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Shengbo Chen

Bio: Shengbo Chen is an academic researcher from Jilin University. The author has contributed to research in topics: Computer science & Hyperspectral imaging. The author has an hindex of 8, co-authored 38 publications receiving 167 citations. Previous affiliations of Shengbo Chen include Center for Excellence in Education & Macau University of Science and Technology.

Papers
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Journal ArticleDOI
TL;DR: A colour mixture analysis (CMA) method based on the Hue-Saturation-Value (HSV) colour space is proposed, thereby improving the accuracy and efficiency of FVC estimation from UAV-captured RGB images and is shown to be superior to that of all three algorithms.
Abstract: Remote sensing via unmanned aerial vehicles (UAVs) is becoming a very important tool for augmenting traditional spaceborne and airborne remote sensing techniques. Commercial RGB cameras are often the payload on UAVs, because they are inexpensive, easy to operate and require little data processing. RGB images are increasingly being used for mapping of fractional vegetation cover (FVC). However, the presence of significantly mixed pixels in close-range RGB images prevents the accurate estimation of FVC. Even where pixel unmixing is applied, limited quantitative spectral information and colour variability within these images could lead to profound errors and uncertainties. This paper proposes a colour mixture analysis (CMA) method based on the Hue-Saturation-Value (HSV) colour space to alleviate the above-mentioned concerns, thereby improving the accuracy and efficiency of FVC estimation from UAV-captured RGB images. First, the a priori colour information of the pure vegetation and background endmembers are extracted from the Hue channel of the UAV proximal sensing images, obviating ground-based image capture and the attendant cost and inconvenience. Second, the relationship between the probability distribution of mixed pixels and that of the two endmembers is estimated. Finally, we estimate FVC from UAV remote sensing images with a maximum a posteriori parameter (MAP) estimator. Two UAV-captured RGB image datasets and a synthetic RGB image dataset were used to test the new method. CMA was compared with three other FVC estimation algorithms, namely, FCLS, HAGFVC and LAB2. The FVC estimates by CMA were found to be highly accurate, with root mean squared errors (RMSE) of less than 0.007 and mean absolute error (MAE) of less than 0.01 for both field datasets. The accuracy was shown to be superior to that of all three algorithms. A comprehensive analysis of the estimation accuracy under various spatial resolutions and vegetation cover levels was conducted using both field and synthetic datasets. Results show that the CMA method can robustly and accurately estimate FVC across the full range of vegetation coverage and various resolutions. Uncertainty and sensitivity analysis of colour variability due to heterogeneity and shadow were also tested. Overall, CMA was shown to be robust to variation in colour and illumination.

50 citations

Journal ArticleDOI
01 Nov 2020-Heliyon
TL;DR: It is concluded that integration of Landsat 8 and Sentinel-1, either speckle filtered or unfiltered, improves crop classification and speckles do not have statistically significant effects.

26 citations

Journal ArticleDOI
TL;DR: The conclusion is that both decision-level and pixel-level fusion approaches produced comparable classification results, and either of the procedures can be adopted in areas with inescapable cloud problems for updating crop inventories and acreage estimation at regional scales.
Abstract: Crops mapping unequivocally becomes a daunting task in humid, tropical, or subtropical regions due to unattainability of adequate cloud-free optical imagery. Objective of this study is to evaluate the comparative performance between decision- and pixel-levels data fusion ensemble classified maps using Landsat 8, Landsat 7, and Sentinel-2 data. This research implements parallel and concatenation approach to ensemble classify the images. The multiclassifier system comprises of Maximum Likelihood, Support Vector Machines, and Spectral Information Divergence as base classifiers. Decision-level fusion is achieved by implementing plurality voting method. Pixel-level fusion is achieved by implementing fusion by mosaicking approach, thus appending cloud-free pixels from either Sentinel-2 or Landsat 7. The comparison is based on the assessment of classification accuracy. Overall accuracy results show that decision-level fusion achieved an accuracy of 85.4%, whereas pixel-level fusion classification attained 82.5%, but their respective kappa coefficients of 0.84 and 0.80 but are not significantly different according to Z-test at $\alpha = {\text{0.05}}$ . F1-score values reveal that decision-level performed better on most individual classes than pixel-level. Regression coefficient between planted areas from both approaches is 0.99. However, Support Vector Machines performed the best of the three classifiers. The conclusion is that both decision-level and pixel-level fusion approaches produced comparable classification results. Therefore, either of the procedures can be adopted in areas with inescapable cloud problems for updating crop inventories and acreage estimation at regional scales. Future work can focus on performing more comparison tests on different areas, run tests using different multiclassifier systems, and use different imagery.

23 citations

Journal ArticleDOI
TL;DR: This work proposes a two-step validation method for rugged terrains based on computer simulations for downward shortwave radiation (DSR) with an advantage of using small amount of ground stations to upscale DSR with relatively high accuracy overrugged terrains.
Abstract: Estimation of downward shortwave radiation (DSR) is of great importance in global energy budget and climatic modeling. Although various algorithms have been proposed, effective validation methods are absent for rugged terrains due to the lack of rigorous methodology and reliable field measurements. We propose a two-step validation method for rugged terrains based on computer simulations. The first step is to perform point-to-point validation at local scale. Time-series measurements were applied to evaluate a three-dimensional (3-D) radiative transfer model. The second step is to validate the DSR at pixel-scale. A semiempirical model was built up to interpolate and upscale the DSR. Key terrain parameters were weighted by empirical coefficients retrieved from ground-based observations. The optimum number and locations of ground stations were designed by the 3-D radiative transfer model and Monte Carlo method. Four ground stations were selected to upscale the ground-based observations. Additional three ground stations were set up to validate the interpolated results. The upscaled DSR was finally applied to validate the satellite products provided by MODIS and Himawari-8. The results showed that the modeled and observed DSR exhibited good consistency at point scale with correlation coefficients exceeding 0.995. The average error was around 20 W/m2 for the interpolated DSR and 10 W/m2 for the upscaled DSR in theory. The accuracies of the satellite products were acceptable at most times, with correlation coefficients exceeding 0.94. From an operational point of view, our method has an advantage of using small amount of ground stations to upscale DSR with relatively high accuracy over rugged terrains.

20 citations

Journal ArticleDOI
17 Mar 2021-Sensors
TL;DR: In this article, a linear regression model is applied to evaluate four VIs that are commonly used to estimate LAI: normalized difference vegetation index (NDVI), soil adjusted vegetation index, transformed SAVI (TSAVI), and enhanced vegetation index(EVI).
Abstract: Saturation effects limit the application of vegetation indices (VIs) in dense vegetation areas. The possibility to mitigate them by adopting a negative soil adjustment factor X is addressed. Two leaf area index (LAI) data sets are analyzed using the Google Earth Engine (GEE) for validation. The first one is derived from observations of MODerate resolution Imaging Spectroradiometer (MODIS) from 16 April 2013, to 21 October 2020, in the Apiacas area. Its corresponding VIs are calculated from a combination of Sentinel-2 and Landsat-8 surface reflectance products. The second one is a global LAI dataset with VIs calculated from Landsat-5 surface reflectance products. A linear regression model is applied to both datasets to evaluate four VIs that are commonly used to estimate LAI: normalized difference vegetation index (NDVI), soil adjusted vegetation index (SAVI), transformed SAVI (TSAVI), and enhanced vegetation index (EVI). The optimal soil adjustment factor of SAVI for LAI estimation is determined using an exhaustive search. The Dickey-Fuller test indicates that the time series of LAI data are stable with a confidence level of 99%. The linear regression results stress significant saturation effects in all VIs. Finally, the exhaustive searching results show that a negative soil adjustment factor of SAVI can mitigate the SAVIs' saturation in the Apiacas area (i.e., X = -0.148 for mean LAI = 5.35), and more generally in areas with large LAI values (e.g., X = -0.183 for mean LAI = 6.72). Our study further confirms that the lower boundary of the soil adjustment factor can be negative and that using a negative soil adjustment factor improves the computation of time series of LAI.

18 citations


Cited by
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10 Jul 1986
TL;DR: In this paper, a multispectral image was modeled as mixtures of reflectance spectra of palagonite dust, gray andesitelike rock, and a coarse rock-like soil.
Abstract: A Viking Lander 1 image was modeled as mixtures of reflectance spectra of palagonite dust, gray andesitelike rock, and a coarse rocklike soil. The rocks are covered to varying degrees by dust but otherwise appear unweathered. Rocklike soil occurs as lag deposits in deflation zones around stones and on top of a drift and as a layer in a trench dug by the lander. This soil probably is derived from the rocks by wind abrasion and/or spallation. Dust is the major component of the soil and covers most of the surface. The dust is unrelated spectrally to the rock but is equivalent to the global-scale dust observed telescopically. A new method was developed to model a multispectral image as mixtures of end-member spectra and to compare image spectra directly with laboratory reference spectra. The method for the first time uses shade and secondary illumination effects as spectral end-members; thus the effects of topography and illumination on all scales can be isolated or removed. The image was calibrated absolutely from the laboratory spectra, in close agreement with direct calibrations. The method has broad applications to interpreting multispectral images, including satellite images.

1,107 citations

Journal ArticleDOI

674 citations

Journal ArticleDOI
TL;DR: An overview of remote sensing systems, techniques, and vegetation indices along with their recent (2015–2020) applications in Precision agriculture is provided.
Abstract: Agriculture provides for the most basic needs of humankind: food and fiber. The introduction of new farming techniques in the past century (e.g., during the Green Revolution) has helped agriculture keep pace with growing demands for food and other agricultural products. However, further increases in food demand, a growing population, and rising income levels are likely to put additional strain on natural resources. With growing recognition of the negative impacts of agriculture on the environment, new techniques and approaches should be able to meet future food demands while maintaining or reducing the environmental footprint of agriculture. Emerging technologies, such as geospatial technologies, Internet of Things (IoT), Big Data analysis, and artificial intelligence (AI), could be utilized to make informed management decisions aimed to increase crop production. Precision agriculture (PA) entails the application of a suite of such technologies to optimize agricultural inputs to increase agricultural production and reduce input losses. Use of remote sensing technologies for PA has increased rapidly during the past few decades. The unprecedented availability of high resolution (spatial, spectral and temporal) satellite images has promoted the use of remote sensing in many PA applications, including crop monitoring, irrigation management, nutrient application, disease and pest management, and yield prediction. In this paper, we provide an overview of remote sensing systems, techniques, and vegetation indices along with their recent (2015–2020) applications in PA. Remote-sensing-based PA technologies such as variable fertilizer rate application technology in Green Seeker and Crop Circle have already been incorporated in commercial agriculture. Use of unmanned aerial vehicles (UAVs) has increased tremendously during the last decade due to their cost-effectiveness and flexibility in obtaining the high-resolution (cm-scale) images needed for PA applications. At the same time, the availability of a large amount of satellite data has prompted researchers to explore advanced data storage and processing techniques such as cloud computing and machine learning. Given the complexity of image processing and the amount of technical knowledge and expertise needed, it is critical to explore and develop a simple yet reliable workflow for the real-time application of remote sensing in PA. Development of accurate yet easy to use, user-friendly systems is likely to result in broader adoption of remote sensing technologies in commercial and non-commercial PA applications.

291 citations

Journal ArticleDOI
TL;DR: In this article, a review of ground-based, airborne and space-borne remote sensing methods suitable for permafrost hazard assessment and management is presented, together with results from spectral image classification, and with multi-temporal data from change detection and displacement measurements significantly improves the detection of hazard potential.
Abstract: Modern remote sensing techniques can help in the assessment of permafrost hazards in high latitudes and cold mountains. Hazard development in these areas is affected by process interactions and chain reactions, the ongoing shift of cryospheric hazard zones due to atmospheric warming, the large spatial scales involved and the remoteness of many permafrost-related threats. This paper reviews ground-based, airborne and spaceborne remote sensing methods suitable for permafrost hazard assessment and management. A wide range of image classification and change detection techniques support permafrost hazard studies. Digital terrain models (DTMs) derived from optical stereo, synthetic aperture radar (SAR) or laser scanning data are some of the most important data sets for investigating permafrost-related mass movements, thaw and heave processes, and hydrological hazards. Multi-temporal optical or SAR data are used to derive surface displacements on creeping and unstable frozen slopes. Combining DTMs with results from spectral image classification, and with multi-temporal data from change detection and displacement measurements significantly improves the detection of hazard potential. Copyright © 2008 John Wiley & Sons, Ltd.

125 citations

01 May 2014
TL;DR: In this article, the authors used high-resolution (0.03°) surface solar radiation (SSR) data from the Satellite Application Facility on Climate Monitoring (CM SAF) to study the subgrid spatial variability in all-sky SSR over Europe and the spatial representativeness of 143 surface sites with homogeneous records for their site-centered larger surroundings varying in size from 0.25° to 3°, as well as with respect to a given standard grid of 1° resolution.
Abstract: [1] The validation of gridded surface solar radiation (SSR) data often relies on the comparison with ground-based in situ measurements. This poses the question on how representative a point measurement is for a larger-scale surrounding. We use high-resolution (0.03°) SSR data from the Satellite Application Facility on Climate Monitoring (CM SAF) to study the subgrid spatial variability in all-sky SSR over Europe and the spatial representativeness of 143 surface sites with homogeneous records for their site-centered larger surroundings varying in size from 0.25° to 3°, as well as with respect to a given standard grid of 1° resolution. These analyses are done on a climatological annual and monthly mean basis over the period 2001–2005. The spatial variability of the CM SAF data set itself agrees very well with surface measurements in Europe, justifying its use for the present study. The annual mean subgrid variability in the 1° standard grid over European land is on average 1.6% (2.4 W m−2), with maximum of up to 10% in Northern Spain. The annual mean representation error of point values at 143 surface sites with respect to their 1° surrounding is on average 2% (3 W m−2). For larger surroundings of 3°, the representation error increases to 3% (4.8 W m−2). The monthly mean representation error at the surface sites with respect to the 1° standard grid is on average 3.7% (4 W m−2). This error is reduced when site-specific correction factors are applied or when multiple sites are available in the same grid cell, i.e., three more sites reduce the error by 50%.

70 citations