Satellite-derived Land Research Articles

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.

Spatio-temporal soil drying in southeastern South America: the importance of effective sampling frequency and observational errors on drydown time scale estimates

ABSTRACT The study of the spatio-temporal dynamics of surface soil moisture (SSM) drydowns integrates the soil response to climatic conditions, drainage and land cover and is key to advances in our knowledge of the soil–atmosphere water exchange. SSM drydowns have also been employed to compare soil moisture spatio-temporal behaviour between different data sources such as satellite-derived data and land–surface models, difficult to compare with standard methodologies. However, the errors introduced by satellite effective sampling frequencies (SF) and by different methodologies employed to define a drydown period have until now not been properly addressed in the literature. Here, SSM from microwave remote sensing products operating at L, C and X frequency bands are analysed together with SSM from a land–surface model in southeastern South America during 2010–2014, at seasonal and annual scales. We use an SSM-based drydown detection methodology and an exponential model to estimate the drydown time scale. The errors generated by the SF and by using SSM instead of precipitation to define the start of the drydown period are examined using a synthetic soil moisture model. Most of the products can detect the negative correlation between aridity conditions and drydown time scales (faster soil drying in the semiarid west and slower – and noisier – towards the wetter east). The Soil Moisture Ocean Salinity (SMOS) L-band product reproduces the smoothest drydown time scale spatial patterns at the annual and seasonal scales and displays large seasonal contrasts, although its error due to SF is the highest among the three products. The Organizing Carbon Hydrology In Dynamic Ecosystems (ORCHIDEE) land–surface model resampled by the SF of each product shows better agreement with SMOS, followed by the X-band product. The agreement is higher over the southern Pampas Plains, a region with high coverage of satellite-derived data and flat topography. SSM observational errors generate higher relative uncertainties for drydown time scales longer than 8 days, while the SF is more relevant for shorter drydowns. Also, the SF has a larger impact than soil depth, particularly in the dry season, when sparse temporal coverage misses short drydowns. Soil texture influence is captured by SMOS and ORCHIDEE, revealing slower drydowns for finer textures at the annual scale. Our results show that the soil drying behaviour is comparable between microwave remote sensing products and a land–surface model and that the observational errors and the SF are important sources of uncertainty to consider when interpreting drydown results.

Empirical Bayes small area prediction under a zero-inflated lognormal model with correlated random area effects.

Many variables of interest in agricultural or economical surveys have skewed distributions and can equal zero. Our data are measures of sheet and rill erosion called Revised Universal Soil Loss Equation - 2 (RUSLE2). Small area estimates of mean RUSLE2 erosion are of interest. We use a zero-inflated lognormal mixed effects model for small area estimation. The model combines a unit-level lognormal model for the positive RUSLE2 responses with a unit-level logistic mixed effects model for the binary indicator that the response is nonzero. In the Conservation Effects Assessment Project (CEAP) data, counties with a higher probability of nonzero responses also tend to have a higher mean among the positive RUSLE2 values. We capture this property of the data through an assumption that the pair of random effects for a county are correlated. We develop empirical Bayes (EB) small area predictors and a bootstrap estimator of the mean squared error (MSE). In simulations, the proposed predictor is superior to simpler alternatives. We then apply the method to construct EB predictors of mean RUSLE2 erosion for South Dakota counties. To obtain auxiliary variables for the population of cropland in South Dakota, we integrate a satellite-derived land cover map with a geographic database of soil properties. We provide an R Shiny application called viscover (available at https://lyux.shinyapps.io/viscover/) to visualize the overlay operations required to construct the covariates. On the basis of bootstrap estimates of the mean square error, we conclude that the EB predictors of mean RUSLE2 erosion are superior to direct estimators.

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.

Observed earlier start of the growing season from middle to high latitudes across the Northern Hemisphere snow-covered landmass for the period 2001–2014

Vegetation phenology in spring has received much attention for its importance to terrestrial ecosystem carbon exchange and climate–biosphere interactions studies. Through control on surface water and heat balance, snow cover largely impacts on spring vegetation phenology. However, under the background of global warming and rapid reduction of spring snow cover extent across the Northern Hemisphere (NH), the responses of spring vegetation phenology have not been well documented. Using two satellite-derived land cover dynamic datasets and 420 in situ vegetation phenology observations from five filed datasets, this study evaluated the accuracy of satellite-derived vegetation phenology datasets and explored the changes of start of the growing season (SOS) across the NH snow-covered landmass for the period 2001–2014. Compared with MEaSUREs VIPPHEN, the MODIS SOS maps displayed higher accuracy in capture the real SOS climatology by validating with in situ observations (R2 = 0.67, bias = −3.99 d). Moreover, evidences from MODIS SOS maps pointed out that the SOS advanced by approximately 2.36 d in NH middle to high latitudes (43.5°N–70.0°N), but delayed by about 0.53 d in lower latitudes (33.0°N–43.5°N) from 2001 to 2014. The contrast SOS anomalies across the NH snow-covered landmass were further proved by changes in spring NDVI derived from GIMMS in the corresponding period. In addition, the observed changes in SOS were consistent with the spatiotemporal pattern of spring snow end date found in previous studies, indicating vegetation phenology changes should be taken into account in estimating the impacts of snow in climate–biosphere interactions studies.

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.

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.

Responses of Vegetation Productivity to Temperature Trends Over Continental United States From MODIS Imagery

Vegetation growth and productivity may record signals of global climate change since vegetation is sensitive to modifications in climatic variables, such as temperature and precipitation. Significant changes in vegetation growth and productivity can affect the exchange of energy, water vapor, and momentum between the land surface and atmosphere. Despite consensus on the vegetation growth enhancement by the warming of temperature, little attention has been given to the understanding of the impacts of cooling temperature trends on vegetation growth and productivity. Based on satellite-derived land surface temperatures and vegetation indices, this study shows that alternating temperature trends are more reasonable than a monotonic temperature trend for the study period from 2003 to 2016. More importantly, results reveal that both warming and cooling temperature trends can have a positive impact on vegetation growth over the continental United States. These findings are of importance to understand global carbon cycle sinks and sources accurately in different regions.

A Bayesian Kriging Regression Method to Estimate Air Temperature Using Remote Sensing Data

Surface air temperature (Ta) is an important physical quantity, usually measured at ground weather station networks. Measured Ta data is inadequate to characterize the complex spatial patterns of Ta field due to low density and unevenness of the networks. Remote sensing can provide satellite imagery with large scale spatial coverage and fine resolution. Estimating spatially continuous Ta by integrating ground measurements and satellite data is an active research area. A variety of methods have been proposed and applied in this area. However, the existing studies primarily focused on daily Ta and failed to quantify uncertainties in model parameter and estimated results. In this paper, a Bayesian Kriging regression (BKR) method is proposed to model and estimate monthly Ta using satellite-derived land surface temperature (LST) as the only input. The BKR is a spatial statistical model with the capacity to quantify uncertainties via Bayesian inference. The BKR method was applied to estimate monthly maximum air temperature (Tmax) and minimum air temperature (Tmin) over the conterminous United States in 2015. An exploratory analysis shows a strong relationship between LST and Ta at the monthly scale, indicating LST has the great potential to estimate monthly Ta. 10-fold cross-validation approach was adopted to compare the predictive performance of the BKR method with the linear regression method over the whole region and the urban areas of the contiguous United States. For the whole region, the results show that the BKR method achieves a competitively better performance with averaged RMSE values 1 . 23 K for Tmax and 1 . 20 K for Tmin, which are also lower than previous studies on estimation of monthly Ta. In the urban areas, the cross-validation demonstrates similar results with averaged RMSE values 1 . 21 K for Tmax and 1 . 27 K for Tmin. Posterior samples for model parameters and estimated Ta were obtained and used to analyze uncertainties in the model parameters and estimated Ta. The BKR method provides a promising way to estimate Ta with competitively predictive performance and to quantify model uncertainties at the same time.

Balancing prediction accuracy and generalization ability: A hybrid framework for modelling the annual dynamics of satellite-derived land surface temperatures

Annual temperature cycle (ATC) models enable the multi-timescale analysis of land surface temperature (LST) dynamics and are therefore valuable for various applications. However, the currently available ATC models focus either on prediction accuracy or on generalization ability and a flexible ATC modelling framework for different numbers of thermal observations is lacking. Here, we propose a hybrid ATC model (ATCH) that considers both prediction accuracy and generalization ability; our approach combines multiple harmonics with a linear function of LST-related factors, including surface air temperature (SAT), NDVI, albedo, soil moisture, and relative humidity. Based on the proposed ATCH, various parameter-reduction approaches (PRAs) are designed to provide model derivatives which can be adapted to different scenarios. Using Terra/MODIS daily LST products as evaluation data, the ATCH is compared with the original sinusoidal ATC model (termed the ATCO) and its variants, and with two frequently-used gap-filling methods (Regression Kriging Interpolation (RKI) and the Remotely Sensed DAily land Surface Temperature reconstruction (RSDAST)), under clear-sky conditions. In addition, under overcast conditions, the LSTs generated by ATCH are directly compared with in-situ LST measurements. The comparisons demonstrate that the ATCH increases the prediction accuracy and the overall RMSE is reduced by 1.8 and 0.7 K when compared with the ATCO during daytime and nighttime, respectively. Moreover, the ATCH shows better generalization ability than the RKI and behaves better than the RSDAST when the LST gap size is spatially large and/or temporally long. By employing LST-related controls (e.g., the SAT and relative humidity) under overcast conditions, the ATCH can better predict the LSTs under clouds than approaches that only adopt clear-sky information as model inputs. Further attribution analysis implies that incorporating a sinusoidal function (ASF), the SAT, NDVI, and other LST-related factors, provides respective contributions of around 16%, 40%, 15%, and 30% to the improved accuracy. Our analysis is potentially useful for designing PRAs for various practical needs, by reducing the smallest contribution factor each time. We conclude that the ATCH is valuable for further improving the quality of LST products and can potentially enhance the time series analysis of land surfaces and other applications.

Estimating cholera risk from an exploratory analysis of its association with satellite-derived land surface temperatures

ABSTRACTOccurrence and growth of Vibrio cholerae, the causative agent of cholera, is linked to modalities of elevated temperatures and heavy precipitation. Previous studies have employed temperature- and satellite-derived precipitation data to determine the risk of cholera, but predictions were limited because of the coarse spatial resolution of temperature data (about 50 km). Cholera estimation has a severe impact on those in vulnerable regions with marginal civil infrastructure and those suffering additional damage after a natural disaster. In this study, a new remote-sensing data-based algorithm is proposed that includes a pathway to associate coarse-resolution cholera prediction with high-resolution land surface temperature (LST) dataset. The algorithm allows identification and prediction of regions with elevated risk of cholera at least four weeks in advance. Additionally, it employs a hierarchical structure comprising long-term anomalous LST values to determine hot spots of potential Vibrio cholerae. The algorithm was tested in five cholera epidemic regions of Sub-Saharan Africa (Mozambique, Central African Republic, Cameroon, South Sudan, and Rwanda), with realistic accuracy in demarcating regions where human cholera cases had been reported.