Satellite-derived Land Surface Research Articles

A global increase in tree cover extends the growing season length as observed from satellite records

Plant phenology provides information on the seasonal dynamics of plants, and changes herein are important for understanding the impact of climate change and human management on the biosphere. Land surface phenology is the study of plant phenology across large spatial scales estimated by satellite observations. However, satellite observations (pixels) are often composed of a mixture of vegetation types, like woody vegetation and herbaceous vegetation, having different phenological characteristics. Therefore, any changes in tree cover presumably impact land surface phenology, as trees usually have a different seasonal cycle compared to herbaceous vegetation. On the other hand, changes in land surface phenology are often interpreted as a result of climate change-induced impacts on the photosynthetic activity of vegetation. Therefore, it is important to better understand the role of changes in vegetation cover (here, the proportion between tree and short vegetation cover) in satellite-derived land surface phenology analysis. We studied the impact of changes in tree cover on satellite observed land surface phenology at a global scale over the past three decades. We found an extension of the growing season length in 36.6% of the areas where tree cover increased, whereas only 20.1% of the areas where tree cover decreased showed an increase in growing season length. Furthermore, the ratio between tree cover and short vegetation cover was found to affect changes in the length of the growing season, with the denser tree cover showing a more pronounced extension of the growing season length (especially in boreal forests). These results highlight the importance of changes in tree cover when analyzing the impact of climate change on vegetation phenology. Our study thereby addresses a critical knowledge gap for an improved understanding of changes in land surface phenology during recent decades in the context of climate and human-induced global land cover change.

Modelling spatiotemporal variations of the canopy layer urban heat island in Beijing at the neighbourhood scale

Abstract. Information on the spatiotemporal characteristics of Beijing's urban–rural near-surface air temperature difference, known as the canopy layer urban heat island (UHI), is important for future urban climate management strategies. This paper investigates the variation of near-surface air temperatures within Beijing at a neighbourhood-scale resolution (∼ 100 m) during winter 2016 and summer 2017. We perform simulations using the urban climate component of the ADMS-Urban model with land surface parameters derived from both local climate zone classifications and OpenStreetMap land use information. Through sensitivity simulations, the relative impacts of surface properties and anthropogenic heat emissions on the temporal variation of Beijing's UHI are quantified. Measured UHI intensities between central Beijing (Institute of Atmospheric Physics) and a rural site (Pinggu) during the Atmospheric Pollution and Human Health in a Chinese Megacity (APHH-China) campaigns, peak during the evening at ∼ 4.5 ∘C in both seasons. In winter, the nocturnal UHI is dominated by anthropogenic heat emissions but is underestimated by the model. Higher-resolution anthropogenic heat emissions may capture the effects of local sources (e.g. residential buildings and adjacent major roads). In summer, evening UHI intensities are underestimated, especially during heatwaves. The inability to fully replicate the prolonged release of heat stored in the urban fabric may explain this. Observed negative daytime UHI intensities in summer are more successfully captured when surface moisture levels in central Beijing are increased. However, the spatial correlation between simulated air temperatures and satellite-derived land surface temperatures is stronger with a lower urban moisture scenario. This result suggests that near-surface air temperatures at the urban meteorological site are likely influenced by fine-scale green spaces that are unresolved by the available land cover data and demonstrates the expected differences between surface and air temperatures related to canopy layer advection. This study lays the foundations for future studies of heat-related health risks and UHI mitigation strategies across Beijing and other megacities.

An Improved Cloud Gap-Filling Method for Longwave Infrared Land Surface Temperatures through Introducing Passive Microwave Techniques

Satellite-derived land surface temperature (LST) data are most commonly observed in the longwave infrared (LWIR) spectral region. However, such data suffer frequent gaps in coverage caused by cloud cover. Filling these ‘cloud gaps’ usually relies on statistical re-constructions using proximal clear sky LST pixels, whilst this is often a poor surrogate for shadowed LSTs insulated under cloud. Another solution is to rely on passive microwave (PM) LST data that are largely unimpeded by cloud cover impacts, the quality of which, however, is limited by the very coarse spatial resolution typical of PM signals. Here, we combine aspects of these two approaches to fill cloud gaps in the LWIR-derived LST record, using Kenya (East Africa) as our study area. The proposed “cloud gap-filling” approach increases the coverage of daily Aqua MODIS LST data over Kenya from <50% to >90%. Evaluations were made against the in situ and SEVIRI-derived LST data respectively, revealing root mean square errors (RMSEs) of 2.6 K and 3.6 K for the proposed method by mid-day, compared with RMSEs of 4.3 K and 6.7 K for the conventional proximal-pixel-based statistical re-construction method. We also find that such accuracy improvements become increasingly apparent when the total cloud cover residence time increases in the morning-to-noon time frame. At mid-night, cloud gap-filling performance is also better for the proposed method, though the RMSE improvement is far smaller (<0.3 K) than in the mid-day period. The results indicate that our proposed two-step cloud gap-filling method can improve upon performances achieved by conventional methods for cloud gap-filling and has the potential to be scaled up to provide data at continental or global scales as it does not rely on locality-specific knowledge or datasets.

A Method for Downscaling Satellite Soil Moisture Based on Land Surface Temperature and Net Surface Shortwave Radiation

Due to the coarse spatial resolution of currently available microwave (mostly passive) soil moisture (SM) products, it is difficult to apply these SM data in watersheds or at local scales. To this end, a number of downscaling approaches have been developed to improve the spatial resolution of microwave SM products. Specifically, the optical-/thermal-based downscaling methods are most widely used in recent decades. However, such methods normally rely on instantaneous optical/thermal land surface parameters, which are commonly inapplicable under cloudy conditions. The purpose of this study is to develop a new downscaling method based on the temporal variation in geostationary satellite-derived land surface temperature and net surface shortwave radiation. The proposed method has a certain potential to improve data availability under cloudy conditions, because geostationary satellites are capable of providing land surface parameters at high temporal resolution. The proposed method was tested over the REMEDHUS network in Spain. The scaling strategy of cumulative distribution function matching was used to remove systematic differences in spatial mismatch between satellite pixels and <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">in situ</i> SM measurements. Results indicate that the downscaled SM agrees well with <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">in situ</i> measurements and has comparable accuracy with the original microwave SM product. The overall root mean square errors with the <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">in situ</i> measurements for the original microwave SM and the downscaled SM are 0.054 and 0.057 <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\text{m}^{3}/\text{m}^{3}$ </tex-math></inline-formula> , respectively. This method not only has a successful attempt to downscale microwave SM data using temporal information but also has the potential to avoid the failure of traditional instantaneous observations-based downscaling procedure due to clouds.

Multisensor Thermal Infrared and Microwave Land Surface Temperature Algorithm Intercomparison

To ensure optimal and consistent algorithm usage within climate studies utilizing satellite-derived Land Surface Temperature (LST) datasets, an algorithm intercomparison exercise was undertaken to assess the various operational and scientific LST retrieval algorithms in use. This study was focused on several LST products including single-sensor products for AATSR, Terra-MODIS, SEVIRI, SSM/I and SSMIS; a Climate Date Record (CDR), which is a combined dataset drawing from AATSR, SLSTR and MODIS; and finally a merged low Earth orbit/geostationary product using data from AATSR, MODIS and SEVIRI. Therefore, the analysis included 14 algorithms: seven thermal infrared algorithms and seven microwave algorithms. The thermal infrared algorithms include five split-window coefficient-based algorithms, one optimal estimation algorithm and one single-channel inversion algorithm, with the microwave focusing on linear regression and neural network methods. The algorithm intercomparison assessed the performance of the retrieval algorithms for all sensors using a benchmark database. This approach was chosen due to the lack of sufficient in situ validation sites globally and the bias this limited set engendered on the training of particular algorithms. A simulated approach has the ability to test all parameters in a consistent, fair manner at a global scale. The benchmark database was constructed from European Centre for Medium-Range Weather Forecasts Re-analysis 5 (ERA5) atmospheric data, Combined ASTER and MODIS Emissivity for Land (CAMEL) infrared emissivity data, and Tool to Estimate Land Surface Emissivities at Microwave frequencies (TELSEM) emissivity data for the period of 2013–2015. The best-performing algorithms had biases of under 0.2 K and standard deviations of approximately 0.7 K. These results were consistent across multiple sensors. Areas of improvement, such as coefficient banding, were found for all algorithms as well as lines for further inquiry that could improve the global and regional performance.

Development of a 250-m Downscaled Land Surface Temperature Data Set and its Application to Improving Remotely Sensed Evapotranspiration Over Large Landscapes in Northern China

Satellite-derived land surface temperature (LST) is critical for retrieving terrestrial evapotranspiration (ET); however, its availability is limited by low spatial resolution and inclement weather conditions. This study develops a spatiotemporal regression strategy that can downscale 1-km Moderate Resolution Imaging Spectroradiometer (MODIS) LST product to 250-m resolution and simultaneously gap-fill the missing values. The proposed methodology synergistically uses random forest (RF) model and geographically weighted regression, which are, respectively, available for demonstrating the nonlinear correlation between LST and explanatory variables and for calibrating the RF-derived residuals. The study is conducted across a region of ~1.49 million square kilometers in northern China. The coupled model creates a 250-m spatial resolution LST product with the root-mean-square error (RMSE) of 2.32 and 1.87 K when compared with field observations and reference Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) LST, respectively. Meanwhile, it minimizes the constraint of LST availability due to inclement weather conditions with RMSE of 2.69 and 2.31 K relative to field observations and reference images, respectively. The results further reveal that remote-sensing-derived ET using the 250-m downscaled LST data is fairly accurate with the relative errors of 6%–9% as evaluated with flux measurements. The 250-m modeled ET retrievals exhibit a more intense hydrological response to the water use conditions compared with the 1-km remotely sensed ETs and Noah land surface model ETs. This study may benefit land surface hydrology research and water resource management.

An optimal sampling method for multi-temporal land surface temperature validation over heterogeneous surfaces

The development of ground-based sampling strategies is vital for validating medium or coarse-resolution satellite-derived land surface temperature (LST) products. Conventional LST sampling at the satellite pixel scale has been limited to the homogeneous surfaces. In this study, an optimal sampling strategy called spatial and diurnal temperature cycle-constrained sampling (SDCS) is proposed to extend the feasibility of LST validation over heterogeneous surfaces with dramatic diurnal LST changes. SDCS integrates a priori information including land cover, diurnal LSTs data, and spatial distribution characteristics of samples to improve the representativeness of multi-temporal measurements over heterogeneous surfaces. SDCS was applied to four varied study areas using simulated satellite data and was compared with four existing methods including random sampling, systematic sampling, land cover-based stratified sampling, and conditioned Latin hypercube (CLH) sampling. Results showed that SDCS could significantly improve the representativeness of samples with a limited sample size. In homogeneous surfaces with an LST standard deviation (SD) of less than 2 K, the root mean square error (RMSE) of diurnal LSTs estimated by SDCS was less than 0.3 K when using 0.20% of the total pixels. In moderate heterogeneous surfaces (LST SD less than 5 K), 0.32% of the total pixels were required to achieve RMSE less than 0.5 K. In extremely heterogeneous surfaces (LST SD > 6 K), 0.96% of the pixels were needed to achieve the same accuracy. Further, the representativeness of the samples selected by SDCS was stable in diurnal space with uncertainties of LST bias less than 0.27 K. Moreover, the samples exhibited a dispersed spatial distribution with a nearest neighbor index of 1.27–1.54. SDCS can generalize to various regions and dates and can be employed in field campaigns for diurnal LST validation over heterogeneous surfaces.

SPATIAL DISAGGREGATION OF LANDSAT-DERIVED LAND SURFACE TEMPERATURE OVER A HETEROGENEOUS URBAN LANDSCAPE USING PLANETSCOPE IMAGE DERIVATIVES

Abstract. Satellite-derived land surface temperature (LST) is frequently utilized to characterize the intensity of urban heat island (UHI) effect in highly urbanized and rapidly urbanizing cities. However, current spaceborne thermal sensors cannot capture temperature variations within heterogeneous urban landscapes at finer scales due to its coarse spatial resolution. This study aims to apply Regression-Kriging (RK) method to downscale a 30-meter Landsat-derived LST to 3 meters using different PlanetScope image derivatives. To avoid multicollinearity, exploratory regression was performed to reduce the initial set of 16 indices to 7 explanatory variables, namely, Enhanced Vegetation Index (EVI), Modified Soil-Adjusted Vegetation Index (MSAVI), Normalized Pigment Chlorophyll Ratio Index (NPCRI), Visible Green-based Built-up Index (VgNIR-BI), Mean, Entropy, and Homogeneity. Ordinary Least Squares (OLS) regression was applied to fit the models and the residuals of the best performing models were interpolated using Ordinary Kriging technique and added back to the downscaled LST. The model with the highest accuracy was obtained using the combination of MSAVI, EVI, and Mean, with an R2 of 0.75 and RMSE of 1.12 °C, 0.58 °C, 0.80 °C, and 1.45 °C in estimating the LST of built-up, bare soil, vegetation, and water classes, respectively. The results indicate that the inclusion of textural features in the regression could improve model accuracy without increasing the variance of coefficient estimates. Moreover, RK method (RMSE = 1.10–1.16 °C) was proven to be a reliable downscaling technique because it redistributes the spatial variability of LST that were not preserved in the OLS regression (RMSE = 1.60–1.75 °C).

Improved estimates of monthly land surface temperature from MODIS using a diurnal temperature cycle (DTC) model

Satellite-derived land surface temperature (LST) has been widely used to understand surface energy exchange and surface radiation budget and is a critical input of earth system models for carbon and water cycles studies at regional to global scales. Spatially and temporally consistent thermal measurements with long historical records are in a high demand to understand the biophysical changes of Earth surfaces. The current monthly LST products’ spatial resolutions are relatively coarse and the temporal and spatial consistency could be interrupted by the presence of clouds and satellite orbit limitations. This study develops a diurnal temperature cycle (DTC) model-based approach from MODIS observations that are suitable for constructing the long-term and near-global coverage of monthly LSTs at 1 km to fill these data gaps. The new approach allows us to estimate temporally representative 24-hr mean and maximum temperatures in a diurnal cycle of each month. We performed an inter-comparison among satellite-based data, including DTC-based MODIS estimates, the simple composite of four MODIS overpasses, and the hourly geostationary observations (GEO), and also assessed the satellite-based LST against in-situ LSTs from FLUXNET globally. Our proposed DTC-based mean LSTs outperformed the other two satellite estimates from the simple composite and GEO, showing a mean difference of 0.3 °C and an RMSE of 2.2 °C relative to the in-situ measurements. Moreover, we illustrate an application that explores the relationship between the DTC-based monthly LSTs and the soil moisture anomalies across the United States. The monthly maximum LSTs estimated from DTC scheme show higher sensitivity to droughts than the monthly mean. In sum, the improved monthly LST dataset using DTC scheme can enhance our understanding of the thermal dynamics resulting from land–atmosphere interaction across the local, regional, and global scales.

Calculating the surface melt rate of Antarctic glaciers using satellite-derived temperatures and stream flows.

Melt rate models are fundamental for understanding the impacts of climate change on glaciers and the subsequent effects on habitats and sea level rise. Ice melt models have mostly been derived from energy balance or air temperature index calculations. This research demonstrates that satellite-derived land surface temperature (LST) measurements provide a simpler method for estimating surface melt rate that substitutes for energy balance models. Since these satellite images are continuous (distributed) across space, they do not need calibration for topography. Antarctic glacier melt discharge data from nearby stream gauges were used to calibrate an LST-derived melt model. The model calculations are simplified by the fact that groundwater flow is assumed to be minimal due to permafrost, and the glaciers are assumed to only melt on the surface. A new method called the Temperature Area Sum model is developed, which builds on an existing Temperature Area Index model. A daily melt rate model is developed using 77 Landsat 8 images and calculates the volume of meltwater produced per hectare for any given LST between - 7 and 0°C. A seasonal average daily melt rate model is also developed that uses 1660 MODIS images. The utility of the seasonal MODIS model is demonstrated by calculating melt rates, water flows and wetness across the entire Ross Sea Region. An unexpected large wet area to the southwest of the Ross Ice Shelf requires further investigation and demonstrates the usefulness of these models for large remote areas. Surface melt rate and wetness can now be calculated for different climate change scenarios.

Impacts of Land Cover/Use on the Urban Thermal Environment: A Comparative Study of 10 Megacities in China

Satellite-derived land surface temperature (LST) reveals the variations and impacts on the terrestrial thermal environment on a broad spatial scale. The drastic growth of urbanization-induced impervious surfaces and the urban population has generated a remarkably increasing influence on the urban thermal environment in China. This research was aimed to investigate land surface temperature (LST) intensity response to urban land cover/use by examining the thermal impact on urban settings in ten Chinese megacities (i.e., Beijing, Dongguan, Guangzhou, Hangzhou, Harbin, Nanjing, Shenyang, Suzhou, Tianjin, and Wuhan). Surface urban heat island (SUHI) footprints were scrutinized and compared by magnitude and extent. The causal mechanism among land cover composition (LCC), population, and SUHI was also identified. Spatial patterns of the thermal environments were identical to those of land cover/use. In addition, most impervious surface materials (greater than 81%) were labeled as heat sources, on the other hand, water and vegetation were functioned as heat sinks. More than 85% of heat budgets in Beijing and Guangzhou were generated from impervious surfaces. SUHI for all megacities showed spatially gradient decays between urban and surrounding rural areas; further, temperature peaks are not always dominant in the urban core, despite extremely dense impervious surfaces. The composition ratio of land cover (LCC%) negatively correlates with SUHI intensity (SUHII), whereas the population positively associates with SUHII. For all targeted megacities, land cover composition and population account for more than 63.9% of SUHI formation using geographically weighted regression. The findings can help optimize land cover/use to relieve pressure from rapid urbanization, maintain urban ecological balance, and meet the demands of sustainable urban growth.

Downscaling Land Surface Temperature Using Multiscale Geographically Weighted Regression Over Heterogeneous Landscapes in Wuhan, China

The deficiency of fine-resolution satellite-derived land surface temperature (LST) has impeded the meticulous exploration of the urban thermal environment at micro spatial scales. Although the LST downscaling methods are well-documented, the scale dependence between LST and environmental factors has been rarely considered in the regression establishment. This article proposes a new method (MGWRK) coupling multiscale geographically weighted regression (MGWR) and area-to-point kriging (ATPK) to generate fine-resolution LST maps over heterogeneous landscapes. Technically, normalized difference vegetation index (NDVI), normalized difference built-up index (NDBI), and albedo are selected using random forests (RF) as scale factors. First, the spatial and temporal robustness of MGWRK is evaluated by downscaling aggregated Landsat-8 Thermal Infrared Sensor images (1000-m) into finer resolutions of 500, 400, 300, 200, and 100-m in different seasons (spring, summer, and winter). Results reveal that MGWRK is superior to GWR and DisTrad methods for spatial details improvement (SSIM > 0.93) and LST fidelity ( ${\boldsymbol{R}^2}$ > 0.91) under all scales and in the seasons. Second, MGWRK is utilized to downscale the synchronous 1000-m Moderate Resolution Imaging Spectroradiometer (MODIS) LST map into 100-m. The quantitative and qualitative result substantiates that MGWRK obtains ideal performance when applied on MODIS data with few LST distortion ( ${\boldsymbol{R}^2}\; = \;0.915$ , RMSE = 1.607 K) and obvious spatial information enrichment (SSIM = 0.903).

Analysis of the impact of land cover on Surface Temperature Distribution: urban heat island studies i}n Medan and Makassar

The increasing number of an urban population in Indonesia causing Urban Heat Island (UHI) phenomenon that has to be the concern for the local government. Urban Heat Island is a phenomenon of the surface and thermal air climate that is warmer than the surrounding non-urbanized areas due to population density. This phenomenon is identified by the growing number of impervious surface area that can store heat energy better than any other surface area. A ground-based temperature monitoring system can observe this symptom but on a smaller scale. If we want to monitor the UHI in a larger spatial scale, we need to set up a much more ground-based temperature monitoring system, and that would cost too much money. Satellite-derived Land Surface Temperature (LST) may be the solution to this problem. In this study, the UHI phenomenon was examined in two cities in Indonesia: Medan and Makassar using Landsat 8 OLI/TIRS imagery. The Single Channel Algorithm (SCA) was used to retrieved LST data. Spectral indices such as Normalized Difference Vegetation Index (NDVI), Modified Normalized Difference Water Index (MNDWI) and Visible Red –Near Infrared Build up Index (VrNIR-BI) were used. The mean LST in Medan and Makassar was 30.46°C and 30.76°C respectively. The study revealed that mean LST in the vegetation area was the coolest among the impervious and the mixing area. The Pearson Product Moment Correlation to analyze the relationship between land cover that is represented with NDVI values and LST in those study area shows that they have negative relationship. The correlation coefficient between NDVI values and LST in Medan and Makassar was -0.65; and -0.63. While the correlation coefficient between VrNIR-BI values and LST in Medan and Makassar was 0,65 and 0,63 respectively.

Spatial-Temporal Variability of Land Surface Dry Anomalies in Climatic Aspect: Biogeophysical Insight by Meteosat Observations and SVAT Modeling

The spatial-temporal variability of drought occurrence over Bulgaria is characterized based on long-term records (2007–2018) of Meteosat information and the SVAT model-derived soil moisture availability index (referred to root zone depth, SMAI). Land surface temperature according to the satellite-derived Land Surface Analysis Satellite Application Facility Land Surface Temperature (LSASAF LST) product and SMAI were used to designate land surface state dry anomalies. The utility of LST for drought assessment is tested by statistical comparative analyses, applying two approaches, site-scale quantitative comparison, and evaluation of spatial-temporal consistency between SMAI and LST variability. Pearson correlation and regression modeling techniques were applied. The main results indicate for a synchronized behavior between SMAI and LST during dry spells, as follows: opposite mean seasonal course (March–October); high to strong negative monthly correlation for different microclimate regimes. Negative linear regressions between the anomalies of SMAI and LST (monthly mean), with a strong correlation in their spatial-temporal variability. Qualitative evaluation of spatial-temporal variability dynamics is analyzed using color maps. Drought-prone areas were identified on the bases of LST maps (monthly mean), and it is illustrated they are more vulnerable to vegetation burning as detected by the Meteosat FRP-PIXEL product. The current study provides an advanced framework for using LST retrievals based on IR satellite observations from the geostationary MSG satellite as an alternative tool to SMAI, whose calculation requires the input of many parameters that are not always available.

Heat-mortality vulnerability by individual air conditioning, residential housing, and comorbidity index among older adults in five Ohio counties, 2006-2013

PDS 66: Climate change, Exhibition Hall (PDS), Ground floor, August 28, 2019, 1:30 PM - 3:00 PM Background: Identifying characteristics of vulnerability to high temperatures will aid climate change adaptation efforts. Previous research is mixed on air conditioning (AC) effects on heat-associated health outcomes. Most prior studies lacked individual-level AC ownership and housing information. We assessed how housing types, central AC ownership, demographics, and comorbidities modify associations between extreme heat (EH, temperature > 99th percentile) and natural-cause mortality among individuals 65 and older in five Ohio counties. Methods: Ohio Department of Health mortality data were merged with housing data from Cuyahoga, Lucas, Franklin, Hamilton, and Summit County tax assessors by residential address. We defined four non-AC and one AC housing type. Lag day 0-1 average temperature was modeled from 1-km-resolution satellite-derived land surface temperature and airport air temperature. Quintiles of the modified Charleson Comorbidity Index were calculated from Centers for Medicare and Medicaid Services Chronic Conditions data on fee-for-service beneficiaries and merged with the mortality and weather data by birth and death dates and ZIP code. We used a case-crossover design, with controls within the same half-month as the case. We interacted EH with housing type (combinations of AC, basement, and stories) or central AC ownership, marital status, black race, and comorbidity index. Results: For EH vs. non-EH, mortality risk varied significantly between counties, and was increased 44% (95% CI: 17%, 77%) in Cuyahoga County among residents without AC only. Aside from AC status, associations did not vary significantly within housing types. In Cuyahoga County, in homes without AC, risks were strongest among non-married blacks with at least one comorbidity (97% increased risk, 95% CI: 91%, 200%), but risks did not increase monotonically with comorbidity number. Conclusions: Risk of heat-associated mortality was increased among certain individuals without central AC, depending on county and personal health and demographic characteristics. Interventions should focus on households with these characteristics.

A Review of Protocols for Fiducial Reference Measurements of Downwelling Irradiance for the Validation of Satellite Remote Sensing Data over Water

This paper reviews the state of the art of protocols for the measurement of downwelling irradiance in the context of Fiducial Reference Measurements (FRM) of water reflectance for satellite validation. The measurement of water reflectance requires the measurement of water-leaving radiance and downwelling irradiance just above water. For the latter, there are four generic families of method, using: (1) an above-water upward-pointing irradiance sensor; (2) an above-water downward-pointing radiance sensor and a reflective plaque; (3) a Sun-pointing radiance sensor (sunphotometer); or (4) an underwater upward-pointing irradiance sensor deployed at different depths. Each method—except for the fourth, which is considered obsolete for the measurement of above-water downwelling irradiance—is described generically in the FRM context with reference to the measurement equation, documented implementations, and the intra-method diversity of deployment platform and practice. Ideal measurement conditions are stated, practical recommendations are provided on best practice, and guidelines for estimating the measurement uncertainty are provided for each protocol-related component of the measurement uncertainty budget. The state of the art for the measurement of downwelling irradiance is summarized, future perspectives are outlined, and key debates such as the use of reflectance plaques with calibrated or uncalibrated radiometers are presented. This review is based on the practice and studies of the aquatic optics community and the validation of water reflectance, but is also relevant to land radiation monitoring and the validation of satellite-derived land surface reflectance.

Monitoring spatial pattern of land surface thermal characteristics and urban growth: A case study of King Williams using remote sensing and GIS

The world is currently experiencing unprecedented urban growth, industrialization, and the perceived higher standard of living that is often associated with access to better infrastructure. “Surface Heat Island (SHI) is a phenomenon where urban areas experience higher surface temperatures compared to the surrounding rural areas. The presence of the SHI in urban areas in most cases has a negative impact not only on city dwellers, but also on the environment and the economy. This study aimed at evaluating SHI in King Williams Town by studying the relationship between land surface temperatures, land cover and land cover indices. The derived indices are the Normalized Difference Vegetation Index (NDVI) and Normalized Difference Built-up Index (NDBI), these indices were selected because they are representative of the land surface features.” This study was conducted for the King Williams Town (KWT) study area between the years 1995 and 2018, the land surface temperature was derived from Landsat ETM + high thermal band data. The findings from this study provided an idea on the correlation between satellite derived land surface temperature and the land modification, which occurred during the urbanization of King Williams Town during 23-year period between 1995 and 2018. The built-up land category proved to be the most influential or significant in the development of high land surface temperature levels, while vegetation had an opposite effect as a series of data sets illustrated that vegetated areas had a cooling effect on the surface. Water bodies in the study area had insignificant effect on the surface temperature levels while the grass lands were not as cooling as the vegetation but did provide for a cooling environment on the study area.

Estimation of Groundwater Temperatures in Paris, France

Subsurface temperature data is usually only accessible as point information with a very limited number of observations. To spatialize these isolated insights underground, we usually rely on interpolation methods. Unfortunately, these conventional tools are in many cases not suitable to be applied to areas with high local variability, like densely populated areas, and in addition are very vulnerable to uneven distributions of wells. Since thermal conditions of the surface and shallow subsurface are coupled, we can utilize this relationship to estimate shallow groundwater temperatures from satellite-derived land surface temperatures. Here, we propose an estimation approach that provides spatial groundwater temperature data and can be applied to natural, urban, and mixed environments. To achieve this, we combine land surface temperatures with anthropogenic and natural processes, such as downward heat transfer from buildings, insulation through snow coverage, and latent heat flux in the form of evapotranspiration. This is demonstrated for the city of Paris, where measurements from as early as 1977 reveal the existence of a substantial subsurface urban heat island (SUHI) with a maximum groundwater temperature anomaly of around 7 K. It is demonstrated that groundwater temperatures in Paris can be well predicted with a root mean squared error of below 1 K by means of satellite-derived land surface images. This combined approach is shown to improve existing estimation procedures that are focused either on rural or on urban conditions. While they do not detect local hotspots caused by small-scaled heat sources located underground (e.g., sewage systems and tunnels), the findings for the city of Paris for the estimation of large-scale thermal anomalies in the subsurface are promising. Thus, the new estimation procedure may also be suitable for other cities to obtain a more reliable insight into the spatial distribution of urban ground and groundwater temperatures.

Supplement of the radiance-based method to validate satellite-derived land surface temperature products over heterogeneous land surfaces

Land surface temperature (LST) retrieved from satellite remote sensing data has become a key parameter in research on global environmental change; therefore, the acquisition of accurate satellite-derived LST information is crucial for the diagnosis and analysis of global change. However, it is relatively difficult to obtain the true value of a pixel due to the scale mismatch between in situ measurements and satellite-based observations, especially for commonly heterogeneous and nonisothermal land areas, which greatly increases the difficulty in estimating pixel-representative LST values from in situ measurements for validation of satellite-based LST products. In this study, a supplemented radiance-based (SR-based) validation method was developed to evaluate the latest moderate resolution imaging spectroradiometer (MODIS) Collection 6 Level 2 daily LST/land surface emissivity (LSE) products over a heterogeneous and nonisothermal region of the Heihe Watershed Allied Telemetry Experimental Research (HiWATER) project, West China. In the SR-based framework, pixel-representative LST values are simulated by the MODTRAN model from the corresponding in situ measurements, such as LSE and atmospheric profile measurements, to evaluate the MODIS LST products. The validation results show that the MODIS daytime LST products from the Aqua satellite (MYD11_L2) have a greater accuracy than those from the Terra satellite (MOD11_L2). Analyses of the effect factors indicate a strong correlation between the errors in the MOD11_L2 LST product and the corresponding difference in the MODIS brightness temperature between bands 31 and 32. Although the requirement of synchronous or quasisynchronous in situ measurements for the validated LST products may limit the applicability of the SR-based method, it is still an effective and simple method for validating satellite-derived LST products over mixed pixels. Our method is an indispensable supplement for the validation methods of satellite-derived LST products, and it can be applied in West China and other areas with heterogeneous land surfaces.

A physical model-based method for retrieving urban land surface temperatures under cloudy conditions

Satellite-derived land surface temperature (LST), due to its synoptic coverage, has been widely used for understanding surface energy and carbon fluxes at local, regional, and global scales. Despite great achievements in developing practical algorithms to estimate LSTs from thermal infrared (TIR) data, the retrieval of LSTs for overcast conditions has received much less attention. Existing techniques, such as passive microwave (PMW) approaches, surface energy balance (SEB) models, and reconstruction algorithms relying on spatial/temporal information, for estimating LSTs under cloudy skies cannot fulfill the need for a spatially and temporally consistent LST dataset. Inspired by recent advancements in physically-based urban canopy models (UCMs), this study synergistically used the coupled Weather Research and Forecasting Model (WRF)/UCM system and the random forest (RF) regression technique (hereafter named as WRFF) to effectively estimate LSTs under cloudy conditions. Taking the Baltimore-Washington metropolitan region as a test site, the WRF/UCM simulations (LSTs) were performed from April 28 to May 20, 2011. The MODIS LST images of the same period were used to evaluate the effectiveness of the method introduced in this study. LSTs under cloudy conditions for a partially cloudy image were retrieved using the RF model calibrated by clear-sky pixels from the same image. For a fully cloud-contaminated image, clear-sky pixels from its temporally adjacent images were used to calibrate the RF model to estimate LSTs under cloudy conditions. Results showed that the modeling system could well capture diurnal air temperature variations but tended to underestimate temperature values. The correlation coefficient between MODIS LSTs and simulated LSTs exhibited a wide range from 0.5 to 0.9 with the RMSE (root mean square error) value varying from 1.0 to 9.0 K across different land covers. The utilization of the RF regression technique for estimating LSTs under cloudy conditions from a partially cloud-contaminated LST image greatly reduced the RMSE to ~1.8 K with an improved correlation coefficient. For fully cloud-contaminated LST images, LSTs were estimated with the correlation coefficient and RMSE values of ~0.75 and ~2.0 K, respectively. Overall, the WRFF method has potential to generate a consistent and reliable LST dataset for various applications such as quantification of urban heat island intensity and vulnerability analysis of humans to vector-borne diseases. Further research should be made to examine the impact of the temporal distance between the target image and its temporally adjacent images on the performance of the proposed method.