Xiuhong Li’s research while affiliated with Beijing Normal University and other places

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Publications (7)


Figure 7. Density scatterplots between the reconstructed and the cloud-free Sentinel-2 L2A NDVI series and the surface reflectance time series in 4 bands over the 10 typical vegetation samples in 2020. (a) NDVI; (b-e) surface reflectance in blue, green, red and NIR band, respectively.
Vegetation Species Samples Information.
The lowest RMSE_cv and corresponding R 2 of different N of typical vegetation samples.
The acceptable range of s of typical vegetation samples when N = 24.
The corresponding dates of the images selected in Area A and B under different cases.
Reconstruction of Sentinel-2 Image Time Series Using Google Earth Engine
  • Article
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September 2022

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863 Reads

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19 Citations

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Mengyao Li

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Xiuhong Li

Sentinel-2 NDVI and surface reflectance time series have been widely used in various geoscience research, but the data is deteriorated or missing due to the cloud contamination, so it is necessary to reconstruct the Sentinel-2 NDVI and surface reflectance time series. At present, there are few studies on reconstructing the Sentinel-2 NDVI or surface reflectance time series, and these existing reconstruction methods have some shortcomings. We proposed a new method to reconstruct the Sentinel-2 NDVI and surface reflectance time series using the penalized least-square regression based on discrete cosine transform (DCT-PLS) method. This method iteratively identifies cloud-contaminated NDVI over NDVI time series from the Sentinel-2 surface reflectance data by adjusting the weights. The NDVI and surface reflectance time series are then reconstructed from cloud-free NDVI and surface reflectance using the adjusted weights as constraints. We have made some improvements to the DCT-PLS method. First, the traditional discrete cosine transformation (DCT) in the DCT-PLS method is matrix generated from discrete and equally spaced data, we reconfigured the DCT formulas to adapt for irregular interval time series, and optimized the control parameters N and s according to the typical vegetation samples in China. Second, the DCT-PLS method was deployed in the Google Earth Engine (GEE) platform for the efficiency and convenience of data users. We used the DCT-PLS method to reconstruct the Sentinel-2 NDVI time series and surface reflectance time series in the blue, green, red, and near infrared (NIR) bands in typical vegetation samples and the Zhangjiakou and Hangzhou study area. We found that this method performed better than the SG filter method in reconstructing the NDVI time series, and can identify and reconstruct the contaminated NDVI as well as surface reflectance with low root mean square error (RMSE) and high coefficient of determination (R2). However, in cases of a long range of cloud contamination, or above water surface, it may be necessary to increase the control parameter s for a more stable performance. The GEE code is freely available online and the link is in the conclusions of this article, researchers are welcome to use this method to generate cloudless Sentinel-2 NDVI and surface reflectance time series with 10 m spatial resolution, which is convenient for landcover classification and many other types of research.

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Figure 7. As in Figure 6, except for evapotranspiration.
Figure 9. Spatial distribution of the rate of change in surface parameters: (a) Surface albedo, (b) evapotranspiration, (c) fraction vegetation coverage and the degree of significance, (d) surface albedo, (e) evapotranspiration, (f) fraction vegetation coverage in the MuUs desert from 2001 to 2020; the VSI stands for very significant increase; SI stands for significant increase; SSI stands for micro significant increase; NSI stands for no increase; NSD stands for no decrease; SSDs represent a slightly significant decrease; SD stands for significant decrease; VSD stands for very significant decrease.
Figure 12. The degree of importance of each influence factor (A stands for surface albedo; F stands for fraction vegetation coverage; S stands for snow cover; P stands for precipitation; T stands for Air temperature) on evapotranspiration and surface albedo in different seasons.
The datasets used in this study.
A Study of the Change in Surface Parameters during the Last Four Decades in the MuUs Desert Based on Remote Sensing Data

August 2022

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116 Reads

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2 Citations

As an important part of the Earth’s environmental system, sandy soils are particularly sensitive to changes in the climatic environment. As one of the four major desert regions in China, the MuUs desert has transformed from a desert to an oasis after more than half a century of ecological management. In this paper, we analyzed the spatial and temporal patterns of surface albedo, evapotranspiration, and fraction vegetation cover in the MuUs desert based on the Global Land Surface Satellite (GLASS) product with high spatial and temporal resolution and assessed the relationships between their variability and snow cover, air temperature, and precipitation. It is of great significance to understand the effect of desertification control and climate change after the conversion of land surface types in the MuUs region. The results show that the desertification control in the MuUs area has achieved remarkable results since 1982. The fraction vegetation coverage of the MuUs desert showed a significant increasing trend, with an interannual change rate of 1.32% each decade−1. The surface albedo of MuUs desert decreased significantly. Affected by vegetation and snow cover, it was lower in summer and higher in winter. The evapotranspiration showed a significant upward trend, higher in summer and lower in winter, which is significantly correlated with the changes in surface albedo, air temperature, and vegetation. In addition, the local-scale biophysical effects caused by vegetation change have influenced the climate of the MuUs region, manifested as the increase in precipitation and air temperature. In general, with the support of relevant policies and human construction projects, the overall ecological environment in the MuUs desert is developing in a good way.


A Taylor Expansion Algorithm for Spatial Downscaling of MODIS Land Surface Temperature

January 2022

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48 Reads

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5 Citations

IEEE Transactions on Geoscience and Remote Sensing

Land surface temperature (LST) with fine spatiotemporal resolution is a much-needed parameter in the earth’s surface system. The LST downscaling is an efficient way to improve the spatiotemporal resolution of LST and has been developed rapidly in recent years. Due to the simple operations and discernable effects of statistical regression and its extension algorithms, these algorithms have been widely researched. However, most statistical regression models assume scale invariance, which makes the downscaled LST inaccurate. This study analyzed the scale effect in the process of LST upscaling/downscaling, then proposed a new algorithm based on Taylor expansion for Moderate Resolution Imaging Spectroradiometer (MODIS) LST downscaling. The Taylor expansion algorithm estimates regression coefficients between LST and auxiliary parameters in the consistent scale. It is tested in three typical areas of different landscapes with different auxiliary parameters, and the results are significantly improved compared to the traditional algorithm. However, the new algorithm may introduce the temporal discrepancy between MODIS LST and empirical concavity factor ( S ), which is estimated with Landsat 8 data, into the downscaling procedure in some circumstances. To discuss the influence of temporal discrepancy, we designed three schemes for pairing MODIS and S and analyzed the downscaled results. The results show that the proposed algorithm got the best downscaled results when the MODIS LST acquired time is consistent with the time of S . When the time is inconsistent, the pairing scheme of a similar season gives better results than that of different seasons. The algorithm performs generally well so long as the spatial distribution of auxiliary parameters in the date of Landsat 8 acquisition is similar to the date of MODIS acquisition.


Downscaling Land Surface Temperature Based on Non-Linear Geographically Weighted Regressive Model over Urban Areas

April 2021

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489 Reads

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19 Citations

Land surface temperature (LST) is a vital physical parameter in geoscience research and plays a prominent role in surface and atmosphere interaction. Due to technical restrictions, the spatiotemporal resolution of satellite remote sensing LST data is relatively low, which limits the potential applications of these data. An LST downscaling algorithm can effectively alleviate this problem and endow the LST data with more spatial details. Considering the spatial nonstationarity, downscaling algorithms have been gradually developed from least square models to geographical models. The current geographical LST downscaling models only consider the linear relationship between LST and auxiliary parameters, whereas non-linear relationships are neglected. Our study addressed this issue by proposing an LST downscaling algorithm based on a non-linear geographically weighted regressive (NL-GWR) model and selected the optimal combination of parameters to downscale the spatial resolution of a moderate resolution imaging spectroradiometer (MODIS) LST from 1000 m to 100 m. We selected Jinan city in north China and Wuhan city in south China from different seasons as study areas and used Landsat 8 images as reference data to verify the downscaling LST. The results indicated that the NL-GWR model performed well in all the study areas with lower root mean square error (RMSE) and mean absolute error (MAE), rather than the linear model.


Improving MODIS Aerosol Estimates Over Land With the Surface BRDF Reflectances Using the 3-D Discrete Cosine Transform and RossThick-LiSparse Models

January 2021

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294 Reads

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8 Citations

IEEE Transactions on Geoscience and Remote Sensing

The retrieval of aerosol properties over land from satellite sensors has always been a challenge. At present, several different algorithms for retrieving aerosol optical depth (AOD) have been developed from different satellite sensors. While each algorithm has its own advantages, the accuracy of AOD retrieval still needs to be further improved. To improve the retrieval accuracy of aerosol algorithms, it is necessary to provide a better method to describe the surface properties. In the current study, a new aerosol retrieval algorithm for MODIS images at a high spatial resolution of 500 m is proposed based on a priori Bidirectional Reflectance Distribution Function (BRDF) shape parameters database, which is reconstructed via the three-dimensional discrete cosine transform (DCT-PLS) method. Then the surface reflectances are calculated from the BRDF model (i.e., RossThick-LiSparse), and a non-Lambertian forward model used to describe the surface anisotropy. The new algorithm is used for processing the MODIS over the Beijing-Tianjin-Hebei of China, and Southeastern United States of America regions and results are validated against AERONET AOD measurements as well as compared with the MODIS AOD products. The comparison showed that the estimation scheme of surface reflectance in this new algorithm significantly improved the AOD retrievals accuracy, with average correlation coefficient ~0.965, root-mean-square error ~0.125, the number of AOD retrievals falling within expected error has increased to ~80.1%, and the overestimation uncertainty has been reduced compared with MODIS products. Due to the high spatial resolution and continuous spatial distributions of the AOD retrievals by the new algorithm, therefore, it can well-captured aerosol details over mixed surfaces and better useful for air pollution studies than the MODIS products at local and urban scales. Index Terms-AOD, kernel-driven BRDF model, surface anisotropy, a priori knowledge.


Figure 1. Location of 37 sites in Beijing area. Background map is land cover-type dataset, which is provided by GlobeLand30.
Figure 2. Spatial patterns of kernel weights obtained from the CLSM retrievals and MCD43A1 products (only pixels marked as best quality) over Beijing region on 24 February 2014. (a, d) isotropic kernel weight, (b, e) volumetric kernel weight, (c, f) geometric kernel weight.
Figure 3. Temporal trajectories of kernel weights obtained from the CLSM and MCD43A1 products over different land cover types. (a) isotropic kernel weight, (b) volumetric kernel weight, and (c) geometric kernel weight.
Estimation of BRDF model kernel weights under an a priori knowledge-aided constraint

January 2021

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168 Reads

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1 Citation

Remote Sensing Letters

The reflectance anisotropy of land surface serves as an important bridge between surface biophysical parameters and remote sensing observations. It can characterize by the linear kernel-driven bidirectional reflectance distribution function (BRDF), which is the combination of several kernel functions and kernel weights. These kernel weights can be estimated by remote sensing ; however, the stability of current kernel weights products is still challenging, especially in urban areas with complex aerosol properties and heterogeneous surfaces. In this paper, we propose a method for robust estimation of kernel weights from the Moderate Resolution Imaging Spectroradiometer (MODIS) surface spectral reflectance products (MxD09GA) data based on the constrained least-squares method (CLSM) and a priori knowledge. The kernel weights data were obtained by the CLSM from 2014 to 2017 in Beijing region of China. Validations were carried out using the MxD09GA and BRDF/Albedo products (MCD43A1). The results show that the time series of kernel weights by the CLSM show small variability over different land cover types. The kernel weights estimated by the CLSM can clearly show the phenological signal and fitting ability of surface spectral reflectance is better than that of the MCD43A1 products in Beijing urban area. Experimental results demonstrate that the CLSM has the potential for the robust estimation of kernel weights in urban areas.


Validation and Comparison of MODIS C6.1 and C6 Aerosol Products over Beijing, China

December 2018

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360 Reads

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37 Citations

The operational Moderate Resolution Imaging Spectroradiometer (MODIS) Aerosol Products (APs) have provided long-term and wide-spatial-coverage aerosol optical properties across the globe, such as aerosol optical depth (AOD). However, the performance of the latest Collection 6.1 (C6.1) of MODIS APs is still unclear over urban areas that feature complex surface characteristics and aerosol models. The aim of this study was to validate and compare the performance of the MODIS C6.1 and C6 APs (MxD04, x = O for Terra, x = Y for Aqua) over Beijing, China. The results of the Dark Target (DT) and Deep Blue (DB) algorithms were validated against Aerosol Robotic Network (AERONET) ground-based observations at local sites. The retrieval uncertainties and accuracies were evaluated using the expected error (EE: ±0.05 + 15%) and the rootmean-square error (RMSE). It was found that the MODIS C6.1 DT products performed better than the C6 DT products, with a greater percentage (by about 13%–14%) of the retrievals falling within the EE. However, the DT retrievals collected from two collections were significantly overestimated in the Beijing region, with more than 64% and 48% of the samples falling above the EE for the Terra and Aqua satellites, respectively. The MODIS C6.1 DB products performed similarly to the C6 DB products, with 70%–73% of the retrievals matching within the EE and estimation uncertainties. Moreover, the DB algorithm performed much better than DT algorithm over urban areas, especially in winter where abundant missing pixels were found in DT products. To investigate the effects of factors on AOD retrievals, the variability in the assumed surface reflectance and the main optical properties applied in DT and DB algorithms are also analyzed.

Citations (6)


... Our analysis used 10 m resolution Sentinel-2 data from 2022 and 0.5 m very high-resolution Worldview and GeoEye data from 2021 (figure 2) (see Supplemental Methods for details on data, models, and methods). All available analysis-ready Sentinel 2 VINR Level 2 data were retrieved from the Google Earth Engine catalog for the year 2022 (Phiri et al 2020, Yang et al 2022. Clouds were detected and removed. ...

Reference:

Measuring the extent of trees outside of forests: a nature-based solution for net zero emissions in South Asia
Reconstruction of Sentinel-2 Image Time Series Using Google Earth Engine

... According to Yang et al. [124], seasonality significantly impacts FVC. For instance, in summer, the correlation with LST is expected to increase [125]. In addition, Amiri et al. [28] examined the association between land surface temperature and fractional vegetation cover and established that higher FVC corresponds to relatively lower LST values. ...

A Study of the Change in Surface Parameters during the Last Four Decades in the MuUs Desert Based on Remote Sensing Data

... The BNGR models the linear or nonlinear relationship between LSTs and kernels in the form of implicit functions, rather than a predetermined specific functional form, to predict the parameter interdependencies. Considering the primary land cover types across the urban area, three surface biophysical indices (i.e., NDVI, NDBI and MNDWI, see Table 1) were selected as candidate kernels in the N-DLST method, since their dominant effects on driving urban thermal environment Peng et al., 2018) and efficient performance in LST prediction have been extensively summarized (Wang et al., 2022;Wu et al., 2019;. ...

A Taylor Expansion Algorithm for Spatial Downscaling of MODIS Land Surface Temperature
  • Citing Article
  • January 2022

IEEE Transactions on Geoscience and Remote Sensing

... By establishing a radiative transfer equation, the algorithm inverts the radiance brightness temperature of a single band or window, providing an estimate of LST. In recent years, many researchers have adopted this method for LST inversion to ensure data accuracy and stability [49][50][51]. ...

Downscaling Land Surface Temperature Based on Non-Linear Geographically Weighted Regressive Model over Urban Areas

... Typical satellite sensors are available for obtaining AOD, including the Advanced Very High Resolution Radiometer (AVHRR) (Gao et al., 2016;Tian et al., 2022), the Moderate Resolution Imaging Spectroradiometer (MODIS) (R.C. Levy et al., 2013;Tian et al., 2021), the Medium Resolution Spectral Imager (MERSI) (Fan et al., 2020;Jin et al., 2021), and the Advanced Himawari Imager (AHI) (Ge et al., 2019;Zhang et al., 2019). Nevertheless, most of the above-mentioned sensors cannot provide nighttime light images with high light sensitivity, making them suitable only for daytime observation. ...

Improving MODIS Aerosol Estimates Over Land With the Surface BRDF Reflectances Using the 3-D Discrete Cosine Transform and RossThick-LiSparse Models

IEEE Transactions on Geoscience and Remote Sensing

... than the urban site (*1.26-1.43). The accuracy of the MODIS retrievals is affected by two main parameters, including the estimation of the surface reflectance and the aerosol model, which can be caused underestimation during both polluted and clear conditions (Tian et al. 2018). Our previous study showed that highly absorbing aerosols also caused overestimation over the urban sites . ...

Validation and Comparison of MODIS C6.1 and C6 Aerosol Products over Beijing, China