Wenfeng Zhan

Modelling the diurnal variations of urban heat islands with multi-source satellite data

Examination of the diurnal variations in surface urban heat islands (UHIs) has been hindered by incompatible spatial and temporal resolutions of satellite data. In this study, a diurnal temperature cycle genetic algorithm (DTC-GA) approach was used to generate the hourly 1 km land-surface temperature (LST) by integrating multi-source satellite data. Diurnal variations of the UHI in ‘ideal’ weather conditions in the city of Beijing were examined. Results show that the DTC-GA approach was applicable for generating the hourly 1 km LSTs. In the summer diurnal cycle, the city experienced a weak UHI effect in the early morning and a significant UHI effect from morning to night. In the diurnal cycles of the other seasons, the city showed transitions between a significant UHI effect and weak UHI or urban heat sink effects. In all diurnal cycles, daytime UHIs varied significantly but night-time UHIs were stable. Heating/cooling rates, surface energy balance, and local land use and land cover contributed to the diurnal variations in UHI. Partial analysis shows that diurnal temperature range had the most significant influence on UHI, while strong negative correlations were found between UHI signature and urban and rural differences in the normalized difference vegetation index, albedo, and normalized difference water index. Different contributions of surface characteristics suggest that various strategies should be used to mitigate the UHI effect in different seasons.

Sharpening Thermal Imageries: A Generalized Theoretical Framework From an Assimilation Perspective

Land surface temperature (LST) plays an important role in many fields. However, thermal bands in prevailing sensors that are onboard satellites have limited spatial resolutions, which seriously impede their potential applications. Many approaches that aim to downscale thermal imageries to finer spatial resolution levels have been developed in recent years. This paper managed to construct a Generalized Theoretical Framework from an Assimilation Perspective for them with semiempirical regression and modulation integration techniques. Based on three hierarchical sharpening levels, which include digital number, radiance, and surface temperature, many of them can be brought into such a unified framework as derivatives. Two typical land cover patterns were chosen as case study areas to evaluate the capabilities of various kernels to represent the LST distribution. The results demonstrate that there are great discrepancies among those kernels. The single-band kernels are dependent on different land cover types, while the band-derivative kernels perform better in most circumstances when portraying the LST variations. In addition, the simulated imageries that were resampled by scaling up the original thermal bands with an aggregation technique were utilized to validate a localization approach of temperature vegetation dryness index (TVDI). The results indicate that the TVDI has satisfactory effects when depicting slight LST variations due to soil anomalies. More intercomparisons between the approach presented here and other different methods, including artificial neural network and Gram-Schmidt techniques, were made thoroughly, coupling with the Moderate Resolution Imaging Spectroradiometer and Advanced Spaceborne Thermal Emission Reflection Radiometer data. Consequently, the generalized framework opens up the foreground for sharpening thermal images with high efficiency over a solid theoretical foundation.

Maximum Nighttime Urban Heat Island (UHI) Intensity Simulation by Integrating Remotely Sensed Data and Meteorological Observations

Remote sensing of the urban heat island (UHI) effect has been conducted largely through simple correlation and regression between the UHIs spatial variations and surface characteristics. Few studies have examined the surface UHI from a temporal perspective and related it with climatic and meteorological factors. By selecting the city of Beijing, China, as the study area, the purpose of this research was to evaluate the applicability and feasibility of the support vector machine (SVM) technique to model the daily maximum nighttime UHI intensity (MNUHII) based on integration of MODIS land products and meteorological observations. First, a Gaussian surface model was used to calculate the citys MNUHIIs. Then, SVM regression models were developed to predict the MNUHII from the following variables: the normalized difference vegetation index (NDVI), surface albedo, atmospheric aerosol optical depth (AOD), relative humidity (RH), sunshine hour (SH), and precipitation (PREP). Results demonstrate that the accuracy of the SVM regression in predicting the MNUHII was around 0.8°C to 1.3°C; in addition, the SVM regression outperformed the multiple linear regression and the artificial neural network with backpropagation. A scenario analysis indicates that the relationships between the MNUHII and its influencing factors varied with time and season and were impacted by previous precipitation. The RH and AOD were the most important factors that influenced the MNUHII. In addition, previous precipitation could significantly mitigate the MNUHII. The results suggest that future investigations on the surface UHI effect should consider the climatic and meteorological conditions in addition to the surface characteristics.

Intercomparison of methods for estimating land surface temperature from a Landsat-5 TM image in an arid region with low water vapour in the atmosphere

This study compares the methods for retrieving the land surface temperature (LST) (T s) from Landsat-5 TM (Thematic Mapper) data, including the radiative transfer equation (RTE) method, the mono-window algorithm (MWA) and the generalized single-channel (GSC) method in an arid region with low atmospheric water vapour content. In addition, T s calculated without atmospheric correction of TM band 6 is also assessed. The intercomparison is divided into two parts. The first part is applying the methods at the Biandukou site (100° 58′ E, 38° 16′ N, elevation = 2690 m) and the second part is applying them at Binggou (100° 13′ E, 38° 42′ N, elevation = 3400 m) and Arou (100° 27′ E, 38° 36′ N, elevation = 2960 m) sites. Results demonstrate that these methods provide acceptable accuracies at the Biandukou site. At this site, GSC generates nearly the same accuracy as RTE; MWA estimations are slightly less accurate than RTE and GSC; estimations without atmospheric correction of TM band 6 exhibit the largest errors. On the other hand, MWA is a good choice for retrieving the LST at Binggou and Arou sites. In cases where the meteorological parameters are unavailable, it is an alternative option to calculate T s directly from TM band 6 image without atmospheric correction at these two sites.

Disaggregation of Remotely Sensed Land Surface Temperature: A Generalized Paradigm

The environmental monitoring of earth surfaces requires land surface temperatures (LSTs) with high temporal and spatial resolutions. The disaggregation of LST (DLST) is an effective technique to obtain high-quality LSTs by incorporating two subbranches, including thermal sharpening (TSP) and temperature unmixing (TUM). Although great progress has been made on DLST, the further practice requires an in-depth theoretical paradigm designed to generalize DLST and then to guide future research before proceeding further. We thus proposed a generalized paradigm for DLST through a conceptual framework (C-Frame) and a theoretical framework (T-Frame). This was accomplished through a Euclidean paradigm starting from three basic laws summarized from previous DLST methods: the Bayesian theorem, Toblers first law of geography, and surface energy balance. The C-Frame included a physical explanation of DLST, and the T-Frame was created by construing a series of assumptions from the three basic laws. Two concrete examples were provided to show the advantage of this generalization. We further derived the linear instance of this paradigm based on which two classical DLST methods were analyzed. This study finally discussed the implications of this paradigm to closely related topics in remote sensing. This paradigm develops processes to improve an understanding of DLST, and it could be used for guiding the design of future DLST methods.

Time series decomposition of remotely sensed land surface temperature and investigation of trends and seasonal variations in surface urban heat islands

Previous time series methods have difficulties in simultaneous characterization of seasonal, gradual, and abrupt changes of remotely sensed land surface temperature (LST). This study proposed a model to decompose LST time series into trend, seasonal, and noise components. The trend component indicates long-term climate change and land development and is described as a piecewise linear function with iterative breakpoint detection. The seasonal component illustrates annual insolation variations and is modeled as a sinusoidal function on the detrended data. This model is able to separate the seasonal variation in LST from the long-term (including gradual and abrupt) change. Model application to nighttime Moderate Resolution Imaging Spectroradiometer (MODIS)/LST time series during 2000-2012 over Beijing yielded an overall root-mean-square error of 1.62K between the combination of the decomposed trend and seasonal components and the actual MODIS/LSTs. LST decreased (similar to -0.086K/yr, p<0.1) in 53% of the study area, whereas it increased with breakpoints in 2009 (similar to 0.084K/yr before and similar to 0.245K/yr after 2009) between the fifth and sixth ring roads. The decreasing trend was stronger over croplands than over urban lands (p<0.05), resulting in an increasing trend in surface urban heat island intensity (SUHII, 0.0220.006K/yr). This was mainly attributed to the trends in urban-rural differences in rainfall and albedo. The SUHII demonstrated a concave seasonal variation primarily due to the seasonal variations of urban-rural differences in temperature cooling rate (related to canyon structure, vegetation, and soil moisture) and surface heat dissipation (affected by humidity and wind).

Land Surface Temperature Retrieval from MODIS Data by Integrating Regression Models and the Genetic Algorithm in an Arid Region

Abstract: The land surface temperature (LST) is one of the most important parameters of surface-atmosphere interactions. Methods for retrieving LSTs from satellite remote sensing data are beneficial for modeling hydrological, ecological, agricultural and meteorological processes on Earth’s surface. Many split-window (SW) algorithms, which can be applied to satellite sensors with two adjacent thermal channels located in the atmospheric window between 10 μm and 12 μm, require auxiliary atmospheric parameters (e.g., water vapor content). In this research, the Heihe River basin, which is one of the most arid regions in China, is selected as the study area. The Moderate-resolution Imaging Spectroradiometer (MODIS) is selected as a test case. The Global Data Assimilation System (GDAS) atmospheric profiles of the study area are used to generate the training dataset through radiative transfer simulation. Significant correlations between the atmospheric upwelling radiance in MODIS channel 31 and the other three atmospheric parameters, including the transmittance in channel 31 and the transmittance and upwelling radiance in channel 32, are trained based on the simulation dataset and formulated with three regression models. Next, the genetic algorithm is used to estimate the LST. Validations of the RM-GA method are based on the simulation dataset generated from

Satellite-derived subsurface urban heat island.

The subsurface urban heat island (SubUHI) is one part of the overall UHI specifying the relative warmth of urban ground temperatures against the rural background. To combat the challenge on measuring extensive underground temperatures with in situ instruments, we utilized satellite-based moderate-resolution imaging spectroradiometer data to reconstruct the subsurface thermal field over the Beijing metropolis through a three-time-scale model. The results show the SubUHIs high spatial heterogeneity. Within the depths shallower than 0.5 m, the SubUHI dominates along the depth profiles and analyses imply the moments for the SubUHI intensity reaching first and second extremes during a diurnal temperature cycle are delayed about 3.25 and 1.97 h per 0.1 m, respectively. At depths shallower than 0.05 m in particular, there is a subsurface urban cool island (UCI) in spring daytime, mainly owing to the surface UCI that occurs in this period. At depths between 0.5 and 10 m, the time for the SubUHI intensity getting to its extremes during an annual temperature cycle is lagged 26.2 days per meter. Within these depths, the SubUHI prevails without exception, with an average intensity of 4.3 K, varying from 3.2 to 5.3 K.

An Algorithm for Separating Soil and Vegetation Temperatures With Sensors Featuring a Single Thermal Channel

Soil and vegetation temperature separation (SVTS) is a crucial process in various fields, such as the study of evapotranspiration. An operational and novel algorithm for separating soil and vegetation temperatures from sensors that feature a single thermal channel was developed to analyze high-heterogeneity croplands. The a priori knowledge on interrelationships among neighboring pixels was coupled to both the radiation transfer equation and the conceptual thermal anisotropic model to increase the solvability of forward anisotropic models through the Bayesian theorem. Model sensitivity analysis suggests that component fractions and reference temperatures are the two main factors that control the accuracies of the inversion results. Some validation options, which include the air temperature data from local weather stations, computer simulations, up-scaling techniques, and intercomparisons among different approaches, were selected as indirect techniques in verifying the inverted results. These results demonstrated that the proposed inversion technique reached an acceptable level of accuracy and stability, which highlights the practicalities of monowindow thermal sensors in the SVTS.

Comparing surface-and canopy-layer urban heat islands over Beijing using MODIS data

The canopy-layer urban heat island (CLHI) and the surface-layer urban heat island (SLHI) of Beijing, the capital city of China, were compared on the spatial scale of a city and the temporal scale of a year in this study. A differential temperature vegetation index (DTVX) method was improved by suggesting a new parameterization scheme for estimating daytime air temperature (Ta); a binary linear regression equation was developed for estimating night-time Ta from Moderate Resolution Imaging Spectroradiometer (MODIS) land-surface temperature (Ts) and vegetation indices data during 2009–2010. Validations using weather station observations show that the spatially distributed Ta can be obtained with an accuracy of approximately 2 K. Comparisons between the CLHI and the SLHI indicate that the CLHI agrees well with the SLHI during night-time, but they have a greater difference during daytime either in heat island intensity or in spatial distribution pattern. The SLHI−CLHI intensity difference during daytime has a noticeable seasonal variation, which is small and negative in cold seasons, but large and positive in warm seasons, whereas that at night-time has no significant seasonal variations. The difference in the evapotranspiration cooling effects between urban and rural areas may be the predominant factor that drives the SLHI−CLHI difference.