Liujun Zhu

Liujun Zhu
Hohai University · Yangtze Institute for Conservation and Development

PhD

About

35
Publications
7,735
Reads
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429
Citations
Citations since 2016
29 Research Items
421 Citations
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2016201720182019202020212022020406080100
2016201720182019202020212022020406080100
2016201720182019202020212022020406080100
Introduction
• High spatial resolution soil moisture retrieval from microwave remote sensing; •Calibration and validation of airborne radar system; • Machine learning and its application in remote sensing including image classification, change detection and soil moisture retrieval.
Additional affiliations
August 2019 - August 2023
Monash University (Australia)
Position
  • Research Fellow and Adjunct Research Fellow
September 2017 - February 2018
University of Michigan
Position
  • Visiting student
Description
  • Working on vegetation scattering model for radar soil moisture retrieval
Education
September 2012 - July 2015
Nanjing University
Field of study
  • Remote sensing

Publications

Publications (35)
Article
Multi-temporal analysis has been widely acknowledged as a promising method to derive soil moisture from radar backscatter observations. The method assumes that only soil moisture varies in the period of interest, while all other parameters such as vegetation water content and soil surface roughness are sufficiently time invariant. However, this ass...
Article
Full-text available
Multi-angular and multi-temporal methods have been developed and accepted as two promising strategies for reliable soil moisture retrieval from radar data. However, the way to combine time series multi-angular data acquired from both descending and ascending orbits with different imaging modes (e.g., ScanSAR and Stripmap) remains unresolved. Conseq...
Article
Full-text available
The increased availability of spaceborne radar data projected over the next decade provides a great opportunity for operational soil moisture mapping with high spatial (< 50 m) and temporal (< 3 days) resolution, by combining the data from multiple SAR missions. Accordingly, a multi-frequency soil moisture retrieval framework has been proposed, bei...
Article
Full-text available
The recent and projected investments across the world on radar satellite missions (e.g., Sentinel-1, SAOCOM, BIOMASS and NISAR) provide a great opportunity for operational radar soil moisture mapping with high spatial and temporal resolution. However, there is no retrieval algorithm that can make complementary use of the multi-frequency data from t...
Article
Full-text available
Abstract: The feasibility of soil moisture retrieval from C-band Sentinel-1 data has been widely acknowledged, with pre-operational 1-km products currently available at regional and/or continental scale using the long-term (LTCD) or short-term change detection (STCD) methods. Both algorithms share the same assumptions of time-invariant roughness an...
Article
Full-text available
Hydraulic connectivity has great effects on water quality. Enclosure aquaculture can largely alter lake flow regime and thus deteriorate water quality. Understanding the dynamics and influencing factors of water quality in enclosure aquaculture lakes is of great significance to ecosystem restoration of degraded lakes. However, it remains challengin...
Article
Snow density is one of the important indicators of snow cover hydrological potential. The application of existing algorithms for retrieving dry snow density using synthetic aperture radar (SAR) data is limited by single scattering mechanism, small terrain fluctuation or narrow incidence angle range. In the study, an improved approach was proposed t...
Article
Live fuel moisture content (LFMC) is an important environmental indicator used to measure vegetation conditions and monitor for high fire risk conditions. However, LFMC is challenging to measure on a wide scale, thus reliable models for estimating LFMC are needed. Therefore, this paper proposes a new deep learning architecture for LFMC estimation....
Article
Full-text available
It has been over ten years since the successful launch of the first-ever dedicated satellite for global soil moisture monitoring; Soil Moisture and Ocean Salinity (SMOS). Looking towards the future, P-band (0.3–1 GHz) is a promising technique to replace or enhance the L-band (1.4 GHz) SMOS and SMAP (Soil Moisture Active Passive) missions because of...
Article
Full-text available
L-band passive microwave remote sensing is currently considered a robust technique for global monitoring of soil moisture. However, soil roughness complicates the relationship between brightness temperature and soil moisture, with current soil moisture retrieval algorithms typically assuming a constant roughness parameter globally, leading to a pot...
Article
Full-text available
Live fuel moisture content (LFMC) is an essential variable to model fire danger and behaviour. This paper presents the first application of deep learning to LFMC estimation based on the historical LFMC ground samples of the Globe-LFMC database, as a step towards operational daily LFMC mapping in the Contiguous United States (CONUS). One-year MODera...
Article
Full-text available
The moisture retrieval depth is commonly held to be the approximately top 5 cm at L-band (~21-cm wavelength/1.41 GHz), which is seen as a limitation for hydrological applications. A widely held view is that this moisture retrieval depth increases with wavelength, ranging approximately from one-tenth to one-fourth of the wavelength. Accordingly, P-b...
Article
Full-text available
The National Aeronautics and Space Administration (NASA) Soil Moisture Active Passive (SMAP) mission was launched on 31st January 2015, with the aim of providing global soil moisture maps at 9 km spatial resolution by combining L-band radar and radiometer observations. However, after the SMAP radar became inoperable, NASA decided to utilize the Sen...
Article
The fourth and fifth Soil Moisture Active Passive Experiments (SMAPEx-4 and -5) were conducted at the beginning of the SMAP operational phase, May and September 2015, to: 1) evaluate the SMAP microwave observations and derived soil moisture (SM) products and 2) intercompare with the Soil Moisture and Ocean Salinity (SMOS) and Aquarius missions over...
Article
Full-text available
Various forward models have been developed for measuring agricultural biophysical parameters from microwave or optical data, but their application to periodic surfaces (e.g. periodic crop rows and ploughed soil rows) still suffers from the need of row-feature descriptions. Accordingly, an operational method was proposed herein to estimate the orien...
Article
The traditional in situ data based temperature-humidity indexes (THIs) have been widely used in the assessment of the quality of urban thermal environment, with the spatial details of thermal comfort currently unavailable. In this study, the THI is modified replacing the required in situ air temperature and relative humidity with remote sensing ret...
Presentation
The SMAP satellite, launched on 31st January 2015, was aimed to provide global high resolution soil moisture maps at 9km spatial resolution by downscaling 36 km L-band radiometer brightness temperature observations using 3 km L-band radar backscatter observations, then retrieving soil moisture from the resulting downscaled brightness temperature. H...
Article
High-spatial and -temporal resolution snow cover products in mountain areas are important to hydrological applications. The GF-1 satellite provides multispectral images with 8-m resolution and a revisit up to 2 days, which makes it possible to produce snow cover products. However, it is challenging to extract snow cover from these images because of...
Article
The polarimetric L-band imaging synthetic aperture radar (PLIS) is a high spatial resolution (better than 6 m) airborne synthetic aperture radar system that has been dedicated to scientific research into civilian applications since 2010. The weight of PLIS is ∼38 kg, allowing it to be installed aboard small low-cost aircraft, with two antennas used...
Article
Most of current products can partially reach the requirement of high spatial and temporal resolution needed in urban applications. Fortunately, the new generation of satellite in a form of constellation, e.g. Europe’s Sentinel-2, China’s HJ-1A/B and GF-1/6, is expected to provide more frequent observations (<1 week) with a higher spatial resolution...
Article
Full-text available
High-spatial and -temporal resolution snow cover maps for mountain areas are needed for hydrological applications and snow hazard monitoring. The Chinese GF-1 satellite is potential to provide such information with a spatial resolution of 8 m and a revisit of 4 days. The main challenge for the extraction of multi-temporal snow cover from high-spati...
Conference Paper
Soil moisture retrieval from Synthetic Aperture Radar (SAR) over bare soil using the Integral Equation Model (IEM) has not been fully operational for natural conditions, due mainly to the failure of roughness parameterization. Recently, increasing interest has been drawn to improve soil moisture retrieval accuracy of the IEM by introducing an effec...
Article
Full-text available
HJ-1A/B NDVI (HJ NDVI) time-series data possess relatively high spatio-temporal resolution which is significant for the research on urban areas. However, its application is hindered by noise resulting from the restrictions of imaging quality and limits of the satellite platform. The NDVI noise reduction is necessary. Some noise-reduction techniques...
Conference Paper
HJ-1A/B NDVI (HJ NDVI) time-series data possess relatively high spatio-temporal resolution which is significant for the research on urban areas. However, its application is hindered by noise resulting from the restrictions of imaging quality and limits of the satellite platform. The NDVI noise reduction is necessary. Some noise-reduction techniques...
Article
Promoting the accuracy of hyperspectral image classification is a crucial and complex issue. Hyperspectral image provides details of spectral variation of land surface with continuous spectral data. On the one hand, this characteristic is widely utilized to analyze and interpret different land-cover classes. On the other hand, the availability of l...
Conference Paper
Urban vegetation classification need vegetation index especially temporal information of vegetation, thus high spatio-temporal NDVI product is necessary. NDVI time-series data derived from HJ 1A/B time series imagery (HJ NDVI) have relatively high spatio-temporal resolution. In this research, HJ NDVI time series of typical vegetation types in the c...
Article
Full-text available
Segmentation of remote sensing images is a critical step in geographic object-based image analysis. Evaluating the performance of segmentation algorithms is essential to identify effective segmentation methods and optimize their parameters. In this study, we propose region-based precision and recall measures and use them to compare two image partit...
Article
Full-text available
Compared to the urban sprawl in western countries, China’s version varies, although it retains a dispersed development pattern, which is an important feature. A new framework is provided in this paper to allow for a deeper understanding of China’s spatio-temporal sprawl patterns along its urban fringes. In this framework, Shannon’s entropy analysis...
Article
Full-text available
Snow cover extraction in mountain areas is a complex task, especially from high spatial resolution remote sensing (HSRRS) data. The influence of mountain shadows in HSRRS is severe and normalized difference snow index-based snow cover extraction methods are inaccessible. A decision tree building method for snow cover extraction (DTSE) integrated wi...
Article
Full-text available
Due to the shortage of single-scale accuracy assessment methods, this paper proposes a multi-scale accuracy assessment method based on histo-variograms, which assesses the accuracy of land cover datasets on a pixel and sub-pixel scale. On a pixel scale, a standing-pixel is introduced as the sample tool to accurately assess datasets directly, wherea...

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