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CSTN: A cross-region crop mapping method integrating self-training and contrastive domain adaptation

INTERNATIONAL JOURNAL OF APPLIED EARTH OBSERVATION AND GEOINFORMATION 2025年 136卷

作者： peng, shuwen Zhang, Liqiang Xie, Rongchang Qu, Ying Beijing Normal Univ Fac Geog Sci State Key Lab Remote Sensing Sci Beijing 100875 Peoples R China Peking Univ Ctr Data Sci Beijing 100871 Peoples R China

Crop mapping is essential for agricultural management and food production monitoring, but challenges like limited crop labels and poor model generalization significantly hinder large-scale crop mapping. Here, we introduce a novel Contrastive Self-Training Network (CSTN), integrating a self-training strategy and contrastive domain adaptation (CDA) for cross-region crop mapping. CSTN uses pseudo-labels in the target region generated by the self-training strategy to assist supervised learning, and aligns features across regions using class-aware prototypes. Qualitative and quantitative evaluations demonstrate that CSTN significantly outperforms state-ofthe-art methods with a 12.29 % increase in average F1-score, particularly in maize identification. Moreover, CSTN also enables early-season crop classification for pre-harvest decision-making applications. The interpretability of the model is demonstrated through an in-depth analysis of feature map visualizations, attention map visualizations, and the effectiveness of the modules. This study provides a robust method for enhancing largescale crop mapping and facilitating more accurate and timely agricultural practices.

关键词： Crop mapping Satellite imagery time series Pseudo-labels Contrastive Domain Adaptation Model interpretability

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Nano-flower spherical NiFe LDH adsorbent for efficient removal of H2S in blast furnace gas

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JOURNAL OF HAZARDOUS MATERIALS 2025年 489卷 137622页

作者： peng, shuwen Li, Yuran Wang, Bin Cui, Yanbin Tian, Jinglei Liu, Hongqiang Zhu, Tingyu Chinese Acad Sci Inst Proc Engn Innovat Acad Green Manufacture CAS Key Lab Green Proc & Engn Beijing 100190 Peoples R China Univ Chinese Acad Sci Beijing 100190 Peoples R China HBIS Grp Co Ltd Shijiazhuang 050023 Peoples R China Chinese Acad Sci Inst Urban Environm Ctr Excellence Reg Atmospher Environm Xiamen 361021 Peoples R China

Removal of sulfur species from blast furnace gas is urgently needed due to the strict emission limits imposed on iron-steel industrial flue gas. Improving the sulfur capacity of H2S is a crucial challenge to reduce the operation cost. NiFe layered double hydroxide (LDH) adsorbents were synthesized using the hydrothermal method to strengthen the adsorption of H2S, achieving a high sulfur capacity of 133.6 mg/g at 50 degrees C. Characterization studies have revealed that the reaction pathway of H2S on the NiFe LDH surface involves adsorption, dissociation and oxidation. It has been clarified that the high sulfur capacity can be attributed to the abundant H2S dissociation sites and the excellent O2 activation sites. The dissociation sites of H2S encompass metal sites, -OH and CO32-. The interaction between O2 and the bridge site of asymmetric metal atoms significantly enhances the dissociation of O2. Strengthening the dissociation of H2S and O2 improves the sulfur capacity. The deactivation of adsorbents comes from the continuous consumption of oxygen species mainly composed of -OH and the deposition of sulfur species in the smaller mesopores ranging from 2 to 10 nm. This work provides useful insights into designing highly efficient iron-based adsorbents for the desulfurization of blast furnace gas.

关键词： Layered double hydroxide Adsorption Catalytic oxidation Hydrogen sulfide Blast furnace gas

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Point-Based Multilevel Domain Adaptation for Point Cloud Segmentation

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IEEE GEOSCIENCE AND REMOTE SENSING LETTERS 2022年 19卷 1页

作者： peng, shuwen Xi, Xiaohuan Wang, Cheng Xie, Rongchang Wang, Pu Tan, Hongwu Chinese Acad Sci Aerosp Informat Res Inst Key Lab Digital Earth Sci Beijing 100094 Peoples R China Univ Chinese Acad Sci Coll Resources & Environm Beijing 100049 Peoples R China Peking Univ Ctr Data Sci Beijing 100871 Peoples R China Beijing Yupont Elect Power Technol Co Ltd Beijing 100029 Peoples R China

Although good performance has been recently achieved in point cloud semantic segmentation based on deep learning, it has not been promoted due to differences in actual scenes and the time-consuming production of labeled data sets. Unsupervised domain adaptation (UDA) aims to solve the problem of how to adapt the classifier from one scene (source domain) to another unlabeled scene (target domain), which can reduce the performance drop caused by the domain shift. Since spatial information is important for light detection and ranging (LiDAR) point and the causes of domain gap in image and point cloud tasks are different, projecting point cloud into image for processing is not suitable and how to apply the image-oriented domain adaptation (DA) methods to point cloud is not trivial. In this letter, we propose a 3D point-based UDA method for point cloud semantic segmentation. This model introduces point- and set-level domain adaptive modules to achieve feature alignment between the domains. We evaluate the proposed method with two experiments, including cross terrain adaptation and airborne to mobile adaptation. Compared with the results without using DA, the mean intersection over union (mIoU) increased by 10.45% and 24.69%, respectively, indicating the effectiveness of our method.

关键词： Three-dimensional displays Task analysis Feature extraction Semantics Laser radar Image segmentation Loss measurement Domain adaptation (DA) domain adaptive module domain adversarial training point cloud semantic segmentation

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Satellite mapping of maize cropland in one-season planting areas of China

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SCIENTIFIC DATA 2023年第1期10卷 437页

作者： Xin, Qi Zhang, Liqiang Qu, Ying Geng, Hao Li, Xingang peng, shuwen Beijing Normal Univ Fac Geog Sci Beijing Peoples R China

sAs the major maize-cultivated areas, the one-season cropland of China is increasingly threatened by rapid urbanization and soybean rejuvenation. Quantifying the area changes of maize cropland is crucial for both food and energy security. Nonetheless, due to the lack of survey data related to planting types, long-term and fine-grained maize cropland maps in China dominated by small-scale farmlands are still unavailable. In this paper, we collect 75,657 samples based on field surveys and propose a deep learning-based method according to the phenology information of maize. With the generalization capability, the proposed method produces maize cropland maps with a resolution of 30 m from 2013 to 2021 in the one-season planting areas of China. The maize-cultivated areas derived from the maps are highly consistent with the data recorded by statistical yearbooks (R-2 = 0.85 on average), which indicates that the produced maps are reliable to facilitate the research on food and energy security.

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Mapping annual 10-m maize cropland changes in China during 2017-2021

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SCIENTIFIC DATA 2023年第1期10卷 765页

作者： Li, Xingang Qu, Ying Geng, Hao Xin, Qi Huang, Jianxi peng, shuwen Zhang, Liqiang Beijing Normal Univ Fac Geog Sci Beijing 100875 Peoples R China China Agr Univ Coll Land Sci & Technol Beijing 100083 Peoples R China

China contributed nearly one-fifth of the world maize production over the past few years. Mapping the distributions of maize cropland in China is crucial to ensure global food security. Nonetheless, 10 m maize cropland maps in China are still unavailable, restricting the promotion of sustainable agriculture. In this paper, we collect numerous samples to produce annual 10-m maize cropland maps in China from 2017 to 2021 with a machine learning based classification framework. To overcome the temporal variations of plants, the proposed framework takes Sentinel-2 sequence images as input and utilizes deep neural networks and random forest as classifiers to map maize in a zone-specific way. The generated maps have an overall accuracy (OA) spanning from 0.87 to 0.95 and the maize-cultivated areas estimated by the maps are highly consistent with the records in statistical yearbooks (R2 varying from 0.83 to 0.95). To the best of our knowledge, this is the first annual 10-m maize maps across China, which largely facilitates the sustainable agriculture development in China dominated by smallholder farmlands.

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Evaluation of Footprint Horizontal Geolocation Accuracy of Spaceborne Full-Waveform LiDAR Based on Digital Surface Model

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IEEE JOURNAL OF SELECTED TOPICS IN APPLIED EARTH OBSERVATIONS AND REMOTE SENSING 2020年 13卷 2135-2144页

作者： Wang, Cheng Yang, Xuebo Xi, Xiaohuan Zhang, Hebing Chen, Shichao peng, shuwen Zhu, Xiaoxiao Chinese Acad Sci Aerosp Informat Res Inst Key Lab Digital Earth Sci Beijing 100094 Peoples R China Univ Chinese Acad Sci Coll Resources & Environm Beijing 100049 Peoples R China Henan Polytech Univ Coll Surveying & Land Informat Engn Jiaozuo 454003 Henan Peoples R China China Univ Min & Technol Coll Geosci & Surveying Engn Beijing 100083 Peoples R China

Spaceborne full-waveform LiDAR has shown unique advantages in measuring global surface elevation. Laser footprints generally have decimeter-level vertical accuracy, meeting the requirement of ground elevation control points. In contrast, the footprint horizontal geolocation accuracy is in the meter to ten-meter levels. Although previous researches attempted to locate the footprint horizontal coordinate based on the digital surface model (DSM), the applicability and performance of the DSM-based positioning method in evaluating the footprint geolocation accuracy should be rigorously assessed before large-scale applications. Therefore, this study practices the DSM-based footprint positioning method over several study sites with various land covers and different laser campaigns. The footprint geolocation accuracy of the ICESat/GLAS (Ice, Cloud, and land Elevation Satellite/Geoscience Laser Altimeter System), the first Earth observation full-waveform LiDAR satellite, is evaluated by the DSM-based method. Results indicate that the DSM-based positioning method is only suitable for areas with significant height features, but not applicable in areas with high spatial correlation. The derived footprint geolocation accuracy (8.19-m horizontal shifting with 4.19-m standard deviation) is relatively reliable in urban site with relatively high spatial heterogeneity. This study helps make better use of the DSM-based footprint positioning method and design calibration experiments of full-waveform LiDAR satellites.

关键词： Laser radar Surface emitting lasers Geology Surface topography Surface waves Satellites Measurement by laser beam Digital surface model (DSM) geolocation accuracy ICESat GLAS spaceborne LiDAR waveform simulation

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PMF-FFT并行捕获扇贝损失补偿方法分析

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上海航天（中英文） 2023年第S01期40卷 101-107页

作者：刘庆张锦李忞詝彭舒文周里上海航天电子技术研究所上海201109

在长征四号运载火箭飞行过程中,全球导航卫星系统(GNSS)接收机与卫星的相对运动使得载波存在较大的多普勒频移,必须尽快完成信号捕获。采用PMF-FFT(部分匹配滤波和快速傅里叶变换)方法可以快速捕获GNSS扩频信号,采用频域并行捕获可进一... 详细信息

在长征四号运载火箭飞行过程中,全球导航卫星系统(GNSS)接收机与卫星的相对运动使得载波存在较大的多普勒频移,必须尽快完成信号捕获。采用PMF-FFT(部分匹配滤波和快速傅里叶变换)方法可以快速捕获GNSS扩频信号,采用频域并行捕获可进一步扩展单次载波多普勒频移的搜索范围,然而PMF-FFT在并行捕获中的扇贝损失降低了捕获性能,需要进行补偿。分析了PMF-FFT捕获中扇贝损失的产生机理,并讨论了单通道及通道间扇贝损失的补偿方法。结果表明:给出的补偿方法可改善扇贝损失,为GNSS接收机快速完成卫星信号捕获奠定了基础,适用于运载火箭等高动态环境下的导航接收机设计,对采用PMF-FFT捕获的扩频类接收机、应答机设计也有一定的工程实用价值。

关键词： PMF-FFT 并行捕获扇贝损失补偿高动态 GNSS

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Improving maize yield prediction at the county level from 2002 to 2015 in China using a novel deep learning approach

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COMPUTERS AND ELECTRONICS IN AGRICULTURE 2022年 202卷

作者： Li, Xingang Geng, Hao Zhang, Liqiang peng, shuwen Xin, Qi Huang, Jianxi Li, Xuecao Liu, Suhong Wang, Yuebin Beijing Normal Univ Beijing Key Lab Environm Remote Sensing & Digital Beijing Peoples R China China Agr Univ Coll Land Sci & Technol Beijing Peoples R China China Univ Geosci Sch Land Sci & Technol Beijing Peoples R China China Agr Univ Coll Land Sci & Technol 17 Qinghua East Rd Beijing 100083 Peoples R China

Accurate and timely maize yield monitoring from satellite imagery is in great demand in developing countries. The spatial heterogeneity deprived of the large territory of China makes it a challenge. In this article, we developed a novel deep learning model for maize yield prediction at the county level based on multiple satellite data. The two-stage feature learning structure integrated data from disparate sources, enhancing feature representation. The discriminative features were optimized from two levels: the dictionary matrix learned the spatial diversity of the provinces, and the improved optimizing formulation fit the distribution of the unbalanced records. The cross-validation results showed that our approach could explain 82 % of the variation in maize yield, achieving state-of-the-art. The model was robust when predicting the future, with the average root-mean-square error of 1006 kg/ha and the mean-absolute-percentage error of 17.1 %. The ability of early maize yield prediction clarifies the tremendous application value, showing the data from the first two months can already explain 75.6 % of yield variation. It was the first effort to improve county-level maize yield prediction in China, providing a potential framework for advancing the use of multi-source datasets for maize yield estimating.

关键词： Yield prediction Deep learning Multi -source data Spatial diversity Historical yield correlation

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中国乡村聚落建筑面积时空变化分析

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同济大学学报（自然科学版） 2022年第7期50卷 967-974页

作者：张立强耿昊刘彦随李新港辛奇彭淑雯北京师范大学地理科学学部北京100875 中国科学院地理科学与资源研究所北京100101

基于土地覆盖数据集(CLCD)产品和轮廓提取算法,系统开展了1990―2020年中国乡村聚落建筑形态、面积扩展及其时空变化研究。通过深入分析乡村聚落时空变化特征发现:在快速城镇化进程中,中国乡村人口不断减少的同时,乡村聚落呈现持续扩张... 详细信息

基于土地覆盖数据集(CLCD)产品和轮廓提取算法,系统开展了1990―2020年中国乡村聚落建筑形态、面积扩展及其时空变化研究。通过深入分析乡村聚落时空变化特征发现:在快速城镇化进程中,中国乡村人口不断减少的同时,乡村聚落呈现持续扩张的态势。2020年中国乡村聚落面积约是1990年的2倍。小面积的乡村聚落拥有更快的扩张速度,在面积到达特定阈值后开始逐渐放缓。乡村聚落扩张速度具有明显的空间差异性,西部地区的扩张速度快于东部地区。乡村聚落的持续扩张和规模扩大,一方面反映了中国城乡平衡发展取得了可喜进展,另一方面也反映了城乡建设用地“双增长”可能对农业及生态用地产生负面影响,对此应当引起高度重视。

关键词：乡村聚落乡村地域系统乡村人地关系空间格局时空变化

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Radiative forcing reduced by early twenty-first century increase in land albedo

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Nature 2025年第8065期641卷 1162-1171页

作者： Zhengyang Hou Liqiang Zhang Jingjing peng Giovanni Forzieri Aolin Jia Zhiqiang Xiao Ying Qu Jintai Lin Duoying Ji Zidong Zhu Xin Yao shuwen peng Lanpu Zhao Wenjie Fan Zhaocong Wu Hao Geng Qihao Wang Chenghu Zhou Suhong Liu Liangpei Zhang State Key Laboratory of Remote Sensing and Digital Earth Faculty of Geographical Science Beijing Normal University Beijing China. State Key Laboratory of Remote Sensing and Digital Earth Faculty of Geographical Science Beijing Normal University Beijing China. zhanglq@***. Aerospace Information Research Institute Henan Academy of Sciences Zhengzhou China. zhanglq@***. Earth System Science Interdisciplinary Center/Cooperative Institute for Satellite Earth System Studies (CISESS) University of Maryland College Park MD USA. Department of Civil and Environmental Engineering University of Florence Florence Italy. Remote Sensing and Natural Resource Modeling Department ERIN Luxembourg Institute of Science and Technology Belvaux Luxembourg. Laboratory for Climate and Ocean-Atmosphere Studies Department of Atmospheric and Oceanic Sciences School of Physics Peking University Beijing China. linjt@***. Institute of Carbon Neutrality Peking University Beijing China. linjt@***. Aerospace Information Research Institute Henan Academy of Sciences Zhengzhou China. School of Earth and Space Sciences Peking University Beijing China. State Key Laboratory of Resources and Environmental Information System Institute of Geographical Science and Natural Resources Chinese Academy of Sciences Beijing China. zhouch@***. Aerospace Information Research Institute Henan Academy of Sciences Zhengzhou China. zlp62@***.

Surface albedo greatly affects how much energy the Earth absorbs. Intensive human activities and accelerated climate change have altered surface albedo across spatial and temporal scales, yet assessments of the effects of land use or land cover (LULC) and snow variations on land surface albedo are scarce at the global scale. As a result, the global land surface albedo dynamics over recent decades and their corresponding radiative forcing to the climate system remain poorly understood. Here we quantify the individual and combined effects of snow cover dynamics, LULC conversions and non-conversion regions on albedo variations during 2001-2020 and estimate their induced radiative forcing. We show that the negative radiative forcing induced by the global land surface albedo change was -0.142 (-0.158, -0.114) W m over the past two decades. The global snow-free land surface albedo increased by 2.2% (P < 0.001), with a negative radiative forcing of -0.164 (-0.186, -0.138) W m (P < 0.001). The magnitude of this negative forcing is sevenfold larger than the positive forcing induced by snow dynamics, and equivalent to 59.9% of that caused by CO emissions from 2011 to 2019. The global radiative forcing due to albedo changes in LULC non-conversion regions is 3.9 to 8.1 times greater than that from LULC conversions. The radiative forcing induced by albedo changes highlights the important role of land surface dynamics in modulating global warming.

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