I am a researcher in remote sensing with expertise in vegetation monitoring, currently a research associate in the Ecosystem Dynamics Health and Resilience research program within the School of Life Sciences, Faculty of Science, UTS.
My main research interest is using satellite data for vegetation monitoring, including vegetation parameter retrieval, vegetation dynamics, landscape phenology processes, and their shifting seasonalities with climate variability. I also use airborne remote sensing data and field measurements to observe land surface responses and interactions with climate, land use activities, and major disturbance events.
Currently, my research involves using remote sensing and field measurements to understand the phenology patterns of the grassland across Australian landscapes, and to study the association among pollen, allergens, and human health. Alongside the path, I also develop vegetation phenology products using satellite data, to support researches and managements, e.g. ecosystem resilience to climate change, bushfire fuel accumulation, crop yields, airborne allergens, native vegetation condition, and agricultural management.
Can supervise: YES
- remote sensing
- vegetation phenology
- vegetation monitoring
- climate change
- environment monitoring
- air quality monitoring
GIS and Remote Sensing
Xie, Q, Huang, W & Kong, W 2017, Crop biophysical and biochemical parameter retrieval with quantitative remote sensing techniques, Science Press, Beijing, China.
Ding, Y, Zhang, H, Wang, Z, Xie, Q, Wang, Y, Liu, L & Hall, CC 2020, 'A Comparison of Estimating Crop Residue Cover from Sentinel-2 Data Using Empirical Regressions and Machine Learning Methods', REMOTE SENSING, vol. 12, no. 9.View/Download from: Publisher's site
Xing, N, Huang, W, Xie, Q, Shi, Y, Ye, H, Dong, Y, Wu, M, Sun, G & Jiao, Q 2020, 'A Transformed Triangular Vegetation Index for Estimating Winter Wheat Leaf Area Index', Remote Sensing, vol. 12, no. 1, pp. 16-16.View/Download from: Publisher's site
Leaf area index (LAI) is a key parameter in plant growth monitoring. For several decades, vegetation indices-based empirical method has been widely-accepted in LAI retrieval. A growing number of spectral indices have been proposed to tailor LAI estimations, however, saturation effect has long been an obstacle. In this paper, we classify the selected 14 vegetation indices into five groups according to their characteristics. In this study, we proposed a new index for LAI retrieval-transformed triangular vegetation index (TTVI), which replaces NIR and red bands of triangular vegetation index (TVI) into NIR and red-edge bands. All fifteen indices were calculated and analyzed with both hyperspectral and multispectral data. Best-fit models and k-fold cross-validation were conducted. The results showed that TTVI performed the best predictive power of LAI for both hyperspectral and multispectral data, and mitigated the saturation effect. The R2 and RMSE values were 0.60, 1.12; 0.59, 1.15, respectively. Besides, TTVI showed high estimation accuracy for sparse (LAI < 4) and dense canopies (LAI > 4). Our study provided the value of the Red-edge bands of the Sentinel-2 satellite sensors in crop LAI retrieval, and demonstrated that the new index TTVI is applicable to inverse LAI for both low-to-moderate and moderate-to-high vegetation cover.
Peng, D, Zhang, H, Liu, L, Huang, W, Huete, AR, Zhang, X, Wang, F, Yu, L, Xie, Q, Wang, C, Luo, S, Li, C & Zhang, B 2019, 'Estimating the aboveground biomass for planted forests based on stand age and environmental variables', Remote Sensing, vol. 11, no. 19.View/Download from: Publisher's site
© 2019 by the authors. Measuring forest aboveground biomass (AGB) at local to regional scales is critical to understanding their role in regional and global carbon cycles. The Three-North Shelterbelt Forest Program (TNSFP) is the largest ecological restoration project in the world, and has been ongoing for over 40 years. In this study, we developed models to estimate the planted forest aboveground biomass (PF_AGB) for Yulin, a typical area in the project. Surface reflectances in the study area from 1978 to 2013 were obtained from Landsat series images, and integrated forest z-scores were constructed to measure afforestation and the stand age of planted forest. Normalized difference vegetation index (NDVI) was combined with stand age to develop an initial model to estimate PF_AGB. We then developed additional models that added environment variables to our initial model, including climatic factors (average temperature, total precipitation, and total sunshine duration) and a topography factor (slope). The model which combined the total precipitation and slope greatly improved the accuracy of PF_AGB estimation compared to the initial model, indicating that the environmental variables related to water distribution indirectly affected the growth of the planted forest and the resulting AGB. Afforestation in the study area occurred mainly in the early 1980s and early 21st century, and the PF_AGB in 2003 was 2.3 times than that of 1998, since the fourth term TNSFP started in 2000. The PF_AGB in 2013 was about 3.33 times of that in 2003 because many young trees matured. The leave-one-out cross-validation (LOOCV) approach showed that our estimated PF_AGB had a significant correlation with field-measured data (correlation coefficient (r) = 0.89, p < 0.001, root mean square error (RMSE) = 6.79 t/ha). Our studies provided a method to estimate long time series PF_AGB using satellite repetitive measures, particularly for arid or semi-arid areas.
Xie, M, Wang, Z, Huete, A, Brown, LA, Wang, H, Xie, Q, Xu, X & Ding, Y 2019, 'Estimating peanut leaf chlorophyll content with dorsiventral leaf adjusted indices: Minimizing the impact of spectral differences between adaxial and abaxial leaf surfaces', Remote Sensing, vol. 11, no. 18.View/Download from: Publisher's site
© 2019 by the authors. Relatively little research has assessed the impact of spectral differences among dorsiventral leaves caused by leaf structure on leaf chlorophyll content (LCC) retrieval. Based on reflectance measured from peanut adaxial and abaxial leaves and LCC measurements, this study proposed a dorsiventral leaf adjusted ratio index (DLARI) to adjust dorsiventral leaf structure and improve LCC retrieval accuracy. Moreover, the modified Datt (MDATT) index, which was insensitive to leaves structure, was optimized for peanut plants. All possible wavelength combinations for the DLARI and MDATT formulae were evaluated. When reflectance from both sides were considered, the optimal combination for the MDATT formula was (R723 - R738)/(R723 - R722) with a cross-validation Rcv2 of 0.91 and RMSEcv of 3.53 μg/cm2. The DLARI formula provided the best performing indices, which were (R735 - R753)/(R715 - R819) for estimating LCC from the adaxial surface (Rcv2 = 0.96, RMSEcv = 2.37 μg/cm2) and (R732 - R754)/(R724 - R773) for estimating LCC from reflectance of both sides (Rcv2 = 0.94, RMSEcv = 2.81 μg/cm2). A comparison with published vegetation indices demonstrated that the published indices yielded reliable estimates of LCC from the adaxial surface but performed worse than DLARIs when both leaf sides were considered. This paper concludes that the DLARI is the most promising approach to estimate peanut LCC.
Xie, Q, Dash, J, Huete, A, Jiang, A, Yin, G, Ding, Y, Peng, D, Hall, CC, Brown, L, Shi, Y, Ye, H, Dong, Y & Huang, W 2019, 'Retrieval of crop biophysical parameters from Sentinel-2 remote sensing imagery', International Journal of Applied Earth Observation and Geoinformation, vol. 80, pp. 187-195.View/Download from: Publisher's site
Xie, Q, Dash, J, Huang, W, Peng, D, Qin, Q, Mortimer, H, Casa, R, Pignatti, S, Laneve, G, Pascucci, S, Dong, Y & Ye, H 2018, 'Vegetation Indices Combining the Red and Red-Edge Spectral Information for Leaf Area Index Retrieval', IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, vol. 11, no. 5, pp. 1482-1492.View/Download from: Publisher's site
Leaf area index (LAI) is a crucial biophysical variable for agroecosystems monitoring. Conventional vegetation indices (VIs) based on red and near infrared regions of the electromagnetic spectrum, such as the normalized difference vegetation index (NDVI), are commonly used to estimate the LAI. However, these indices commonly saturate at moderate-to-dense canopies (e.g., NDVI saturates when LAI exceeds three). Modified VIs have then been proposed to replace the typical red/green spectral region with the red-edge spectral region. One significant and often ignored aspect of this modification is that the reflectance in the red-edge spectral region is comparatively sensitive to chlorophyll content which is highly variable between different crops and different phenological states. In this study, three improved indices are proposed combining reflectance both in the red and red-edge spectral regions into the NDVI, the modified simple ratio index (MSR), and the green chlorophyll index (CIgreen) formula. These improved indices are termed NDVIred-RE (red and red-edge NDVI),MSRred-RE (red and red-edgeMSR index), and CIred-RE (red and red-edgeCI). The indices were tested using RapidEye images and in-situ data from campaigns at Maccarese Farm (Central Rome, Italy), in which four crop types at four different growth stages were measured.We investigated the predictive power of nine VIs for crop LAI estimation, including NDVI, MSR, and CIgreen; the red-edge modified indices: NDVIRed-edge, MSRRed-edge, and CIRed-edge (generally represented by VIRed-edge); and the newly improved indices: NDVIred-RE, MSRred-RE, andCIred-RE (generally represented byVIred-RE). The results show that VIred-RE improves the coefficient of determination (R2) for LAI estimation by 10% in comparison to VIRed-edge. The newly improved indices prove to be the powerful alternatives for the LAI estimation of crops with wide chlorophyll range, and may provide valuable information for satellites equipped with red-ed...
Yin, G, Li, A, Wu, C, Wang, J, Xie, Q, Zhang, Z, Nan, X, Jin, H, Bian, J & Lei, G 2018, 'Seamless Upscaling of the Field-Measured Grassland Aboveground Biomass Based on Gaussian Process Regression and Gap-Filled Landsat 8 OLI Reflectance', ISPRS International Journal of Geo-Information, vol. 7, no. 7, pp. 1-7.View/Download from: Publisher's site
The spatially explicit aboveground biomass (AGB) generated through upscaling field measurements is critical for carbon cycle simulation and optimized management of grasslands. However, the spatial gaps that exist in the optical remote sensing data, underutilization of the multispectral data cube and unavailability of uncertainty information hinder the generation of seamless and accurate AGB maps. This study proposes a novel framework to address the above challenges. The proposed framework filled the spatial gaps in the remote sensing data via the consistent adjustment of the climatology to actual observations (CACAO) method. Gaussian process regression (GPR) was used to fully exploit the multispectral data cube and generated the pixelwise uncertainty concurrent with the AGB estimation. A case study in a 100 km × 100 km area located in the Zoige Plateau, China was used to evaluate this framework. The results show that the CACAO method can fill almost all of the gaps, accounting for 93.1% of the study area, with satisfactory accuracy. The generated AGB map from the GPR was characterized by a relatively high accuracy (R2 = 0.64, RMSE = 48.13 g/m2) compared to vegetation index-derived ones, and was accompanied by a corresponding uncertainty map that provides a new source of information on the credibility of each pixel. This study demonstrates the potential of the joint use of gap-filling and machine-learning methods to generate spatially explicit AGB
Xie, Q, Huang, W, Zhang, B, Chen, P, Song, X, Pascucci, S, Pignatti, S, Laneve, G & Dong, Y 2016, 'Estimating Winter Wheat Leaf Area Index From Ground and Hyperspectral Observations Using Vegetation Indices', IEEE JOURNAL OF SELECTED TOPICS IN APPLIED EARTH OBSERVATIONS AND REMOTE SENSING, vol. 9, no. 2, pp. 771-780.View/Download from: Publisher's site
Wu, C, Huang, W, Yang, Q & Xie, Q 2015, 'Improved estimation of light use efficiency by removal of canopy structural effect from the photochemical reflectance index (PRI)', AGRICULTURE ECOSYSTEMS & ENVIRONMENT, vol. 199, pp. 333-338.View/Download from: Publisher's site
Xie, Q, Huang, W, Dash, J, Song, X, Huang, L, Zhao, J & Wang, R 2015, 'Evaluating the potential of vegetation indices for winter wheat LAI estimation under different fertilization and water conditions', ADVANCES IN SPACE RESEARCH, vol. 56, no. 11, pp. 2365-2373.View/Download from: Publisher's site
Xie, Q, Huang, W, Cai, S, Liang, D, Peng, D, Zhang, Q, Huang, L, Yang, G & Zhang, D 2014, 'Comparative Study on Remote Sensing Invertion Methods for Estimating Winter Wheat Leaf Area Index', Guang Pu Xue Yu Guang Pu Fen Xi/Spectroscopy and Spectral Analysis, vol. 2014, no. 5, pp. 1352-1356.View/Download from: Publisher's site
Xie, Q, Huang, W, Liang, D, Chen, P, Wu, C, Yang, G, Zhang, J, Huang, L & Zhang, D 2014, 'Leaf Area Index Estimation Using Vegetation Indices Derived From Airborne Hyperspectral Images in Winter Wheat', IEEE JOURNAL OF SELECTED TOPICS IN APPLIED EARTH OBSERVATIONS AND REMOTE SENSING, vol. 7, no. 8, pp. 3586-3594.View/Download from: Publisher's site
Xie, Q, Huang, W, Liang, D, Peng, D, Huang, L, Song, X, Zhang, D & Yang, G 2014, 'Research on Universality of Least Squares Support Vector Machine Method for Estimating Leaf Area Index of Winter Wheat', Guang Pu Xue Yu Guang Pu Fen Xi/Spectroscopy and Spectral Analysis, vol. 2014, no. 2.View/Download from: Publisher's site
Xie, Q, Liang, D & Huang, W 2014, 'Using least squares support vector machines to estimate time series leaf area index', Infrared and Laser Engineering, vol. 43, pp. 243-248.
Xie, Q, Liu, Y, Huete, A & Nguyen, H 2020, 'Multi-scale phenology from digital time-lapse camera to Sentinel-2 and MODIS over Australian pastures', Vienna, Austria.View/Download from: Publisher's site
Huete, A, Tran, NN, Nguyen, H, Xie, Q & Katelaris, C 2019, 'Forecasting Pollen Aerobiology with Modis EVI, Land Cover, and Phenology Using Machine Learning Tools', IGARSS 2019 - 2019 IEEE International Geoscience and Remote Sensing Symposium, IEEE International Geoscience and Remote Sensing Symposium, IEEE, Yokohama, Japan, pp. 5429-5432.View/Download from: Publisher's site
Grass pollens are a major source of aeroallergens globally, inducing allergic asthma and hay fever in up to 500 million people worldwide. Pollen forecasting research and methods are site-dependent and tend to be empirically derived composites of expert knowledge and weather data. In this study we utilize satellite-based information of landscape conditions and phenology to better discern and predict grass pollen evolution. We employed machine learning approaches to formulate and better understand relationships between landscape phenology and seasonal flowering-induced pollen concentrations. We show that machine learning approaches significantly improved pollen prediction capabilities and provided key information to better attribute changes in pollen counts driven by shifting ecological landscapes from climate change drivers.
Anniballe, R, Casa, R, Castaldi, F, Fascetti, F, Fusilli, F, Huang, W, Laneve, G, Marzialetti, P, Palombo, A, Pascucci, S, Pierdicca, N, Pignatti, S, Xie, Q, Santini, F, Silvestro, PC, Yang, H & Yang, G 2015, 'Sinergistic use of radar and optical data for agricultural data products assimilation: A case study in Central Italy', Proceedings of the 2015 IEEE International Geoscience and Remote Sensing Symposium (IGARSS), IEEE International Geoscience and Remote Sensing Symposium, IEEE, Milan, Italy.View/Download from: Publisher's site
The paper describes the preliminary results of the January-August 2015 multi-frequency EO data acquisition campaign conducted over the Maccarese (Central Italy) farm. From January to May radar Cosmo SkyMed Ping-Pong (HH-VV), RapidEye and ZY-3 multispectral VHR optical images, as well as in situ data, have been acquired to retrieve biophysical and/or bio-chemical characteristics of soil and crops. LAI trend has been analyzed and compared by using both polarimetric and optical retrieval algorithms while soil moisture measurements have been compared with the radar backscattering.
- Macquarie University, Australia
- Queensland University of Technology, Australia
- Terrestrial Ecosystem Research Network (TERN), Australia
- University of Southampton, UK
- Aerospace Information Research Institute, Chinese Academy of Sciences, China
- São Paulo State University, Brazil
- Ecological and Forestry Applications Research Centre (CREAF), Spain
- Southwest Jiaotong University, China
- Northeast Normal University, China