Sitemap
A list of all the posts and pages found on the site. For you robots out there, there is an XML version available for digesting as well.
Pages
Posts
Future Blog Post
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Blog Post number 4
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Blog Post number 3
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Blog Post number 2
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Blog Post number 1
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publications
Bidirectional reflectance effects over flat land surface from the charge-coupled device data sets of the HJ-1A and HJ-1B satellites
Published in Journal of Applied Remote Sensing, 2013
This study presents a method for extracting and normalizing hemispherical-directional reflectance factors (HDRFs) from HJ-1A and HJ-1B CCD imagery using a semiempirical BRDF model, demonstrating the significance of directional effects and the feasibility of HDRF normalization for environmental monitoring.
Recommended citation: F. Zhao, X. Gu, T. Yu, W. Verhoef, Y. Guo, Y. Du, H. Shang, and H. Zhao (2013). Bidirectional reflectance effects over flat land surface from the charge-coupled device data sets of the HJ-1A and HJ-1B satellites. Journal of Applied Remote Sensing, 7(1), 073466.
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Early detection of crop injury from glyphosate on soybean and cotton using plant leaf hyperspectral data
Published in Remote Sensing, 2014
This study explores the use of hyperspectral reflectance data and new features derived through canonical analysis to detect glyphosate injury in non-Glyphosate-Resistant soybean and cotton, showing improved early detection compared to traditional spectral indices.
Recommended citation: F. Zhao, Y. Huang, Y. Guo, K. N. Reddy, M. A. Lee, Reginald S. Fletcher, and Steven J. Thomson (2014). Early detection of crop injury from glyphosate on soybean and cotton using plant leaf hyperspectral data. Remote Sensing, 6(2), 1538–1563.
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Early detection of crop injury from herbicide glyphosate by leaf biochemical parameter inversion
Published in International Journal of Applied Earth Observation and Geoinformation, 2014
This study demonstrates the feasibility of using PROSPECT model inversion on leaf hyperspectral reflectance data to detect glyphosate-induced injury in non-Glyphosate-Resistant (non-GR) soybean and cotton within 48 hours of treatment by tracking changes in leaf chlorophyll content.
Recommended citation: F. Zhao, Y. Guo, Y. Huang, K. N. Reddy, M. A. Lee, R. S. Fletcher, and S. J. Thomson (2014). Early detection of crop injury from herbicide glyphosate by leaf biochemical parameter inversion. International Journal of Applied Earth Observation and Geoinformation, 31, 78–85.
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A method to reconstruct solar-induced canopy fluorescence spectrum from hyperspectral measurements
Published in Remote Sensing, 2014
This study proposes a Fluorescence Spectrum Reconstruction (FSR) method to retrieve the full spectrum of solar-induced canopy fluorescence (640–850 nm) using a spectral fitting approach and singular vector decomposition, with its accuracy being domostrated on simulated and experimental datasets.
Recommended citation: F. Zhao, Y. Guo, W. Verhoef, X. Gu, L. Liu, and G. Yang (2014). A method to reconstruct solar-induced canopy fluorescence spectrum from hyperspectral measurements. Remote Sensing, 6(10), 10171–10192.
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Simulated impact of sensor field of view and distance on field measurements of bidirectional reflectance factors for row crops
Published in Remote Sensing of Environment, 2015
This study uses a Monte Carlo simulation model to investigate the impact of sensor field of view (FOV) and distance on field-measured bidirectional reflectance factors (BRFs) in row crops, providing recommendations to minimize measurement bias.
Recommended citation: F. Zhao, Y. Li, X. Dai, W. Verhoef, Y. Guo, H. Shang, X. Gu, Y. Huang, T. Yu, and J. Huang (2015). Simulated impact of sensor field of view and distance on field measurements of bidirectional reflectance factors for row crops. Remote Sensing of Environment, 156, 129–142.
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Detection of the onset of glyphosate-induced soybean plant injury through chlorophyll fluorescence signal extraction and measurement
Published in Journal of Applied Remote Sensing, 2015
This study demonstrates that chlorophyll fluorescence (ChlF) measurements can effectively detect glyphosate-induced injury in soybean plants, with key fluorescence parameters showing significant differences between treatment groups as early as 24 to 48 hours after application.
Recommended citation: F. Zhao, Y. Guo, Y. Huang, K. N. Reddy, Y. Zhao, and W. T. Molin (2015). Detection of the onset of glyphosate-induced soybean plant injury through chlorophyll fluorescence signal extraction and measurement. Journal of Applied Remote Sensing, 9(1), 097098.
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Quantitative estimation of fluorescence parameters for crop leaves with Bayesian inversion
Published in Remote Sensing, 2015
This study measured backward and forward fluorescence radiance of crop leaves using a hyperspectral spectroradiometer, performed sensitivity analysis on the FluorMODleaf model, and applied Bayesian inversion to retrieve key fluorescence parameters.
Recommended citation: F. Zhao, Y. Guo, Y. Huang, W. Verhoef, C. van der Tol, B. Dai, L. Liu, H. Zhao, and G. Liu (2015). Quantitative estimation of fluorescence parameters for crop leaves with Bayesian inversion. Remote Sensing, 7(10), 14179–14199.
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FluorWPS: A Monte Carlo ray-tracing model to compute sun-induced chlorophyll fluorescence of three-dimensional canopy
Published in Remote Sensing of Environment, 2016
This study proposes and evaluates FluorWPS, a physically-based 3D radiative transfer (RT) model for simulating sun-induced chlorophyll fluorescence (SIF) using Monte Carlo ray tracing, demonstrating its accuracy in reproducing spectral and angular SIF distributions with high agreement to field measurements and an established RT model.
Recommended citation: F. Zhao, X. Dai, W. Verhoef, Y. Guo, C. van der Tol, Y. Li, and Y. Huang (2016). FluorWPS: A Monte Carlo ray-tracing model to compute sun-induced chlorophyll fluorescence of three-dimensional canopy. Remote Sensing of Environment, 187, 385–399.
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Superpixel-based adaptive kernel selection for angular effect normalization of remote sensing images with kernel learning
Published in IEEE Transactions on Geoscience and Remote Sensing, 2017
This study proposes a kernel learning approach for angular effect normalization in satellite reflectance data, enabling adaptive kernel selection at the superpixel level to improve image normalization across different land cover types.
Recommended citation: Y. Guo, X. Jia, and D. Paull (2017). Superpixel-based adaptive kernel selection for angular effect normalization of remote sensing images with kernel learning. IEEE Transactions on Geoscience and Remote Sensing, 55(8), 4262–4271.
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Effective sequential classifier training for SVM-based multitemporal remote sensing image classification
Published in IEEE Transactions on Image Processing, 2018
This study proposes a sequential classifier training approach (SCT-SVM) for multitemporal remote sensing image classification, leveraging classifiers from previous images to reduce training sample requirements and improve accuracy, demonstrating its effectiveness on Sentinel-2A data over an Australian agricultural area.
Recommended citation: Y. Guo, X. Jia, and D. Paull (2018). Effective sequential classifier training for SVM-based multitemporal remote sensing image classification. IEEE Transactions on Image Processing, 27(6), 3036–3048.
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Reconstruction of the full spectrum of solar-induced chlorophyll fluorescence: Intercomparison study for a novel method
Published in Remote Sensing of Environment, 2018
This study proposes an advanced Fluorescence Spectrum Reconstruction (aFSR) method to accurately reconstruct the full Solar-Induced Chlorophyll Fluorescence (SIF) spectrum, demonstrating its superiority over existing methods through comprehensive evaluations using simulated and experimental datasets.
Recommended citation: F. Zhao, R. Li, W. Verhoef, S. Cogliati, X. Liu, Y. Huang, Y. Guo, and J. Huang (2018). Reconstruction of the full spectrum of solar-induced chlorophyll fluorescence: Intercomparison study for a novel method. Remote Sensing of Environment, 219, 233–246.
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Nomination-favoured opinion pool for optical-SAR-synergistic rice mapping in face of weakened flooding signals
Published in ISPRS Journal of Photogrammetry and Remote Sensing, 2019
This study proposes an optical-SAR-synergistic approach for mapping paddy rice fields in Australia, addressing the challenge of weakened flooding signals due to direct drilling sowing methods, improving rice detection accuracy by combining optical and SAR data using a novel nomination-favoured opinion pool (NF-OP).
Recommended citation: Y. Guo, X. Jia, D. Paull, and J. A. Benediktsson (2019). Nomination-favoured opinion pool for optical-SAR-synergistic rice mapping in face of weakened flooding signals. ISPRS Journal of Photogrammetry and Remote Sensing, 155, 187–205.
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Activities of the IEEE GRSS University of New South Wales Canberra Student Chapter, Australia [Column Article]
Published in IEEE Geoscience and Remote Sensing Magazine, 2020
This column article focuses on the establishment and activities of the IEEE Geoscience and Remote Sensing Society UNSW Canberra Student Chapter.
Recommended citation: Y. Guo, J. Zhang, A. Farooq, X. Chen, and X. Jia (2020). Activities of the IEEE GRSS University of New South Wales Canberra Student Chapter, Australia [Column Article]. IEEE Geoscience and Remote Sensing Magazine, 8(3), 102–103.
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Plant species richness prediction from DESIS hyperspectral data: A comparison study on feature extraction procedures and regression models
Published in ISPRS Journal of Photogrammetry and Remote Sensing, 2023
This study evaluates the potential of spaceborne hyperspectral data from the DESIS instrument for predicting plant species richness in southeast Australia, comparing different feature extraction and regression methods to assess predictive performance.
Recommended citation: Y. Guo, K. Mokany, C. Ong, P. Moghadam, S. Ferrier, and S. R. Levick (2023). Plant species richness prediction from DESIS hyperspectral data: A comparison study on feature extraction procedures and regression models. ISPRS Journal of Photogrammetry and Remote Sensing, 196, 120–133.
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Global retrieval of the spectrum of terrestrial chlorophyll fluorescence: First results with TROPOMI
Published in Remote Sensing of Environment, 2024
This study presents a data-driven approach to reconstruct the terrestrial Solar-Induced Chlorophyll Fluorescence (SIF) spectrum from TROPOMI measurements, enabling better understanding of photosynthetic function and ecosystem dynamics through improved spatiotemporal SIF retrievals.
Recommended citation: F. Zhao, W. Ma, J. Zhao, Y. Guo, M. Tariq, and J. Li (2024). Global retrieval of the spectrum of terrestrial chlorophyll fluorescence: First results with TROPOMI. Remote Sensing of Environment, 300, 113903.
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Spatioformer: A geo-encoded transformer for large-scale plant species richness prediction
Published in IEEE Transactions on Geoscience and Remote Sensing, 2025
This study introduces Spatioformer, a model that integrates geolocation context with satellite imagery to predict plant species richness across large spatial scales, which is employed to map Australia’s plant diversity.
Recommended citation: Y. Guo, K. Mokany, S. R. Levick, J. Yang, and P. Moghadam (2025). Spatioformer: A geo-encoded transformer for large-scale plant species richness prediction. IEEE Transactions on Geoscience and Remote Sensing, 63, 4403216.
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talks
Talk 1 on Relevant Topic in Your Field
Published:
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Conference Proceeding talk 3 on Relevant Topic in Your Field
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This is a description of your conference proceedings talk, note the different field in type. You can put anything in this field.
teaching
Teaching experience 1
Undergraduate course, University 1, Department, 2014
This is a description of a teaching experience. You can use markdown like any other post.
Teaching experience 2
Workshop, University 1, Department, 2015
This is a description of a teaching experience. You can use markdown like any other post.