Yuhao Liu
Rice University
yuhao.liu@rice.edu&Pranavesh Panakkal
Rice University
pranavesh@rice.edu&Sylvia Dee
Rice University
sylvia.dee@rice.edu&Guha Balakrishnan
Rice University
guha@rice.edu&Jamie Padgett
Rice University
jamie.padgett@rice.edu&Ashok Veeraraghavan
Rice University
vashok@rice.eduCorresponding author
Abstract
Cloud occlusion is a common problem in the field of remote sensing, particularly for retrieving Land Surface Temperature (LST). Remote sensing thermal instruments onboard operational satellites are supposed to enable frequent and high-resolution observations over land; unfortunately, clouds adversely affect thermal signals by blocking outgoing longwave radiation emission from the Earth’s surface, interfering with the retrieved ground emission temperature. Such cloud contamination severely reduces the set of serviceable LST images for downstream applications, making it impractical to perform intricate time-series analysis of LST. In this paper, we introduce a novel method to remove cloud occlusions from Landsat 8 LST images. We call our method ISLAND, an acronym for Interpolating Land Surface Temperature using land cover. Our approach uses LST images from Landsat 8 (at resolution with 16-day revisit cycles) and the NLCD land cover dataset. Inspired by Tobler’s first law of Geography, ISLAND predicts occluded LST through a set of spatio-temporal filters that perform distance-weighted spatio-temporal interpolation. A critical feature of ISLAND is that the filters are land cover-class aware, making it particularly advantageous in complex urban settings with heterogeneous land cover types and distributions. Through qualitative and quantitative analysis, we show that ISLAND achieves robust reconstruction performance across a variety of cloud occlusion and surface land cover conditions, and with a high spatio-temporal resolution. We provide a public dataset of 20 U.S. cities with pre-computed ISLAND LST outputs. Using several case studies, we demonstrate that ISLAND opens the door to a multitude of high-impact urban and environmental applications across the continental United States.
Keywords cloud removal land surface temperature thermal imaging Landsat land cover
1 Introduction
Land surface temperature (LST) is a fundamental aspect of Earth’s climate system, it attracts extensive studies across diverse disciplines. The wide array of fields include climate change (Horton etal.,, 2016; Seneviratne etal.,, 2021), urban planning (Sobrino etal.,, 2013; Huang and Wang,, 2019; Osborne and Alvares-Sanches,, 2019), vegetation and land cover changes(Gomez-Martinez etal.,, 2021; Chun and Guldmann,, 2018), and human health (Orimoloye etal.,, 2018; Dee etal.,, 2022; Dugord etal.,, 2014). LST changes rapidly both in space and time due to the strong heterogeneity of land surface characteristics and the short timescales of weather. As a consequence, accurate characterization of LST requires dense spatial and temporal sampling (Li etal.,, 2013).
Growing recognition of the importance of accurate LST observations has drivenrapid advances in remote sensing(Li etal.,, 2013).Satellite-based thermal infrared (TIR) data provides LST measurements with high spatial and temporal resolution at a global scale.As an example, the National Aeronautics and Space Administration (NASA) Landsat8 satellite was launched in 2013, and its platform has provided detailed LSTdata over the last decade(USGS, 2023a, ; USGS, 2023b, ).The Landsat 8 and 9 satellites each include a Thermal Infrared Sensor (TIRS) onboard, which collects LST data at a spatial resolution of with a revisit cycle of 16 days.The high spatial resolution of TIRS has enabled numerous studies of LST overheterogeneous regions like urbancenters(Huang and Wang,, 2019; Sobrino etal.,, 2013; Streutker,, 2003)and wetlands(Eisavi etal.,, 2016; Demarquet etal.,, 2023).
Despite these advancements, cloud occlusion persists as a substantial obstacle to achieving reliable spaceborne retrievals of LST.Clouds adversely affect LST readings by blocking thermal radiation emitted from Earth’s surface.This results in cloud-contaminated pixels exhibiting considerably lower valuescompared to their true values, rendering the affected imagesunusable(Jin etal.,, 2019).Unfortunately, cloud contamination is a frequent phenomenon in Landsat images,with studies indicating that an average of 35% of Landsat images globallycontain missing data due to cloudcontamination(Roy etal.,, 2008).Moreover, most cities experience enhanced daytime cloud cover compared to theirsurrounding rural regions(Vo etal.,, 2023), making cloud-free LST imageseven harder to obtain for cities.Furthermore, regions at higher latitudes, such as Pacific Northwest cities likePortland and Seattle, are prone to rainier weather conditions,leading to more frequent cloud contaminations(NOAA,, 2020).Under these circ*mstances, obtaining a single cloud-free image may be infeasible for weeks or even months, severely limiting the utility of Landsat LST data.Collectively, these barriers have limited practical high spatial and temporal sampling of LST. Thus, the temporal dynamics of LST over complex, heterogeneous terrains remain under-observed, under-studied, and under-constrained, especially at scale.
In this paper, we present a novel method to mitigate the effects of cloud contamination in satellite LST images.Our method incorporates Tobler’s First Law of Geography (TFL), which statesthat “everything is related to everything else, but near things are morerelated than distant things”(Tobler,, 1970).In addition to the distance-decay effect from TFL, we also incorporate multi-temporal information and, crucially, integrate land cover data into our model.Land cover data contains information about physical land types, such as forests, open water, and urban/developed areas.While existing studies have demonstrated the strong relationship between landcover types andLST(Chaudhuri and Mishra,, 2016; Zhao etal.,, 2020; Imran etal.,, 2021), ourmodel represents the first attempt to utilize land cover to infer occluded LSTpixel values in satellite images.We call our model ISLAND, an acronym for Interpolating Land Surface Temperature using land cover.ISLAND performs interpolation using a set of spatio-temporal filters to capture surrounding pixel values and historical patterns.Notably, these filters are designed to be sensitive to land cover classes, enabling class-specific pattern capture and higher reconstruction accuracy.
We demonstrate that ISLAND provides robust LST reconstruction performance across various occlusion and land cover conditions.Our results indicate that ISLAND greatly improves the practical temporal resolution of Landsat LST images by robustly estimating cloud-contaminated LST pixels.Simulation evaluation shows that the RMSE in our reconstructed Landsat 8 LST images is around . In situ evaluation shows that our RMSE is approximately .We present three illustrative examples underscoring the utility of ISLAND, namely, (1) urban heat island effects, (2) derivation of surface temperature trends, and (3) social vulnerability and urban heat stress.
ISLAND leverages publicly available data from Landsat 8(USGS, 2023a, ) andNational Land Cover Database (NLCD)(Yang etal.,, 2018). This approach ensures the accessibility andtransferability of ISLAND, as it is open-source and can be easily deployed inany region within the continental United States (CONUS) as per userrequirements.All source code111https://github.com/Way-Yuhao/ISLAND is available to the public, accompanied by a dataset222https://doi.org/10.17603/ds2-3rf5-sd58 comprising 20 urban regions with pre-computed ISLAND LST outputs.We envision that ISLAND will provide tremendous operational value for a variety of applications in Earth, geospatial, and social sciences, and ultimately pave the way for a new generation of LST studies using remote sensing.
2 Related Work
The refinement of cloud-removing algorithms is an open area of research in the field of remote sensing.Several existing studies develop and discuss cloud removal algorithms for remotely acquired LST.Unfortunately, these algorithms generally target lower spatial resolutions andhom*ogeneous land cover types(Wu etal.,, 2021).For example,Yu etal., (2019) proposed a method to reconstruct ModerateResolution Imaging Spectroradiometer (MODIS) LST over cloudy pixels using landenergy balance theory and similar pixels at a spatial resolution of.Zeng etal., (2015) proposed a spatiotemporal technique to reconstruct MODIS LSTproducts using regression and multispectral ancillary data to classify pixels,which improves reconstruction accuracy.Unfortunately, MODIS LST at is too coarse to resolve urban infrastructures, such as buildings and roads.
There are existing studies that reconstruct cloudy-sky LST at the spatial resolution of Landsat 8 pixels ().Wang etal., (2019) proposed a method to reconstruct cloud-sky Landsat 8LST by considering solar-cloud-satellite geometry.However, this method requires a temporally adjacent clear-sky image as a reference, which is hard to obtain, limiting its robustness.Furthermore, the accuracy of their reconstructed Landsat LST was not validated against in situ LST measurements.Zhu etal., (2022) reconstruct Landsat 8 LST data using an annualtemperature cycle (ATC) model and adjacent spatial information from similarpixels.Zhu etal., (2022) validated their results against in situ LST measurementsat six Surface Radiation Budget Network (SURFRAD) sites(Augustine etal., 2000a, ).However, there are several limitations to their study.First, their reconstruction method may fail when the cloud cover percentage exceeds 70%, limiting the scope of their application.Additionally, the ATC model assumes a constant mean annual surface temperature per region, suggesting that their model likely underestimates LST under global warming and the increased prevalence of the urban heat island effect.Lastly, the primary limitation forZhu etal., (2022) and all existingstudies mentioned in this section is that these studies are mainly designed andvalidated on relatively hom*ogeneous regions of land cover, such as cropland,shrubland, and grassland; their results were not demonstrated over urbanregions.This study addresses this gap and provides a scalable method to generate high-resolution LST, even for highly urbanized regions with complex temperature feedback and heterogeneous urban land surface types.We achieve this by explicitly employing NLCD land cover labels and using the satellite LST product with the highest resolution (Landsat 8) to maximize its usability in cities.
Our model shares some of the high-level design principles with existing models, such as the use of multitemporal remote sensing data.To the best of our knowledge, ISLAND is the first algorithm that incorporates land cover labels as ancillary data for cloud removal in LST images.We summarize our key contributions as follows:(1) We use NLCD land cover labels to accurately reconstruct Landsat 8 LST under cloudy-sky conditions.(2) Using spatial adjacency and multi-temporal filters, ISLAND effectively removes cloud contaminations from Landsat 8 LST images even under severe occlusion, thereby dramatically improving the temporal resolution of Landsat 8 LST products.(3) Simulation evaluation shows that the RMSE in our reconstructed Landsat 8 LST images is around . In situ evaluation shows that our RMSE is approximately .(4) We release a public dataset of cloud-free, reconstructed LST maps for 20 US cities from 2019–2023.
3 Data
In this section, we explain the implementation of the data compilation steps of our model. See Fig.1 for a visual overview.
3.1 Landsat 8 Data
We collect Landsat 8 Collection 2 Level-2 LST products from Google EarthEngine(Gorelick etal.,, 2017).Landsat 8 provides LST products at resolution and a revisit cycle of 16 days.Landsat 8 collects top-of-atmosphere (TOA) spectral radiance values via its band 10 Thermal Infrared Sensor (TIRS).LST is derived from TOA spectral radiance using the radiative transfer equation(RTE)-based single-channel algorithm(Malakar etal.,, 2018).ASTER GED(Hulley etal.,, 2015) was used to correct the effect of surfaceemissivity.Atmospheric effects were compensated by the Goddard Earth Observing System, Version 5 (GEOS-5) reanalysis data using the radiative transfer model MODTRAN 5.2.The RMSE of Landsat LST products is approximately(Malakar etal.,, 2018).Please refer toMalakar etal., (2018) for a more detailed descriptionof the Landsat LST retrieval algorithm.We denote the input Landsat LST image as , where and denote the height and width of the LST image, respectively.
The Landsat LST product includes cloud mask information(CFMask)(Zhu and Woodco*ck,, 2012) on a per-pixel basis.CFMask provides pixel quality attributes for each Landsat 8 LST image, indicating the presence of cloud contamination.We build a pixel-wise binary occlusion mask for each Landsat LST image, where the pixel value is set to True if CFMask indicates that there is cloud, cloud shadow, or cirrus.From the occlusion mask, we calculate the occlusion factor that measures the fraction of pixels occluded.Formally, we have , where denotes a pixel of . is an important metric that measures the severity of cloud contamination.A higher indicates more severe occlusion.
3.2 NLCD Land Cover data
For land cover data, we use the National Land Cover Database(NLCD)(Yang etal.,, 2018) from the U.S. Geological Survey (USGS).NLCD provides spatially referenced descriptive data on the characteristics of the land surface using a set of thematic classes (e.g.,urban, forest, and agriculture).NLCD is available for the CONUS region at resolution with a release cycle of once per 3years.We denote the NLCD land cover image as .
3.3 In situ data
We collect in situ LST data at four Surface Radiation Budget Network (SURFRAD)sites(Augustine etal., 2000b, ).We use SURFRAD in situ data to validate our reconstruction results under cloudy conditions.The RMSE of SURFRAD in situ LST measurements is approximately0.5–(Wang and Liang,, 2009).SURFRAD measures downwelling and upwelling longwave flux every minute.The Stefan–Boltzmann law states
(1) |
where and are measured upwelling and downwelling longwave flux, respectively, is the broadband longwave surface emissivity, is the Stefan–Boltzmann constant, and is the surface skin temperature (equivalent to LST).
The in situ LST is obtained by inverting the upwelling component of Eq.(1)
(2) |
FollowingMalakar etal., (2018), we estimate the broadband emissivity() via a spectral-to-broadband regressionrelationship(Ogawa etal.,, 2008) using emissivity values from ASTERGEDv3 product(Hulley etal.,, 2015)
(3) |
where , are narrowband ASTER emissivities centered on 8.3, 8.6, 9.1, 10.6, and , respectively.
4 Methods
After acquiring the required inputs from Sec.3, we perform interpolation to predict cloud-occluded pixels.Our interpolator uses two complementary mechanisms: a spatial channel and a temporal channel.
4.1 Spatial Channel
In our approach, the prediction of occluded pixel values is informed by leveraging the information embedded in their surroundings.We term this approach the spatial channel.The intuition behind the spatial channel is that nearby objects that are in the same land cover class are likely to exhibit similar thermal properties.This approach filters the data based on land cover class, since the distribution of LST is dependent on land cover, as depicted in Fig.2.The computation of the spatial channel bears a close resemblance to bilateralfiltering(Paris etal.,, 2009).While traditional bilateral filtering considers variations in pixel intensities of the input image with the aim of preserving sharp edges, the spatial channel uses a different approach, where we filter the input image using the pixel attributes informed by auxiliary images (land cover class and occlusion ).
Consider our goal to be predicting at an occluded pixel location .Under a low occlusion factor , we estimate using a weighted average estimate from other pixels.Conceptually, the weights are determined based on three factors: proximity, land cover class label, and cloud occlusions.
We use a 2D Gaussian filter to model the distance-decay effect.Consider a pixel within a neighborhood centered at .Let be the Euclidean distance between and .We compute the proximity weight using a Gaussian kernel with standard deviation :
(4) |
decreases the influence of distance pixels while prioritizing the influence of nearby pixels.
We further modify the Gaussian filtering so that we only consider that is cloud-free and has the same land cover class as .Let be the inverse of the occlusion mask , so that if there is no cloud contamination at , and otherwise.To constrain land cover class, let function evaluate the land cover class of two pixels and such that if the corresponding land cover classes of and are the same (i.e.,), and otherwise.Formally, we write our weighted average local filter as follows:
(5) |
where is a normalization parameter that ensures weights sum to within each neighborhood (i.e., ):
(6) |
Local filtering (defined in Eq.(5)) works well when the occlusion factor is low.For a high , there are fewer neighboring pixels available, and local filtering leads to noisy or even invalid estimations.Instead of local filtering, we resort to global averaging when encountering high .Here we estimate the pixel value of with the average temperature of all non-occluded pixels that have the same land cover class, that is
(7) |
where is the land cover class of the occluded pixel , and is the total number of non-occluded pixels with the same land cover class as , excluding itself.Contrary to local filtering (Eq.(5)), here the averaging is performed across the entire image, rather than across some local window.As a result, we are no longer able to capture proximity effects.Therefore, outputs tend to be blurry due to spatial averaging.
Algorithm 1 shows the implementation of the spatial channel.Let be the occluded LST image as input, be the size of the local window, and be the maximum occlusion factor threshold for local filtering.We obtain interpolated image as follows:
In our implementation, we set the values of the optimization parameters as and . These values were selected based on extensive testing on a subset of images in our dataset to achieve the best performance.
4.2 Temporal Channel
In this section, we show how to generate an interpolated prediction using temporal information.We call this method the temporal channel, which is complementary to the spatial channel defined in Sec.4.1.As seen in Fig.3, the intuition behind the temporal channel is that objects in the same land cover class tend to exhibit similar thermal dynamics over time.The temporal channel involves four steps: (a) select a set of frames as a reference, (b) preprocess each reference frame via the spatial channel, (c) apply linear adjustments to each reference image, and (d) interpolate occluded regions based on the set of adjusted reference frames.
Reference frame selection:We select reference frames based on two conditions: (i) seasonality and (ii) cloud occlusion.The goal is to identify suitable reference images that can be used to accurately reconstruct occluded LST.
For seasonality, we take into account the temporal offset between the occluded target image and other available images .Due to temporal continuity, the previous and the next LST sample over the same region are good reference points.Constrained by the revisit cycle of Landsat 8, the previous and next samples are taken 16 days before and after the target date.In addition to the immediate temporal neighbors, we also leverage the predictable seasonal variations in LST signals. This allows us to include the previous and the next sample acquired in other years as potential references.In our actual implementation, we increase the selection limit to samples collected 2 cycles prior to or after the target date in each year, defining the temporal bracket duration as 2.See vertical gray stripes in Fig.3(b) for a visual illustration.
The second condition of reference frame selection is the occlusion factor .We have observed that minimally occluded reference frames lead to smaller interpolation errors.Therefore we only consider selecting reference frames whose is below a certain maximum tolerable threshold, (see red dotted line in Fig.3(b)).We call the set of images satisfying these two conditions candidate reference frames .Within , we prioritize selecting images that are captured closer to the target frame in time, as measured by the temporal difference .To achieve this, we choose a subset of frames from with the lowest .We call this selected subset of reference frames .
Spatial channel pre-processing:Given a selection of reference frames , we apply the computation of spatial channel from Fig.4.1 to produce a set of spatially complete reference frames.As a result, each pixel contains either observed or interpolated temperature data.Note that the imposed constraint on means that the spatial channel only needs to interpolate a minimal amount of occlusion.
Linear adjustments:After pre-processing , we apply a linear adjustment to all pixels in each class. Fig.3(a) shows that the changes in the mean LST (denoted as ) can differ drastically between classes.Although the selection of reference frames helps mitigate these discrepancies, some adjustments are still necessary.Specifically, for each reference frame, we add the difference in (i.e.,) between two dates to all pixels belonging to the corresponding land cover class. visually translates to the length of each bar in Fig.3(a).We repeat this process for all images in .
Reference frame-based interpolation:After the previous two steps, we obtain a set of linearly-adjusted reference frames.The interpolated LST image, , is the average of each linearly adjusted reference frame.This interpolation step combines the information from multiple reference frames to produce a spatially complete and temporally consistent estimate of the occluded LST image.
Algorithm 2 shows the implementation of the procedures above. Let be the number of reference frames to be selected, be the temporal bracket duration, and be the maximum tolerable occlusion factor. We compute the interpolated image as follows:
We set , , and . SP() denotes the function for spatial channel defined in Algorithm 1, and and at line 4 follow their default values from Sec.4.1.The subscript used in line 2 signifies the first elements of the sequence, arranged according to the minimization criterion. is the set of all pixel locations where the corresponding land cover class is , and is the cardinality of set .
4.3 Estimate LST via Weighted Average
Following the previous two subsections, we acquire initial predictions from the spatial channel and from the temporal channel. The final interpolated LST, denoted as , is calculated as the weighted average of the two initial predictions:
(8) |
where we set the weight .Note that for a minimally occluded image (a small ), more weight is assigned to the spatial channel.Contrarily, for a severely occluded image (a large ), fewer neighboring pixels are available for the spatial channel, and more weight is assigned to the temporal channel accordingly.
5 Results
5.1 Public Data Products
We deploy ISLAND across 20 regions in the United States. These regions areselected for having the highest populations, as reported in the 2020 U.S.Census(United States Census Bureau,, 2023).333For each region, we manually define apolygon roughly covering the metro region for each city. Due to the 32 MB limitper image download from Earth Engine, the polygon may not encompass the entiremetro region for some cities, such as New York.We collect Landsat data from 2019–2023 and NLCD 2021 release using the data compilation process described in Sec.3.We then use ISLAND to predict cloud-contaminated LST for each region.ISLAND produces interpolated LST at a spatial resolution of every 16 days444Subject to data availability and requires . for each observation region.Our public dataset is available on the NHERI DesignSafeCyberinfrastructure555https://doi.org/10.17603/ds2-3rf5-sd58(Rathje etal.,, 2017).
Fig.4 shows a set of interpolated LST maps produced by ISLAND. We choose a diverse set of examples consisting of different land cover and cloud occlusion conditions. ISLAND reconstructs spatially complete LST maps under a variety of conditions, from lightly occluded by thin cirrus clouds (Houston) to heavily occluded by optically thick clouds (New York, 88% occluded). Our model performs well across regions with different land cover characteristics, from dense urban settings (New York and Los Angeles) to diverse wetlands (Jacksonville).
Parameters MAE (K) RMSE (K) City M1 M2 M3 M4 M5 M1 M2 M3 M4 M5 Houston 250 10 1.88 2.19 1.82 2.49 4.26 2.43 2.85 2.33 3.10 5.05 Houston 750 3 2.00 2.39 1.87 2.57 3.97 2.53 3.05 2.38 3.25 4.71 Jacksonville 75 2 1.47 1.57 1.53 2.14 3.89 1.88 2.07 1.88 2.61 4.51 Phoenix 500 1 1.96 2.08 1.36 2.18 2.63 2.62 2.78 1.81 2.88 3.33 New York 100 2 1.25 1.37 1.14 2.11 3.82 1.65 1.84 1.42 2.63 4.28
5.2 Simulation Evaluation
To evaluate the performance of our LST reconstruction method, we simulate cloud contamination by artificially occluding Landsat LST images. The added occlusions occupy a set of rectangular regions. We set LST pixel values to zero and occlusion mask to True in these regions. To evaluate, we choose four urban regions with different climatological and surface land cover conditions, as seen in Table1. For each region, we apply up to occlusion regions of size pixels for all available LST images from 2019–2023. We use NLCD 2021 release as input for our simulation evaluation. For LST images with actual cloud occlusion (i.e.,real occlusion), we place artificial occlusions alongside real occlusions and ensure no overlap. The evaluation metrics are computed only in artificially occluded areas by comparing them to the original LST images. We report the mean absolute error (MAE) and root mean squared error (RMSE) in Table1. M1 refers to our model ISLAND, while M2 - M4 refers to other models discussed in 5.3.
Table1 suggests that the RMSE typically ranges from 1.65– for a variety of urban regions and occlusion scenarios. The first two rows of Table1 indicate that more occlusions generally lead to larger reconstruction errors. ISLAND performs well in dense urban regions (such as New York City) and diverse wetlands (such as Jacksonville). Our simulation evaluation indicates that ISLAND is robust in performance and is able to generalize reasonably well to different regions in the U.S. and under different occlusion characteristics.
5.3 Ablation Study on Simulation Data
To further demonstrate the effectiveness of our model, we perform an ablation study666Not to be confused with the glaciological definition of ablation, which refers to the process of removing snow, ice, or water from a glacier or a snow field. Here, we use the artificial intelligence definition of ablation study, where certain components of a model are removed in order to gain a better understanding of the model’s behavior. , where we remove key components of our model and observe the impact of each of these components on the overall performance. Table1 shows a list of models. M1 refers to our full model, ISLAND. In M2, we exclude the temporal channel and only keep the spatial channel. In M3, we discard the spatial channel and only keep the temporal channel. Note that M1 is a weighted average of M2 and M3, following Eq.(8). M4 is the same as ISLAND, except we remove NLCD land cover labels as input. M5 adopts a simplified approach where missing values are replaced with the average pixel value within an image without considering their spatial distribution or land cover class.
Table1 shows that the use of NLCD land cover labels (M1) leads to better reconstruction performance over the model without NLCD as input (M4). Such difference is more pronounced in dense urban regions, such as New York, and heterogeneous regions, such as Jacksonville.
When confronted with heavier occlusions (large ), M3 consistently emerges as the top-performing model.This finding highlights the effectiveness of relying solely on the temporal channel in such challenging scenarios.Conversely, for occlusions of low to moderate severity, the spatial channel exhibits stronger performance.By appropriately favoring the temporal prediction in the presence of heavy occlusions, and the spatial prediction for lighter occlusions, our weighting scheme (Eq.(8)) allows for adaptability to varying occlusion levels, enhancing the model’s robustness and accuracy in capturing LST under diverse conditions.
5.4 In situ Evaluation
In this section, we evaluate ISLAND reconstruction results against in situ LST measurements collected in four SURFRAD stations. Our in situ evaluation uses Landsat and SURFRAD data collected in 2013–2020 and NLCD 2016 release as inputs to ISLAND. In 5.2, simulation evaluation examines ISLAND’s ability to reconstruct a theoretical clear-sky LST under artificial occlusion. However, studies have shown that clouds have a cooling effect on the shaded region(Weng and Fu,, 2014), and simulation data is unable to evaluate if our reconstruction algorithm accounts for this effect. As such, we use in situ measurements to evaluate reconstruction performance under cloud-sky conditions.
There are two primary disadvantages to evaluating ISLAND against SURFRAD in situ data.First, remote retrieval of LST, even in clear-sky conditions, has uncertainties.Sources of error include uncertainty in emissivity estimation, atmospheric compensation, etc.Further, a Landsat 8 pixel (at resolution) is larger than thefield-of-view (FoV) of the SURFRAD instrument(Malakar etal.,, 2018).Differences in FoV compounded with spatial heterogeneity in temperature atSURFRAD sites(Malakar etal.,, 2018) cause Landsat LST to furtherdeviate from in situ LST. Therefore, we report Landsat LST versus in situ LST under clear-sky conditions (without involving ISLAND) in Fig.5. RMSE ranges from across four sites.777We choose not to include two other SURFRAD sites, BND and FPK, because the Landsat clear-sky RMSE at these sites is too high in our calculation, at and respectively. The error here represents the underlying uncertainties of comparing Landsat LST with in situ LST, which is external to our reconstruction algorithm.
The second disadvantage of SURFRAD evaluation is that all SURFRAD sites are located in rural, hom*ogeneous areas. Recall that the primary advantage of ISLAND is the use of NLCD data, which is applicable to urban areas but not to SURFRAD sites. Unfortunately, there are no publicly available in situ LST validation sites located in urban regions. Despite these limitations, we report in situ validation results to build an understanding of how ISLAND performs under real cloud occlusion.
Fig.6 shows ISLAND reconstructed LST versus in situ LST under cloudy-sky conditions. We define a given data point as cloudy-sky if and only if the corresponding Landsat pixel is flagged as cloud, cloud shadow, or cirrus, according to CFMask. Reconstruction RMSE ranges from , across four sites. Compared to clear-sky RMSE in Fig.5, ISLAND introduces an additional RMSE error across four sites, with the average additional RMSE being .
Fig.6 also shows that there is no systematic overestimation (i.e.,positive bias) across all four SURFRAD sites, suggesting that ISLAND effectively accounts for the local cooling effects caused by clouds. We believe that the modeling of the local cooling effect is primarily driven by the spatial channel defined in 4.1. As clouds move, they also cool the surrounding neighborhood. Under the assumption that surface objects have some degree of thermal inertia, we believe that the spatial channel utilizes surrounding cooler pixels to account for the local cooling effect caused by clouds, thereby accurately predicting LST under cloudy-sky conditions. While simulation evaluation (Table1) shows that using the temporal channel alone (M3) leads to better results than M1, in situ evaluation, however, suggests that using both spatial and temporal channels (M1, Fig.6) leads to better results. When using temporal channel only (M3), the RMSE at DRA, SXF, PSU, and GWN are , , , and , respectively.
Finally, Fig.6 includes reconstruction error for all cloudy-sky conditions except for dates with more than 99% of pixels occluded (), demonstrating ISLAND’s robustness under a wide variety of cloud occlusion scenarios, including severely occluded LST images.
5.5 Applications
In this section, we show a set of applications demonstrating the impact of ISLAND on a variety of LST applications in urban environments.
5.5.1 Deriving surface temperature trends
The robustness demonstrated in Sec.5.2 – 5.4 and the compelling results displayed in Fig.4 underscore the model’s ability to generate accurate interpolated LST values across a wide range of conditions, with the only constraint being that the occlusion factor . As highlighted in Sec.1, previous studies investigating changes in land surface temperature through remote sensing have been constrained by limited observational conditions, restricting their analyses to a fraction of dates characterized by minimal cloud occlusion (Sobrino etal.,, 2013; Baiocchi etal.,, 2017; Huang and Wang,, 2019; Gomez-Martinez etal.,, 2021). However, with the introduction of ISLAND, these limitations become obsolete, granting access to a significantly expanded set of operational LST data, particularly in urbanized regions.
Beyond the production of interpolated image outputs, ISLAND enables the examination of temporal variations in LST on daily-to-seasonal timescales. By reconstructing skillful LST maps for the majority () of observation dates, ISLAND enables the comparison of thermal behaviors for a given region across time, at a relatively dense sampling rate of every 16 days, and encompassing diverse land cover types. To illustrate this capability, Fig.7 showcases the evolution of surface temperature in Houston. Each colored line represents the average LST for a particular land cover class for all grid cells over Houston. Pronounced temperature seasonality is evident in the time series from 2019–2023, with clear changes in seasonal structure from year to year. The ability to partition surface temperatures retrieved from different land cover types reveals differences of up to C between forested, water-covered surfaces and urban developed surfaces (e.g.,open-water vs. developed high intensity). With a 16-day temporal resolution, one can evaluate temperature distributions and variances over different land cover classes in different seasons. ISLAND facilitates an in-depth investigation of the temporal dynamics of surface temperature within any region located in the CONUS, providing valuable insights for climatological and ecological analyses.
5.5.2 Urban heat island effects
Another key application of our model is the ability to study urban heat islandeffects (UHIE)(Moller etal.,, 2022) at high spatiotemporal resolution.UHIE refers to the phenomenon of urban areas being significantly warmer than their surrounding rural areas.UHIE is primarily driven by the differences in thermal absorption between different materials.For example, grass- or water-covered surfaces tend to have lower temperatures than concrete and asphalt.By providing high spatial and temporal resolution LST outputs, ISLAND offers a novel data product for identifying, studying, and monitoring UHIE in major U.S. metropolitan areas.Fig.8 shows maps of three of the largest U.S. metropolitan areas, Los Angeles, Chicago, and Houston, where the pixel values indicate the number of days surpassing a region-specific temperature threshold.The thresholds are selected based on the definition of Extreme Dangerfrom the National Weather Service (NWS) heat index(Rothfusz and Headquarters,, 1990).The NWS heat index is a function of both temperature and relative humidity.The Comparative Climatic Data (CCD-2018)(NOAA,, 2020) providesthe morning annual average relative humidity () for each city.We select the LST threshold for each region as the minimum ambient dry bulb temperature that meets the NWS Extreme Danger criteria for the city’s corresponding .The range of observations is 4.5years, at a sampling rate of once per 16 days.Higher values in the frequency maps indicate a more frequent occurrence of UHIE.These frequency maps are available at resolution and can be easily computed using our public dataset.From an urban planning perspective, these UHIE frequency maps offer a powerful tool for enhancing our understanding of how land cover choices influence micro-climates, heat extremes, and the associated health risks.By providing insights into the spatial distribution and frequency of UHIE, these maps can inform decision-making processes regarding urban development and land cover management, aiming to mitigate the adverse effects of heat on public health and well-being.
5.5.3 Social Vulnerability & Urban Heat Stress
As illustrated in the last two applications, ISLAND facilitates the development of comprehensive datasets of LST and UHIE. The developed datasets will enable better characterization of heat exposure and its impacts on social, infrastructure, and environmental systems. A representative example application would be to investigate inequities in urban heat exposure.Given the health, well-being, and quality of life implications of urban heat,and initiatives like Justice 40(The White House,, 2022), whichcall for federal climate investments to be directed to environmental justicecommunities, understanding the equities in urban heat exposure can centrallyguide prospective investments.For example, quantifying inequities in exposure to urban heat will help design adaptation measures such as increasing vegetation cover or guiding urban planning, among others.
The distribution of UHIE for residential areas and the social vulnerability ofthe exposed population for a few cities are shown in Fig.9. Here,social vulnerability is measured using the Centers for Disease Control andPrevention Social Vulnerability Index (CDC SVI)(Centers for Disease Control etal.,, 2020). TheCDC SVI measures social vulnerability on a scale of 0 (least vulnerable) to 1(most vulnerable), taking into account socioeconomic status (e.g.,housing costburden), household characteristics (e.g.,civilian with a disability), racialand ethnic minority status (e.g.,Hispanic, Alaska Native), housing type andtransportation factors (e.g.,no vehicle). The latest available residentialland use data (2016) fromMcShane etal., (2022) are used to identifyresidential regions. Additionally, UHIE is calculated as the number of daysthat a pixel (resolution of )exceeds a land surface temperature threshold of C(308.5K). Only pixels with at least one day of temperature over the threshold areconsidered for the analysis.
From Fig.9, many cities show a systemic inequity in heat exposure. For example, in Los Angeles, socially vulnerable communities are exposed to high urban temperatures compared to less socially vulnerable communities (Pearson’s correlation, ). A similar trend can be seen in cities such as San Antonio () and San Francisco (), San Jose (), and New York (). In contrast, cities such as Houston (), Dallas (), Jacksonville (), and Fort Worth () show no or negligible inequity in urban heat exposure. Factors ranging from vegetative cover to colocation with industrial or commercial locations might influence urban heat exposure. By providing reliable and more complete datasets to quantify UHIE, this study will allow for a better understanding of the factors influencing heat exposure and, as a result, will aid in developing strategies to mitigate urban heat stress.
While this section only provides three case studies, the proposed method facilitates many applications requiring high-resolution site-specific data. Some example applications include designing building envelopes and Heating, Ventilation, and Air Conditioning (HVAC) systems,investigating the influence of thermal stresses on infrastructure aging and deterioration, power system reliability, and energy demand shifts,or even high-resolution weather and natural hazard modeling that accounts for fine-scale surface temperature effects.
6 Discussion & Conclusions
6.1 Assumptions and Limitations
Despite the demonstrated validity and effectiveness of ISLAND, the model does contain important assumptions and limitations.
Limitations of NLCD land cover labels:As stated in Fig.1, our model uses the NLCD land cover data.Specifically, we use the NLCD 2021 release(Dewitz,, 2021) for ourpublic data products, which most accurately reflect the state of land coverlabels in the year 2021.Our Landsat LST inputs, in contrast, span from 2019–2023.Within this observational period, NLCD 2021 alone does not reflect changes in land cover due to urban expansion, meandering, or coastal erosion.NLCD releases every three years, and there are versions available for 2019, 2016, etc.Although a fraction of observation dates could have potentially benefited from using the 2019 release instead of 2021, we chose not to implement computation using multiple NLCD versions for simplicity.Despite using only the NLCD release for 2021, we still observe a significant advantage in terms of reconstructed LST when employing the NLCD dataset as input, as seen inTable1.
Reliance of accurate cloud masks:Another assumption of our model is that all clouds are correctly labeled. Our algorithm relies on accurate cloud labeling, and errors in cloud labeling would most likely propagate to LST estimations.Recall from Fig.1 that we utilizeCFMask(Zhu and Woodco*ck,, 2012) to determine if a given pixel is affected bycloud, cloud shadow, or cirrus.CFMask algorithm can produce inaccurate cloud labels, leading to erroneous LST reconstructed values.Cirrus is a category of clouds known to be challenging todetect(Qiu etal.,, 2020).Row two of Fig.4 shows that unidentified cirrus in the lower left corner leads to visible discontinuities in the reconstructed LST image.Unidentified cirrus pixels seem to affect the spatial channel more than the temporal channel.Moreover, it is difficult to reliably detect clouds in snow-covered terrain dueto the spectral similarity between cloud andsnow(Stillinger etal.,, 2019).Consequentially, in a simulated evaluation, we observe significantly higher RMSE in Denver, a region with prolonged snow coverage.Finally, in some cases, we also observe that optically bright and thermally cold buildings are falsely labeled as clouds, though this mislabeling is rare.The increasing prevalence of white roofing applied to increase urban albedo anddecrease the UHIE may worsen the future uses of ISLAND(Fayad etal.,, 2021).It is important to address these challenges and improve cloud labeling techniques to ensure the accuracy of our model’s predictions.
Errors external to interpolation:As mentioned in Sec.5.4, remote LST retrieval itself has underlying uncertainties, even in clear-sky conditions.Other sources of error include, but are not limited to, sensor calibration,atmospheric profiles, and emissivityestimation(Li etal.,, 2013).Profiling and mitigating these types of errors are areas of activeresearch(Li etal.,, 2013) in the field of remote sensing; suchimprovements are beyond the scope of this paper.
6.2 Transferability and Scalability
We designed ISLAND to be easily accessible to the broader research community.All required inputs of our model listed in Fig.1 areacquired from publicly available sources and are extracted from Google EarthEngine(Gorelick etal.,, 2017) and its Python API, geemap(Wu,, 2020).Since our model is essentially based on a set of filters, we do not require massive computing power or GPU acceleration.For context, it takes roughly 2min to process one imageon a 12-core CPU (AMD Ryzen 5900) with 32GB of DRAM.
Currently, our model is only available to the CONUS region, constrained by the NLCD dataset.In Fig.6.3, we discuss potential avenues for expanding the operational region.
6.3 Potential Improvements and Future Opportunities for ISLAND
Extending area of study:In this paper, visual results (Fig.4), simulation evaluations (Table1), and demonstrated applications are all focused on urban regions.While in situ evaluation (Fig.5 and 6) are conducted on SURFRAD sites, which offers an indicator of model performance on regions with relatively high land cover hom*ogeneity (see Fig.A for visual illustrations), extensive future testing is required to better quantify performance outside of urban settings.
In Sec.6.1, we showed that the use of the NLCD dataset currently restricts our analysis to the CONUS region.In theory, we can potentially expand to other regions where there are appropriate land cover labels.For example, the Copernicus CORINE Land Coverdataset(Buchhorn etal.,, 2020) wouldfacilitate the extension of the model over Europe; the China land cover dataset(CLCD)(Yang and Huang,, 2021) is also available, enabling research over theAsian continent. It is important to note that the performance of ISLAND isimpacted by spatiotemporal resolution, diversity, and accuracy of land coverlabels.Therefore, careful consideration should be given to the suitability and quality of the land cover datasets when expanding the study area beyond CONUS.Additional testing is required to quantify ISLAND performance across different datasets and cities outside of CONUS with different land cover characteristics.
Improving temporal resolution:Higher temporal resolution for LST products is instrumental for downstream operational studies.Our model uses Landsat 8(USGS, 2023a, ) data to achieve one LSTreconstruction every 16 days.Recently, the Landsat program launched a companion satellite, Landsat9(USGS, 2023b, ), carrying a nearly identical TIRS as Landsat 8.Landsat 8 and Landsat 9 are phased eight days apart.By incorporating data from both satellites, we can reduce the time gap between consecutive satellite visits to just eight days.This enables us to capture LST measurements at a higher temporal resolution.
Another avenue to increasing the temporal resolution is to use satelliteproducts with shorter revisit cycles. For example, theSentinel-3(Donlon etal.,, 2012) program provides a temporal resolution ofat least once per day (at the equator). Unfortunately, the spatial resolutionof their LST products is lower than that of Landsat 8 (e.g.,Sentinel-3 at).Additional benchmarking on the selected data source is required before applying ISLAND, as performance could vary based on spatio-temporal resolution.
Incorporating deep learning:The basis of our model is a set of filters designed around adjacency and temporal properties of thermal signatures.As seen in Algorithms 1 and 2, these filters are hand crafted to explicitly represent these relationships.While we clearly demonstrated the effectiveness of ISLAND through qualitative and quantitative analysis, we acknowledge that a well-designed deep learning algorithm has the potential to achieve even better performance.Here, we highlight a few examples.Firstly, deep learning models have the capability to capture complex inter-class relationships between different land cover labels.This could enhance the overall accuracy of our interpolator.Secondly, a dynamic spatial channel that adapts based on occlusion characteristics could be incorporated, allowing the model to better handle varying cloud cover conditions.Additionally, an optimized weighting scheme, an improved cloud detection filter, and an updated NLCD land cover dataset to account for changes in land cover could be integrated into a deep learning framework.Lastly, integrating additional data sources, such as other satellite data andground-based observations, could further reduce reconstruction errors, andthere are existing deep learning techniques(Han etal.,, 2024) to perform datafusion.Given the complexity of the problem and the non-linear nature ofLST(Wu etal.,, 2021), deep learning is a suitable direction for futurework, but designing and training a deep learning framework might requireextensive research.
6.4 Advancing Existing State-of-the-Art LST Estimates
In this paper, we showed that a large fraction of Landsat measurements are occluded by clouds. As a consequence, the actual usable temporal resolution of Landsat is significantly reduced, falling below once per month when subject to frequent cloud occlusions. The role of ISLAND is to mitigate cloud contamination in LST images, maximizing its usable temporal resolution.
Indeed, the addition of ISLAND represents an advance over existing LST products, as shown in Fig.10.Generally speaking, there is a trade-off between spatial resolution and temporal resolution for satellite LST products.Amongst all available satellite measurements, Landsat 8 (along with the later-launched Landsat 9) offers the best spatial resolution at , with 16-day revisit cycles.Operating in sun-synchronous orbits (SSO), Landsat 8 has the advantage of providing global coverage and maintaining time-constant illumination conditions of the observed surfaces (except for seasonal variations).The MODIS program(Wan,, 2013), consisting of a pair of satellites namedAqua and Terra, offers a much higher resolution at daily revisit cycles butprovides LST data at a much lower spatial resolution of .Satellites in geostationary orbits do provide higher temporal resolution at the expense of spatial resolution and global coverage.For example, GridSat-B1(Knapp etal.,, 2011; Knapp,, 2014) provides brightnesstemperature (BT) data at a resolution of .
In addition to satellite-based methods, climate reanalysis data provides an alternative approach to obtaining LST.These products are generally designed to maintain the best possible physicaland temporal consistency and require prohibitive computationalcosts(Hakim etal.,, 2016).The spatial resolution of these products is not comparable to satellite-basedmethods; HRRR(James etal.,, 2022) provides climate data at andERA5(MuñozSabater,, 2019) at around .Fig.10(b) provides a visual comparison of the spatial resolution of LST from ERA5 and ISLAND.Due to relatively low spatial resolution, the urban spatial structure over Philadelphia is indistinguishable in ERA5 skin temperature fields. In contrast, our method effectively removes cloud contamination and produces a high-resolution reconstruction of LST at resolution.
As shown in Fig.5.5, many downstream applications generally benefit from increased spatial and temporal resolution.For dense urban settings, high spatial resolution is particularly desirable, making the Landsat data a preferred choice.In Fig.6.3, we discussed the potential of incorporating measurements from Landsat 9 to further enhance the temporal resolution to 8 days.This advancement would bring us closer to achieving consistent weekly measurements at a spatial resolution of .Such a combination of high spatial and temporal resolution of LST data is instrumental to our understanding of urban areas.
6.5 Conclusions
This paper introduces ISLAND, a novel model designed to address the issue of cloud occlusion in satellite LST images. ISLAND removes occlusion by estimating LST pixel values through a set of spatio-temporal filters. These filters account for the land cover class, resulting in higher LST reconstruction accuracy. ISLAND addresses a fundamental limitation of LST retrieval via remote sensing, thereby dramatically increasing the number of serviceable LST images via a robust mechanism to mitigate cloud contamination. These improvements enable nearly bi-weekly coverage of LST at resolution over the CONUS region, a large advance over previously available LST products derived from remote sensing. We show ISLAND can operate in a variety of land cover types and cloud occlusion scenarios in both simulations and in situ evaluations. Overall, ISLAND provides a promising framework for a multitude of scientific applications that require high-resolution, frequent observations of LST, including but not limited to (1) urban heat island effects, (2) derivation of surface temperature trends, and (3) social vulnerability and urban heat stress.
Acknowledgement
The authors gratefully acknowledge the support of this research by the National Science Foundation (NSF) award numbers 1652633 and 2107313. The contributions of Pranavesh Panakkal and Jamie E. Padgett were partially supported by NSF award number 2227467. Any opinions, findings, conclusions, or recommendations expressed in this paper are those of the authors and do not necessarily reflect the views of the sponsors.
Appendix A Additional Details on NLCD Land Cover
Fig.11 shows the visualization of the NLCD maps referenced in this paper. Fig.11(a) shows urban NLCD maps for cities in our visual results (Fig.5.1), simulation evaluation (Sections5.2–5.3), and illustrated applications (Fig.5.5), while Fig.11(b) shows the NLCD maps around four SURFRAD sites for our in situ evaluation (Fig.5.4). Refer to Fig.11(c) for the legend of NLCD classes.
The heterogeneity of land cover types in urban settings is clearly reflected in Fig.11(a). As discussed in Fig.2 and 3, LST is closely related to land cover; as such, complex terrains in cities make LST reconstruction challenging. The difference in land cover distributions between urban regions is also evident in Fig.11(a). New York City is characterized by high-density developments with very sparse natural landscapes. Houston, TX, has extensive urban and suburban developments with mixed land use, accompanied by agricultural land, forest, and water bodies on the outskirts. Jacksonville, FL, has large expanses of coastal wetlands. Phoenix, AZ, is an urban region surrounded by deserts and mountains with sparse vegetation. We chose the four regions to conduct our simulation evaluation (Table1) to represent ISLAND’s performance under a variety of settings.
Our in situ validation targets are SURFRAD stations located in rural regions. Fig.11(b) shows the relative hom*ogeneous land cover types surrounding the SURFRAD stations, which is in stark contrast to the urban regions in Fig.11(a).
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