Geology Reports⌕ Search
Home/Archive/USGS

USGS · 70224305

Urban heat island and its regional impacts using remotely sensed thermal data – A review of recent developments and methodology

Hua Shi·George Z. Xian·Roger F. Auch·Kevin Gallo·Qiang Zhou
View original source ↗Export metadata ↓

Abstract

Many novel research algorithms have been developed to analyze urban heat island (UHI) and UHI regional impacts (UHIRIP) with remotely sensed thermal data tables. We present a comprehensive review of some important aspects of UHI and UHIRIP studies that use remotely sensed thermal data, including concepts, datasets, methodologies, and applications. We focus on reviewing progress on multi-sensor image selection, preprocessing, computing, gap filling, image fusion, deep learning, and developing new metrics. This literature review shows that new satellite sensors and valuable methods have been developed for calculating land surface temperature (LST) and UHI intensity, and for assessing UHIRIP. Additionally, some of the limitations of using remotely sensed data to analyze the LST, UHI, and UHI intensity are discussed. Finally, we review a variety of applications in UHI and UHIRIP analyses. The assimilation of time-series remotely sensed data with the application of data fusion, gap filling models, and deep learning using the Google Cloud platform and Google Earth Engine platform also has the potential to improve the estimation accuracy of change patterns of UHI and UHIRIP over long time periods.

Explore related subjects

LandArticle

Keep this discovery

Explore connections, maps & timelines

BibTeXRIS

Hua Shi, George Z. Xian, Roger F. Auch, Kevin Gallo, Qiang Zhou. 2021-08-18. Urban heat island and its regional impacts using remotely sensed thermal data – A review of recent developments and methodology. https://doi.org/10.3390/land10080867

Cite the original work for its findings. Save a collection to share your selection of sources.

KEEP EXPLORING

Related USGS reports

USGS2026

Improving estimates of spatially distributed agricultural land use using pesticide data

Accurate, spatially resolved agricultural land-use data are essential for estimating water demand, evaluating agricultural production, and understanding long-term changes in managed landscapes. However, existing land-use datasets are often temporally discontinuous, particularly for historical periods, and remotely sensed classifications face persistent challenges distinguishing spectrally similar crop types, capturing sub-annual rotations, and separating active from abandoned plantings. We developed the California Pesticide Use Reporting Land-Use Estimator (CalPUR-LUE), which converts grower-reported pesticide-application records into annual estimates of land use by crop class and cropped acreage at approximately 1-square-mile resolution across California. CalPUR-LUE performance was evaluated for 2018 using complementary spatial, categorical, and quantitative comparisons with California Department of Water Resources (CADWR) remotely sensed land-use data and county-level agricultural economic reports. At the county-meridian-township-range-section (COMTRS) scale, CalPUR-LUE and CADWR acreage estimates showed substantial proportional agreement (log-transformed Lin’s concordance correlation coefficient = 0.77) with a mean proportional bias of −15%. Crop-class agreement was high when CalPUR-LUE identified agricultural activity, with user’s accuracy exceeding 83% for all evaluated crop classes. Overall categorical agreement was 67% (Cohen’s kappa = 0.56). Grain crop underestimation contributed substantially to disagreement; however, there is evidence of CADWR systematic overestimation of grain during 2016–2019. County-level agricultural reports further corroborated CalPUR-LUE estimates and, in several cases, aligned more closely with CalPUR-LUE than with CADWR. These results demonstrate that pesticide-use reporting provides an independent and complementary source of agricultural land-use information that can corroborate contemporary estimates and reconstruct annual spatially distributed land use where historical data are limited.

California↗
USGS2026

Biochar modulates the dynamics of legacy nutrients in enhancing soil health and crop productivity

Most major crops in agricultural soils exhibit relatively low nutrient use efficiency for nitrogen (N), phosphorus (P), and potassium (K), often necessitating supplemental nutrient inputs to achieve sustainable yields. Furthermore, the increasing use of biowastes such as compost, manure, and biosolids, which frequently have nutrient ratios that do not match crop requirements, has contributed to excessive nutrient inputs and subsequent accumulation in soils. This situation has been further exacerbated by intensive farming practices involving multiple cropping cycles per season. Overuse of nutrients causes them to accumulate in the soil, creating a legacy nutrient pool. The application of biochar as soil amendment is considered a potential strategy to control legacy nutrients dynamics. The current review inspects the possible value of biochar in modulating legacy nutrient reserves in the soil, thereby increasing the bioavailability of nutrients and improving crop yield. This review discusses the search scope and synthesis approaches for the bibliometric methodological component through rigorous screening process (Preferred Reporting Items for Systematic Reviews and Meta-Analysis (PRISMA)), focusing on journal articles published in last 20 years that specifically address legacy nutrient management. The significance of the economic and environmental effects of legacy nutrients and the insufficient knowledge of how biochar application influences nutrient dynamics in soil highlight the necessity for additional research to address current gaps.

Land↗
USGS2025

Assessing survey design for long-term population trend detection in piping plovers

Determining appropriate spatio-temporal scales for monitoring migratory shorebirds is challenging. Effective surveys must detect population trends without excessive or insufficient sampling, yet many programs lack formal evaluations of survey effectiveness. Using data from 2012 to 2019 on Louisiana’s barrier islands (Whiskey, west Raccoon, east Raccoon, and Trinity), we assessed how spatial and temporal scales influence population trend inference for piping plovers ( Charadrius melodus ). Point count data were aggregated to grid sizes from 50 to 200 m and analyzed using Bayesian dynamic occupancy models. We found occupancy and colonization estimates varied by spatial resolution, with space–time autocorrelation common across scales. Smaller islands (east and west Raccoon) yielded higher trend detection power due to better detectability, while larger islands (Trinity and Whiskey) showed lower power. Detectability, more than sampling frequency, drove trend inference. Models incorporating spatial autocorrelation outperformed traditional Frequentist approaches but showed poorer fit at coarser scales. These findings underscore how matching analytical scale to ecological processes and selecting appropriate models can influence predictions. Power analysis revealed that increasing survey frequency may improve inference, especially in low-detectability areas. Overall, our study highlights how careful scale selection, model diagnostics, and survey design can enhance monitoring efficiency and support long-term conservation of migratory shorebirds.

Louisiana↗