Peng Fu

Assistant Professor

Harrisburg University of Science and Technology

United States

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Peng Fu is an Assistant Professor at Harrisburg University of Science and Technology in the United States. His research interests focus on enhancing environmental sustainability and resilience through innovative techniques such as machine learning, reflectance spectroscopy, and remote sensing data analysis. Recent publications include studies on urban expansion's impact on vegetation carbon sequestration and advanced methods for monitoring soil organic carbon levels. Dr. Fu's work aims to improve resilience against environmental stresses while leveraging technology for better environmental management.

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Collaborators (6)

Leiqiu Hu

Associate Professor

University of Alabama in Huntsville

UNITED STATES

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Nuria Gomez-Casanovas

Assistant Professor

Texas A&M AgriLife

UNITED STATES

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Jochem Evers

Prof.

Wageningen University & Research

NETHERLANDS

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Weitian Tong

Tenure-track Assistant Professor

Georgia Southern University

UNITED STATES

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Carl Bernacchi

University of Illinois at Urbana-Champaign

UNITED STATES

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Stephen Long

University of Lancaster

UNITED KINGDOM

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Peng Fu

Research Interests

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About

Articles (22)

Enhancing plant resilience under combined stress: the role of reflectance spectroscopy

SSRMF: A sparse spectral reconstruction enhanced matched filter for improving point-source methane emission detection in complex terrain

Synergistic mapping of urban tree canopy height using ICESat-2 data and GF-2 imagery

Unraveling the impact of urban expansion on vegetation carbon sequestration capacity: A case study of the Yangtze River Economic Belt

Self-Sufficient Deep Learning Super-Resolution of MODIS Land Surface Temperature Data: Integrating Kernel-Based Regression and Transfer Learning

Pay for trees with carbon credits to deliver urban green spaces for all

Unmasking the sky: high-resolution PM<sub>2.5</sub> prediction in Texas using machine learning techniques

Causal inference reveals the dominant role of interannual variability of carbon sinks in complicated environmental-terrestrial ecosystems

A novel ensemble learning approach to extract urban impervious surface based on machine learning algorithms using SAR and optical data

Surface soil moisture from combined active and passive microwave observations: Integrating ASCAT and SMAP observations based on machine learning approaches

Accurate Quantification of 0–30 cm Soil Organic Carbon in Croplands over the Continental United States Using Machine Learning

Leaf, plant, to canopy: A mechanistic study on aboveground plasticity and plant density within a maize–soybean intercrop system for the Midwest, USA

Generate verifiable soil carbon credits from croplands

Urban Heat Islands Significantly Reduced by COVID‐19 Lockdown

Drought imprints on crops can reduce yield loss: Nature's insights for food security

Advances in field-based high-throughput photosynthetic phenotyping

Generating high-accuracy and cloud-free surface soil moisture at 1 km resolution by point-surface data fusion over the Southwestern U.S.

A Novel Fusion Method for Generating Surface Soil Moisture Data With High Accuracy, High Spatial Resolution, and High Spatio‐Temporal Continuity

Enhanced drought resistance of vegetation growth in cities due to urban heat, CO2 domes and O3 troughs

Reconciling the inconsistency of annual temperature cycles modelled from Landsat and MODIS LSTs through a percentile approach

Exploring influence factors in mapping soil organic carbon on low-relief agricultural lands using time series of remote sensing data

The inverse relationship between solar-induced fluorescence yield and photosynthetic capacity: benefits for field phenotyping

Collaborators (6)

Leiqiu Hu

Nuria Gomez-Casanovas

Jochem Evers

Weitian Tong

Carl Bernacchi

Stephen Long

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