Peng Fu

Harrisburg University of Science and Technology

Urbanization-associated land use and land cover (LULC) changes lead to modifications of surface microclimatic and hydrological conditions, including the formation of urban heat islands and changes in surface runoff pattern. The goal of the paper is to investigate the changes of biophysical variables due to urbanization induced LULC changes in India...

Land surface temperature (LST) is a valuable parameter in studies of surface energy balance, landscape thermal patterns, and human–environment interactions. An effective way to quantify the LST dynamics over spatial and temporal domains is to utilize the consistent Landsat thermal infrared (TIR) data since 1982. Currently, only a small proportion o...

A comprehensive comparison of the trends and drivers of global surface and canopy urban heat islands (termed Is and Ic trends, respectively) is critical for better designing urban heat mitigation strategies. However, such a global comparison remains largely absent. Using spatially continuous land surface temperatures and surface air temperatures (2...

Urbanization experiences different speeds and forms under diverse development stages across the globe. However, urbanization-induced impacts on long-term surface urban heat island intensity (Is) trends across global cities and the regulators of such impacts remain understudied. Here we estimate interannual trends in daytime Is (i.e., urban-rural di...

Understanding the seasonal variations in surface urban heat island (SUHI) in different local climate zones (LCZs) is crucial to efforts to reduce the impacts of urban warming on local residents. However, such an understanding is constrained by the lack of land surface temperatures (LSTs) at both high spatial and temporal resolutions. This study cre...

The urbanization process has significantly intensified surface urban heat island (SUHI) effects in the Bangkok Metropolitan Region (BMR). However, understanding the evolution of the urban thermal environment is challenging due to the difficulty in obtaining consistent remote sensing data of the cloud-prone landscape in the BMR. In this study, the d...

Plants have evolved to adapt to their neighbours through plastic trait responses. In intercrop systems, plant growth occurs at different spatial and temporal dimensions creating a competitive light environment where aboveground plasticity may support complementarity in light‐use efficiency, realising yield gains per unit area compared to monocultur...

Background Studies show that fine particulate matter (PM2.5) contributes to childhood obesity. However, evidence on the effects of its constituents on obesity has not been explored. Methods Using multistage stratified cluster sampling, we enrolled 41,439 school-age children (aged 6–17 years) from a representative nationwide survey of 30 provinces...

Surface soil moisture (SSM) is of great importance in understanding global climate change and studies related to environmental and earth science. However, neither of current SSM products or algorithms can generate SSM with High spatial resolution, High spatio-temporal continuity (cloud-free and daily), and High accuracy simultaneously (i.e., 3H SSM...

Surface soil moisture (SSM) has a considerable impact on land-atmosphere exchanges of water and energy fluxes. However, due to the inherent deficiencies of remotely sensed data (e.g., cloud contamination in thermal remote sensing, coarse resolutions for microwave remote sensing), none of the current algorithms alone can provide daily and seamless f...

More than half of the satellite-derived Land surface temperatures (LSTs) data are missing due to poor weather conditions (e.g., clouds, shadows, and other atmospheric conditions) and/or sensor failure, which has greatly limited the acquisition of spatially consistent and temporally regular LST data, leading to reduced utilization and accuracy of th...

Gas exchange techniques revolutionized plant research and advanced understanding, including associated fluxes and efficiencies, of photosynthesis, photorespiration, and respiration of plants from cellular to ecosystem scales. These techniques remain the gold standard for inferring photosynthetic rates and underlying physiology/biochemistry, althoug...

The significant reduction in human activities during COVID‐19 lockdown is anticipated to substantially influence urban climates, especially urban heat islands (UHIs). However, the UHI variations during lockdown periods remain to be quantified. Based on the MODIS daily land surface temperature and the in‐situ surface air temperature observations, we...

Conventional remote sensing image retrieval (RSIR) systems perform single-label retrieval with a single label to represent the most dominant semantic content for an image. Improved spatial resolution dramatically boosts the remote sensing image scene complexity, as a remote sensing image always contains multiple categories of surface features. In t...

Sustained increase in atmospheric CO2 is strongly coupled with rising temperature and persistent droughts. While elevated CO2 promotes photosynthesis and growth of vegetation, drier and warmer climate tends to negate this benefit, complicating the prediction of future terrestrial carbon dynamics. Manipulative studies such as Free Air CO2 Enrichment...

Land surface temperature (LST) is an important variable to understand surface energy fluxes, land–atmosphere interactions, and urban thermal environments. Time series analysis of LSTs through semi-physical models such as the annual temperature cycle (ATC) model has become critical for these understandings. However, studies are lacking in examining...

The comprehensive evaluation of model-and satellite-based surface soil temperature (ST) products is a prerequisite for applications of these datasets in hydrology, ecology, and climate change, as well as in passive microwave soil moisture retrieval algorithms. Distinguished from existing regional validations, this study used ground soil temperature...

Remotely sensed and accurate daily mean land surface temperature (Tdm) is valuable for various applications such as air temperature estimation and climate change monitoring. However, most traditional methods employed by the remote sensing community estimate Tdm by averaging the – usually few – observed cloud-free land surface temperatures (LSTs). S...

The Midwestern “Corn-Belt” in the United States is the most productive agricultural region on the planet despite being predominantly rainfed. In this region, global climate change is driving precipitation patterns toward wetter springs and drier mid- to late-summers, a trend that is likely to intensify in the future. The lack of precipitation can l...

High-precision digital soil organic carbon (SOC) stocks mapping is very important for agricultural production management and global carbon cycle. The spatial heterogeneity of farmland SOC is not only influence by the environmental factors of soil formation but also the management practices of tillage, fertilization, and irrigation. However, the tra...

Recently, deep metric learning (DML) has received widespread attention in the field of remote sensing image retrieval (RSIR), owing to its ability to extract discriminative features to represent images and then to measure the similarity between images via learning a distance function among feature vectors. However, the distinguishability of feature...

Soil organic carbon (SOC) content is of critical importance in managing agricultural fields and understanding carbon fluxes at various scales. At regional and global scales, digital mapping of SOC has been successful using environmental variables such as climatic, land use and terrain factors. However, such a mapping for agricultural lands in low-r...

Because of the rapid growth of multi-modal data from internet and social media, cross-modal retrieval has become an important and valuable task in recent years. The purpose of cross-modal retrieval is to obtain the result data in one modality (e.g., image) which is semantically similar to the query data in another modality (e.g., text). In the fiel...

Deep learning techniques, especially convolutional neural networks, have boosted performance in analyzing and understanding remotely sensed images to a great extent. However, existing scene-classification methods generally neglect local and spatial information that is vital to scene classification of remotely sensed images. In this study, a method...

Measuring photosynthesis is critical for quantifying and modeling leaf to regional scale productivity of managed and natural ecosystems. This review explores existing and novel advances in photosynthesis measurements that are certain to provide innovative directions in plant science research. First, we address gas exchange approaches from leaf to e...

Improving photosynthesis is considered a promising way to increase crop yield to feed a growing population. Realizing this goal requires non-destructive techniques to quantify photosynthetic variation among crop cultivars. Despite existing remote sensing-based approaches, it remains a question whether solar-induced fluorescence (SIF) can facilitate...

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...

The Universal Thermal Climate Index (UTCI) is an important and frequently used indicator for evaluating thermal comfort. However, there is a lack of city-scale UTCI mapping over urban agglomerations during heat waves, and of studies of the variation of UTCI-based urban heat island intensities across cities. In addition, there is a need to evaluate...

Photosynthesis is currently measured using time laborious and/or destructive methods which slows research and breeding efforts to identify crop germplasm with higher photosynthetic capacities. We present a plot level screening tool for quantification of photosynthetic parameters and pigment contents that utilizes hyperspectral reflectance from sunl...

Rapidly and accurately obtaining soil organic carbon (SOC) maps can help understand farmland soil fertility and serves managing mineral fertilizers. The pedogenesis theory provides a fundamental basis for digital soil mapping by environmental factors. However, these soil forming factors may be insensitive to soil properties in low relief areas, suc...

The lack of efficient means to accurately infer photosynthetic traits constrains understanding global land carbon fluxes and improving photosynthetic pathways to increase crop yield. Here we investigated whether a hyperspectral imaging camera mounted on a mobile platform could provide the capability to help resolve these challenges, focusing on thr...

Abstract Satellite observations of anthropogenic carbon dioxide (CO2) emissions within urban settings offer unique potential to understand carbon sources and sinks and evaluate carbon mitigation strategies. Despite availability of column‐averaged dry air mole fraction of CO2 ( XCO2) from Orbiting Carbon Observatory‐2 (OCO‐2), temporal variations of...

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...

Global agriculture production is challenged by increasing demands from rising population and a changing climate, which may be alleviated through development of genetically improved crop cultivars. Research into increasing photosynthetic energy conversion efficiency has proposed many strategies to improve production but have yet to yield real-world...

Accurate digital mapping of soil organic carbon (SOC) is important in understanding the global carbon cycle and its implications in mitigating climate change. Visible and near-infrared hyperspectral imaging technology provides an alternative for mapping SOC efficiently and accurately, especially at regional and global scales. However, there is a la...

Spectroscopy is becoming an increasingly powerful tool to alleviate the challenges of traditional measurements of key plant traits at the leaf, canopy, and ecosystem scales. Spectroscopic methods often rely on statistical approaches to reduce data redundancy and enhance useful prediction of physiological traits. Given the mechanistic uncertainty of...

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. De...

Artificial nighttime lights (NTL) generated by human activities offer a unique opportunity to understand urban environments. Although previous studies have widely used NTL images to map urban extent at multiple scales, it remains a challenging task to address how NTL respond exactly to urbanization and thus to map urbanization from NTL. In this stu...

Due to its large spatial coverage and frequent revisit, satellite-derived land surface temperature (LST) has been recently used to explore annual temperature cycle (ATC) variations at regional and global scales. However, variability in seasonality of LSTs has not been examined in detail, particularly in urban areas where elevated temperatures are n...

The urban heat island (UHI) effect changes heat and water cycles in urban areas, and has been accused of elevating energy consumption, deteriorating living environment, and increasing mortality rates. Understanding various UHI effects necessitates a systematic modeling approach. A major problem in UHI simulations is that urban areas were either con...

In recent years, image annotation has attracted extensive attention due to the explosive growth of image data. With the capability of describing images at the semantic level, image annotation has many applications not only in image analysis and understanding but also in some relative disciplines, such as urban management and biomedical engineering....

The Indiana State University (ISU) Community Garden was initially created in 2008 with ~30 different gardeners. Since that time, the garden has expanded significantly and today houses over 150 plots that are cared for by over 125 local gardeners. The garden is located on a city block that was once residential property at the eastern edge of the ISU...

Along with rapid urbanization, nighttime activities from places, such as restaurants, pubs and bars, and theatres, have created enormous economic and social benefits. The nighttime economy (NTE), as a newly developed social phenomenon, has been used to describe economic activities at night. However, few studies have investigated urban nighttime eco...

The proliferation of impervious surfaces results in a series of environmental issues, such as the decrease of vegetated areas and the aggravation of the urban heat island effects. The mapping of impervious surface and its spatial distributions is of significance for the ecological study of urban environment. Currently, the integration of optical an...

Urbanization-associated land use and land cover (LULC) changes lead to modifications of surface microclimatic and hydrological conditions, including the formation of urban heat islands and changes in surface runoff pattern. The goal of the paper is to investigate the changes of biophysical variables due to urbanization induced LULC changes in India...

Recent years have witnessed an emerging concern of the health impact of heat waves. A common approach to investigate heat waves is to resort to the geostationary thermal infrared imagery, such as those from the Geostationary Operational Environmental Satellite (GOES) and Meteosat Second Generation. However, coarse spatial resolutions of geostationa...

Land surface temperature is a key parameter for monitoring urban heat islands, assessing heat related risks, and estimating building energy consumption. These environmental issues are characterized by high temporal variability. A possible solution from the remote sensing perspective is to utilize geostationary satellites images, for instance, image...

Land surface temperature (LST) is of primary importance in understanding global environment change, urban climatology, and land–atmosphere energy exchange. Analysis of long-term remotely sensed LST data remains a challenge for researchers. Most previous studies explored urban thermal pattern over space and time using a limited number of clear-sky i...

Data simulation has been widely used in various applications of remote sensing, especially in design of new type sensors, test of new developed algorithms and other associated applications. However, change of parameters of sensor and its system can affect the accuracy of data simulations. Based on spectral reconstruction, the present study employs...

In this paper, the content and distribution of soil organic matter (SOM) along a soil profile was predicted using two hyperspectral imaging cameras, a visible and near infrared (VNIR) camera (400-1000 nm) and a near infrared (NIR) camera (900-1700 nm). Three to five soil samples were taken from the different horizons of each soil profile. Each samp...