRB in Hebei and Guangxi was only 2–4 km with a ∼2 °C increase on LST. Finally, according to the typical case analysis, it was found that the influence of CRB on LST existed for 1–3 days and did not disappear immediately. The study proved the impact of CRB on LST in surrounding areas, and favors action on climate change relief through CRB control.

Effects of Different Urbanization Levels on Land Surface Temperature Change: Taking Tokyo and Shanghai for Example

The influence of different urbanization levels on land surface temperature (LST) has attracted extensive attention. Though both are world megacities, Shanghai and Tokyo have gone through different urbanization processes that urban sprawl characterized by impervious surfaces was more notable in Shanghai than in Tokyo over the past years. Here, annual and seasonal mean LST in daytime (LSTday), in nighttime (LSTnight), and LSTdiff (annual and seasonal mean difference of LST in daytime and nighttime) were extracted from the MODIS LST product, MYD11A2 006, for 9 typical sites in Shanghai and Tokyo from 2003 to 2018, respectively. Then the effects of the urbanization levels were analyzed through Mann-Kendall statistics and Sen’s slope estimator. The trends of change in LSTday and LSTdiff for most sites in Shanghai, an urbanizing region, rose. In addition, there was no obvious regularity when considering seasonal factors, which could be due to the increasing fragmentized landscapes and scattered water bodies produced by urbanization. By comparison, the change in LST in Tokyo, a post-urbanizing region, was regular, especially in the spring. In other seasons, there was no obvious trend in temperature change regardless of whether the land cover was impervious surface or mountain forest. On the whole, vegetation cover and water bodies can mitigate the urban heat island (UHI) effect in urban regions. For more scientific urban planning, further analysis about the effect of urbanization on LST should focus on the compound stress from climate change and urbanization.

Monitoring of water surface temperature of Eurasian large lakes usingMODISland surface temperature product

AbstractIn this study, data from MODIS land surface temperature product level 3 (MOD11A2) were used to investigate the spatiotemporal variation of Eurasian lakes water surface temperature (LSWT) from 2001 to 2015, and to examine the most influencing factors of that variation. The temperature of most lakes in the dry climate zone and in the equatorial climatic zone varied from 17 to 31°C and from 23 to 27°C, respectively. LSWTs in the warm temperate and cold climatic zones were in the range of 20 to 27°C and −0.6 and 17°C, respectively. The average day time LSWT in the polar climate zone was −0.71°C in the summer. Lakes in high latitude and in the Tibetan Plateau displayed low LSWT, ranging from −11 to 26°C during the night time. Large spatial variations of diurnal temperature difference (DTD) were observed in lakes across Eurasia. However, variations in DTDs were small in lakes located in high latitude and in tropical rainforest regions. The shallow lakes showed a rapid response of LSWT to solar and atmospheric forcing, while in the large and deep lakes, that response was sluggish. Results of this study demonstrated the applicability of remote sensing and MODIS LST products to capture the spatial–temporal variability of LSWT across continental scales, in particular for the vast wilderness areas and protected environment in high latitude regions of the world. The approach can be used in future studies examining processes and factors controlling large scale variability of LSWT.

Modeling Diurnal Land Surface Temperature on a Local Scale of an Arid Environment Using Artificial Neural Network (ANN) and Time Series of Landsat-8 Derived Spectral Indexes

This study aims to model diurnal Land Surface Temperature (LST) on a local scale of an arid environment by utilizing the Artificial Neural Network (ANN) and time series analysis of Landsat-8 satellite imageries. An arid region containing an in-situ LST station (DRA) located in Nevada, United States, was chosen as a test site. 78 Landsat-8 satellite imageries covering the test site were utilized to calculate spectral indexes. Since the spectral indexes represent the surface of Earth as land cover indexes, they can be used as indicators that affect the LST. The relationship between ten spectral indexes and in-situ LST were investigated, and the highly correlated indexes were determined as Built-up Area Extraction Index (BAEI) and Normalized Difference Bareness Index (NDBaI). The BAEI and NDBaI showed −0.80 and −0.94 correlation coefficients (r), respectively, with in-situ LST. Those two indexes and meteorological data, namely relative humidity (RH) and air temperature (AT), were used as inputs in the ANN model. A multi-layer perceptron (MLP) feed-forward network was considered in this study. The ANN model presented highly accurate results in the training and testing process with Root Mean Square Error (RMSE) values 0.74 K and 2.54 K, respectively. After learning and testing processes, the weights and biases were extracted to form the mathematical equation of the ANN model, and the equation was utilized to map LST for three data sets which were acquired during the winter and summer times and were not utilized in the ANN model. The results showed that the LST difference was lower than 1 K with regard to wintertime LST images. However, the LST difference was 2.49 K in the summertime. To test the spatial variability of ANN-based LST, MODIS LST products were considered and ANN-based LST was resampled to 1 km, the same resolution as the MODIS data, for comparison. As a result of the comparison, the highest mean LST difference (for all pixels in the scene) between ANN-based and MODIS LST was calculated as −1.1 K. Although the proposed method tended to underestimate LST as it increased, the obtained results showed that the ANN method would be a powerful tool for predicting and modeling the diurnal LST in an arid environment.

Characterizing spatial patterns of pine bark beetle outbreaks during the dry and rainy season’s in Honduras with the aid of geographic information systems and remote sensing data

Coniferous and mixed forests cover approximately 42% of Honduras forested areas, however; pine bark beetle (PBB) (Dendroctonus spp.) outbreaks are an environmental hazard that has caused incalculable ecological and economic impacts in Honduras. In this research, in order to plan more focalized measures for controlling the PBB outbreaks, it is essential to identify those areas that have a high susceptibility to a PBB outbreak during the dry and rainy seasons. For this purpose, we require to associate the historical PBB outbreak points (2017–2019), with a series of environmental and anthropogenic variables that according to the literature review have affected the initiation and the spread of PBB outbreaks. To assess the current climatic variables we used MODIS land surface temperature product (LST-MOD11A2), wind speed, precipitation, and temperature acquired from the WorldClim data. To assess vegetation vigor, we use the Normalized drought moisture index estimated from the MODIS surface reflectance product (MOD09A3). To give us an understanding of the density of the forest, we compare two products, the MODIS Leaf Area Index (LAI-MOD15A2H), and the MODIS Vegetation Continuous Fields (VFC-MOD44B). We included elevation, aspect, and slope as variables and acquired this data from the Global multiresolution terrain elevation data (GMTED-2010). Finally, we used geographical information systems data to derive proximity to different types of roads as anthropogenic data and wildfire density. We do a preliminary analysis of the variables and eliminated those which show the least importance. Furthermore, we integrated the most relevant variables identified with the PBB points by using the Random Forest (RF) algorithm to fit the model and then predicted the current PBB outbreak susceptibility for the dry and rainy season. Results indicated that climatic variables weigh heavily in determining high susceptibility areas. Our prediction results show high and very high susceptibility in the North-eastern and Central parts of the country especially. The results acquired, can lead to improved preventive and control measures to reduce the negative ecological effects that are caused by PBB outbreaks.

Large-scale rain-fed to paddy farmland conversion modified land-surface thermal properties in Cold China

The recent acute evolution of cropland structure in Cold China could lead to rapid rice paddy expansion, potentially altering land-surface thermal processes and influencing climate. To address the issue, this study investigated the changes in cropland type, land-surface temperature (LST) and heat fluxes in the agricultural region of Cold China during 2000–2015 based on time-series of land-use data and MODIS LST product, using the split-window algorithms (SWA) model and the pixel component arranging and component algorithm (PCACA). The investigation revealed large-scale land transformation from rain-fed farmland to paddy field in Cold China during 2000–2015. Compared to the rain-fed farmland, lower LST was observed in paddy field throughout crop growing seasons, with the highest LST threshold found in June (7.17 ± 1.05 °C) and the lowest value found in August (1.04 ± 0.35 °C). The cooling effect of paddy-field ranged from 0.59 ± 0.06 °C, 0.77 ± 0.07 °C, and 1.08 ± 0.08 °C for the low-, medium-, and high-density paddies, respectively. Compared to other months, stronger cooling effect was found in May and June. Further analysis showed the conversion of a rain-fed farmland to paddy field reduced the sensible heat flux and soil heat flux by 52.94 W/m2 and 15.26 W/m2, respectively, while increased the latent heat flux and net radiation by 115.66 W/m2 and 47.34 W/m2, respectively. The findings from this study indicated the changes in cropland structure and management regime (e.g., irrigation) could profoundly modify land-surface thermal processes and local/regional climate, interfering the signals from global warming. Therefore, instrumental climate data that collected from areas experienced large-scale conversion between rain-fed and paddy farmland should be carefully screened and corrected to prevent land-use induced biases.

GENERATING TIME SERIES OF MEDIUM-RESOLUTION TEMPERATURE VEGETATION DROUGHT INDEX IMAGES USING A KALMAN FILTER METHOD FOR SOIL MOISTURE CHANGE ANALYSIS

Abstract. Soil moisture is one of key environmental variables that affect vegetation cover and energy exchange between the land surface and the atmosphere. Satellite remote sensing technology can provide information for monitoring large-scale soil moisture dynamics quickly. The temperature vegetation dryness index (TVDI) acts as an effective indicator of inferring soil moisture status which is calculated according to the empirical parameterization of composed of the land surface temperature (LST) and the normalized difference vegetation index (NDVI) characteristic space. In this paper, the MODIS TVDI was calculated based on MODIS LST product (MOD11A2, 1 km) and NDVI data (derived from MOD09A1, 500m). Meanwhile, LST and NDVI from Landsat8 OLI images were estimated to obtain Landsat-based TVDI. Then, a Kalman filter algorithm was used to simulate TVDI time series data with 30m resolution and a revisit period of 8 days combining TVDI derived from Landsat and MODIS data. We selected the west of the Songnen Plain, China as the test area and high quality cloudy-free images during growing season (April to October) of 2018 as the input data. The predicted TVDI time series data of medium resolution not only improved the temporal resolution to capture the changes at fine scale within a short period, but also made up for the deficiency of low spatial resolution MODIS data. The results show that it is feasible to generate medium or high resolution TVDI time series data by applying different remote sensing data by Kalman filtering algorithm.

An Efficient Framework for Producing Landsat-Based Land Surface Temperature Data Using Google Earth Engine

A long time-series land surface temperature (LST) product is useful for ecological and environmental studies. However, current LST products cannot provide a global coverage at a fine spatial resolution (∼100 m) over a long period (>30 years). Landsat series satellites that have been launched since 1972 provide a unique opportunity to fill the gap. Here, we proposed a single-channel framework for producing global long time-series Landsat LST retrievals on a Google earth engine (GEE) cloud computing platform. This framework unifies the LST, land surface emissivity (LSE) and atmospheric water vapor (AWV) estimation algorithms, as well as the emissivity and atmospheric input data for the Landsat LST retrievals from the entire Landsat thermal infrared image archive. In situ LST measurements and the MODIS LST products were employed to evaluate Landsat LST retrievals using the proposed framework over land and water surfaces, respectively. In total, 1317 clear-sky LST samples were collected from the Landsat 5–8 series after spatiotemporal registration with seven sites, and the average bias and root-mean-square error (RMSE) were 0.33 and 2.01 K, respectively. Intercomparison between Landsat and MODIS LST retrievals based on 100 clear-sky scenes over 12 inland lakes showed an average bias of 0.17 K and RMSE of 1.11 K. We conclude that the proposed single-channel framework can produce Landsat LST with high accuracy following a simple yet robust way. Implementation of the single-channel method on GEE shows promise in providing the community with freely accessible and global long time-series (>30 years) LST data.

Assessment of Urban Heat Risk in Mountain Environments: A Case Study of Chongqing Metropolitan Area, China

For urban climatic environments, the urban heat island (UHI) effect resulting from land use and land cover change (LUCC) caused by human activities is rapidly becoming one of the most notable characteristics of urban climate change due to urban expansion. UHI effects have become a significant barrier to the process of urbanization and sustainable development of the urban ecological environment. Predicting the spatial and temporal patterns of the urban heat environment from the spatial relationship between land use and land surface temperature (LST) is key to predicting urban heat environment risk. This study established an Urban Heat Environment Risk Model (UHERM) as follows. First, the urban LST was normalized and classified during three different periods. Second, a Markov model was constructed based on spatio-temporal change in the urban heat environment between the initial year (2005) and middle year (2010), and then a cellular automata (CA) model was used to reveal spatial relationships between the urban heat environments of the two periods and land use in the initial year. The spatio-temporal pattern in a future year (2015) was predicted and the accuracy of the simulation was verified. Finally, the spatio-temporal pattern of urban heat environment risk was quantitatively forecasted based on the decision rule for the urban heat environment risk considering both the present and future status of the spatial characteristics of the urban heat environment. The MODIS LST product and LUCC dataset retrieved from remote sensing images were used to verify the accuracy of UHERM and to forecast the spatio-temporal pattern of urban heat environment risk during the period of 2015–2020. The results showed that the risk of urban heat environment is increasing in the Chongqing metropolitan area. This method for quantitatively evaluating the spatio-temporal pattern of urban heat environment risk could guide sustainable growth and provide effective theoretical and technical support for the regulation of urban spatial structure to minimize urban heat environment risk.