论文发表

2025

1. Field-scale evaluation of a satellite-based terrestrial biosphere model for estimating crop response to management practices and productivity

   Jingwen Wang, Jose Luis Pancorbo, Miguel Quemada, and 5 more authors

   ISPRS Journal of Photogrammetry and Remote Sensing, 2025

   Abs DOI

   Timely and accurate information on crop productivity is essential for characterizing crop growing status and guiding adaptive management practices to ensure food security. Terrestrial biosphere models forced by satellite observations (satellite-TBMs) are viewed as robust tools for understanding large-scale agricultural productivity, with distinct advantages of generalized input data requirement and comprehensive representation of carbon–water-energy exchange mechanisms. However, it remains unclear whether these models can maintain consistent accuracy at field scale and provide useful information for farmers to make site-specific management decisions. This study aims to investigate the capability of a satellite-TBM to estimate crop productivity at the granularity of individual fields using harmonized Sentinel-2 and Landsat-8 time series. Emphasis was placed on evaluating the model performance in: (i) representing crop response to the spatially and temporally varying field management practices, and (ii) capturing the variation in crop growth, biomass and yield under complex interactions among crop genotypes, environment, and management conditions. To achieve the first objective, we conducted on-farm experiments with controlled nitrogen (N) fertilization and irrigation treatments to assess the efficacy of using satellite-retrieved leaf area index (LAI) to reflect the effect of management practices in the TBM. For the second objective, we integrated a yield formation module into the satellite-TBM and compared it with the semi-empirical harvest index (HI) method. The model performance was then evaluated under varying conditions using an extensive dataset consisting of observations from four crop species (i.e., soybean, wheat, rice and maize), 42 cultivars and 58 field-years. Results demonstrated that satellite-retrieved LAI effectively captured the effects of N and water supply on crop growth, showing high sensitivity to both the timing and quantity of these inputs. This allowed for a spatiotemporal representation of management impacts, even without prior knowledge of the specific management schedules. The TBM forced by satellite LAI produced consistent biomass dynamics with ground measurements, showing an overall correlation coefficient (R) of 0.93 and a relative root mean square error (RRMSE) of 31.4 %. However, model performance declined from biomass to yield estimation, with the HI-based method (R = 0.80, RRMSE = 23.7 %) outperforming mechanistic modeling of grain filling (R = 0.43, RRMSE = 43.4 %). Model accuracy for winter wheat was lower than that for summer crops such as rice, maize and soybean, suggesting potential underrepresentation of the overwintering processes. This study illustrates the utility of satellite-TBMs in crop productivity estimation at the field level, and identifies existing uncertainties and limitations for future model developments.

2024

1. The Improved U-STFM : A Deep Learning-Based Nonlinear Spatial-Temporal Fusion Model for Land Surface Temperature Downscaling

   Shanxin Guo, Min Li, Yuanqing Li, and 6 more authors

   Remote Sensing, 2024
2. Forest Ecology and Management Forest fire susceptibility mapping based on precipitation-constrained cumulative dryness status information in Southeast China : A novel machine learning modeling approach

   Longlong Zhao, Yuankai Ge, Shanxin Guo, and 3 more authors

   Forest Ecology and Management, 2024

   DOI

2023

1. Stability Analysis of Unmixing ‐ Based Spatiotemporal Fusion Model : A Case of Land Surface Temperature Product Downscaling

   Min Li, Shanxin Guo, Jinsong Chen, and 5 more authors

   Remote Sensing, 2023
2. Statistical Assessments of InSAR Tropospheric Corrections : Applicability and Limitations of Weather Model Products and Spatiotemporal Filtering

   Luyi Sun, Jinsong Chen, Hongzhong Li, and 2 more authors

   Remote Sensing, 2023
3. Object-Oriented Remote Sensing Image Change Detection Based on Color Co-Occurrence Matrix

   Zhu Zhu, Tinggang Zhou, Jinsong Chen, and 4 more authors

   Applied Sciences, 2023

   Abs DOI

   Featured Application: Change detection for remote sensing images. Aiming at the problem of misdetection caused by the traditional texture characteristic extraction model, which does not describe the correlation among multiple bands, an object-oriented remote sensing image change detection method based on a color co-occurrence matrix is proposed. First, the image is divided into multi-scale objects by graph-based superpixel segmentation, and the optimal scale is determined by the overall goodness F-measure (OGF). Then, except for the extraction of the spectral features, the multi-channel texture features based on the color co-occurrence matrix (CCM) are extracted to consider the correlation among multiple bands. To accurately find the representative features to overcome the impact of feature redundancy, a cumulative backward search strategy (CBSS) is further designed. Finally, the change detection is completed by inputting the difference image of dual time points to the trained random forest model. Taking Shenzhen and Dapeng as the study areas, with Sentinel-2 and Skysat images under different spatial resolutions, and the forest–bareland change type as an example, the effectiveness of the proposed algorithm is verified by qualitative and quantitative analyses. They show that the proposed algorithm can obtain higher detection accuracy than the texture features without band correlation.

4. Superpixel Segmentation Based on Anisotropic Diffusion Model for Object-oriented Remote Sensing Image Classification

   Xiaoli Li, Jinsong Chen, Longlong Zhao, and 6 more authors

   IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 2023

   Abs DOI

   Superpixel segmentation is an essential step of object-oriented remote sensing image classification, the accuracy of the superpixel segmentation boundary will directly affect the classification result. Most of the traditional superpixel segmentation algorithms rely on spectral similarity and spatial connectivity to construct superpixels. They can not find the accurate boundary in the complex scenes, such as the spatial distribution of ground features being relatively broken, and large differences in the size and shape, especially long-thin shape and circular shape. Aiming at this problem, a superpixel segmentation algorithm based on anisotropic diffusion model named ADS is proposed and applied to image classification. The anisotropic diffusion model originated in thermodynamics has excellent properties in that the diffusion is continuous and smooth and its diffusion speed depends on the medium, which provides convenience for smoothing homogeneous regions and establishing boundary constraints for different ground objects. With this advantage, the diffusion flux model is established to consider the influence of boundary factors and used to simulate the dissimilarity measure with boundary constraints between pixels and seed points by combining the traditional spectral and spatial distance. Then, the seed points of superpixel are optimized under the K-means framework. The effectiveness of the proposed algorithm is tested and verified with different spatial resolutions, such as Landsat8 with 30m, Sentinel-2 with 10m, and Skysat with 0.5m. A large number of experiments show that the proposed algorithm can better correct the superpixel boundary fitting deviation problem in complex scenes and effectively promote the improvement of image classification accuracy.

2022

1. Hyperspectral Image Classification with Imbalanced Data Based on Semi-Supervised Learning

   Xiaorou Zheng, Jianxin Jia, Jinsong Chen, and 4 more authors

   Applied Sciences, 2022
2. High Quality Object Detection for Multiresolution Remote Sensing Imagery Using Cascaded Multi-Stage Detectors

   Binglong Wu, Yuan Shen, Shanxin Guo, and 3 more authors

   Remote Sensing, 2022

2021

1. PyCLiPSM: Harnessing heterogeneous computing resources on CPUs and GPUs for accelerated digital soil mapping

   Guiming Zhang, A-Xing Zhu, Jing Liu, and 2 more authors

   Transactions in GIS, 2021
2. Deep Learning Network Intensification for Preventing Noisy-Labeled Samples for Remote Sensing Classification

   Chuang Lin, Shanxin Guo, Jinsong Chen, and 4 more authors

   Remote Sensing, 2021
3. Tradeoffs in the Spatial and Spectral Resolution of Airborne Hyperspectral Imaging Systems: A Crop Identification Case Study

   Jianxin Jia, Jinsong Chen, Xiaorou Zheng, and 11 more authors

   IEEE Transactions on Geoscience and Remote Sensing, 2021

   Abs DOI

   Airborne hyperspectral images are used for crop identification with a high classification accuracy because of their high spectral resolution, spatial resolution, and signal-to-noise ratio (SNR). However, the tradeoffs between the three core parameters of a hyperspectral imager (SNR, spatial resolution, and spectral resolution) should be considered for designing an efficient imaging system. Only a few reported studies on the analysis of the impact of SNR on identification accuracy are available. Further, the tradeoffs and mutual interactions among these parameters are rarely considered. In this empirical study, our aim was to understand the relationship among the core parameters and their effects on crop identification accuracy by analyzing the tradeoffs and mutual interactions among these parameters. We analyzed the hyperspectral images of a typical plain agricultural area in Xiongan, China, acquired by the newly developed sensor airborne multimodular imaging spectrometer (AMMIS). The fundamental images were transformed to form datasets with different ranges of spectral resolution, spatial resolution, and SNR using data reconstruction methods. We adopted the classification and regression tree (CART), random forest (RF), and k-nearest neighbor (kNN) classifiers, and observed the overall accuracy (OA) across the degraded hyperspectral datasets. The experimental results indicated that the OA decreased with a decreasing SNR. As the spectral resolution became coarser, the OA first increased, plateaued, and then decreased. However, the OA increased with decreasing spatial resolution. This study was performed with the goal of bridging the knowledge gap between the back-end hyperspectral sensor designing and its front-end applications.

4. Full Parameter Time Complexity (FPTC): A Method to Evaluate the Running Time of Machine Learning Classifiers for Land Use/Land Cover Classification

   Xiaorou Zheng, Jianxin Jia, Shanxin Guo, and 4 more authors

   IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 2021

   Abs DOI

   In emergency responses to natural disasters, actionable information provided by remote sensing images is crucial to help emergency managers become aware of the situation and assess the magnitude of the damage. Without the accurate prediction of time consumption, choosing an algorithm for land use/land cover (LULC) classification under these emergency circumstances could be blind and subjective. Here, we proposed a full parameter time complexity (FPTC) analysis and the corresponding coefficient ωto estimate the actual running time of the LULC classification without actually running the code. The FPTC of five general algorithms is derived in this article. After derivation, the FPTC of k-nearest neighbors (kNN) is F(nv+n{\text{log}}_2 u), the FPTC of logistic regression (LR) is F(Qm2vn), the FPTC of classification and regression tree (CART) is F((m+1)nv{\text{log}}_2n), the FPTC of random forest (RF) is F(s(m+1)nv{\text{log}}_2n), and the FPTC of support vector machine (SVM) is F(m2Qv (n+k)). The results show a strong linear relationship between the actual running time and FPTC [R-squared: kNN (0.991), LR (0.997), CART (0.999), RF (1.000), and SVM (0.999)], with different data size. The average root-mean-squared error between the real running time and the estimated running time is 3.34 s, which demonstrates the effectiveness of FPTC. Combining FPTC with the corresponding coefficient ω, the running time of the classification can be precisely predicted, which will help emergency managers quickly choose algorithms in response to natural disasters with available remote sensing data and limited time.

2020

1. Identification of Short-Rotation Eucalyptus Plantation at Large Scale Using Multi-Satellite Imageries and Cloud Computing Platform

   Xinping Deng, Shanxin Guo, Luyi Sun, and 1 more author

   Remote Sensing, 2020

   DOI
2. High-Resolution U-Net : Preserving Image Details for Extraction Cultivated Land

   Wenna Xu, Xinping Deng, Shanxin Guo, and 6 more authors

   Sensors, 2020
3. Removing Stripe Noise Based on Improved Statistics for Hyperspectral Images

   Jianxin Jia, Xiaorou Zheng, Shanxin Guo, and 2 more authors

   IEEE Geoscience and Remote Sensing Letters, 2020
4. Adaptive Distance-Weighted Voronoi Tessellation for Remote Sensing Image Segmentation

   Xiaoli Li, Jinsong Chen, Longlong Zhao, and 3 more authors

   Remote Sensing, 2020

   DOI
5. Improved SRGAN for Remote Sensing Image Super-Resolution Across Locations and Sensors

   Yingfei Xiong, Shanxin Guo, Jinsong Chen, and 2 more authors

   Remote Sensing, 2020

   DOI
6. Integration of Time Series Sentinel-1 and Sentinel-2 Imagery for Crop Type Mapping over Oasis Agricultural Areas

   Luyi Sun, Jinsong Chen, Shanxin Guo, and 2 more authors

   Remote Sensing, 2020

2018

1. MODIS ocean color product downscaling via spatio-temporal fusion and regression: The case of chlorophyll-a in coastal waters

   Shanxin Guo, Bo Sun, Hankui K. Zhang, and 5 more authors

   International Journal of Applied Earth Observation and Geoinformation, 2018

   important paper

   DOI

2016

1. Unification of soil feedback patterns under different evaporation conditions to improve soil differentiation over flat area

   Shanxin Guo, A-Xing Zhu, Lingkui Meng, and 4 more authors

   International Journal of Applied Earth Observation and Geoinformation, 2016

   DOI

2015

1. Data-Gap Filling to Understand the Dynamic Feedback Pattern of Soil

   Shanxin Guo, Lingkui Meng, A-Xing Zhu, and 4 more authors

   Remote Sensing, Sep 2015

   DOI