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Improving model performance in mapping cropland soil organic matter using time-series remote sensing data

Journal of Integrative Agriculture 2024年第8期23卷 2820-2841页

作者： Xianglin Zhang Jie Xue Songchao Chen Zhiqing Zhuo Zheng Wang Xueyao Chen Yi Xiao Zhou Shi institute of Applied remote sensing and information Technology College of Environmental and Resource SciencesZhejiang UniversityHangzhou 310058China Department of Land Management Zhejiang UniversityHangzhou 310058China ZJU-Hangzhou Global Scientific and Technological Innovation Center Zhejiang UniversityHangzhou 311215China institute of Digital Agriculture Zhejiang Academy of Agricultural SciencesHangzhou 310021China Key Laboratory of Spectroscopy sensing Ministry of Agriculture and Rural AffairsHangzhou 310058China

Faced with increasing global soil degradation,spatially explicit data on cropland soil organic matter(SOM)provides crucial data for soil carbon pool accounting,cropland quality assessment and the formulation of effective management *** a spatial information prediction technique,digital soil mapping(DSM)has been widely used to spatially map soil information at different ***,the accuracy of digital SOM maps for cropland is typically lower than for other land cover types due to the inherent difficulty in precisely quantifying human *** overcome this limitation,this study systematically assessed a framework of“information extractionfeature selection-model averaging”for improving model performance in mapping cropland SOM using 462 cropland soil samples collected in Guangzhou,China in *** results showed that using the framework of dynamic information extraction,feature selection and model averaging could efficiently improve the accuracy of the final predictions(R^(2):0.48 to 0.53)without having obviously negative impacts on *** the dynamic information of the environment was an efficient way to generate covariates that are linearly and nonlinearly related to SOM,which improved the R^(2)of random forest from 0.44 to 0.48 and the R^(2)of extreme gradient boosting from 0.37to *** recursive feature selection(FRFS)is recommended when there are relatively few environmental covariates(500).The Granger-Ramanathan model averaging approach could improve the prediction accuracy and average *** the structures of initial prediction models are similar,increasing in the number of averaging models did not have significantly positive effects on the final *** the advantages of these selected strategies over information extraction,feature selection and model averaging have a great potential for high-accuracy soil mapping

关键词： cropland soil organic matter digital soil mapping machine learning feature selection model averaging

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Global convergence in terrestrial gross primary production response to atmospheric vapor pressure deficit

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Science China(Life Sciences) 2024年第9期67卷 2016-2025页

作者： Chao Huang Jingfeng Huang Jingfeng Xiao Xing Li Hong SHe Yu Liang Fusheng Chen Hanqin Tian Key Laboratory of National Forestry and Grassland Administration on Forest Ecosystem Protection and Restoration of Poyang Lake Watershed College of ForestryJiangxi Agricultural UniversityNanchang 330045China CAS Key Laboratory of Forest Ecology and Management Institute of Applied EcologyChinese Academy of SciencesShenyang 110016China institute of Applied remote sensing&information Technology College of Environmental and Resource SciencesZhejiang UniversityHangzhou 310058China Key Laboratory of agricultural remote sensing and information Systems Zhejiang ProvinceZhejiang UniversityHangzhou 310058China Earth Systems Research Center Institute for the Study of EarthOceansand SpaceUniversity of New HampshireDurham NH 03824USA Research institute of Agriculture and Life Sciences Seoul National UniversitySeoul 08826South Korea School of Natural Resources University of Missouri203 ABNR BuildingColumbiaMO 65211USA Schiller institute for Integrated Science and Society Department of Earth and Environmental SciencesBoston CollegeChestnut HillMA 02467USA

Atmospheric vapor pressure deficit(VPD)increases with climate warming and may limit plant ***,gross primary production(GPP)responses to VPD remain a mystery,offering a significant source of uncertainty in the estimation of global terrestrial ecosystems carbon *** this study,in-situ measurements,satellite-derived data,and Earth System Models(ESMs)simulations were analysed to show that the GPP of most ecosystems has a similar threshold in response to VPD:first increasing and then *** VPD exceeds these thresholds,atmospheric drought stress reduces soil moisture and stomatal conductance,thereby decreasing the productivity of terrestrial *** ESMs underscore CO_(2) fertilization effects but predict significant GPP decline in low-latitude ecosystems when VPD exceeds the *** results emphasize the impacts of climate warming on VPD and propose limitations to future ecosystems productivity caused by increased atmospheric water *** VPD,soil moisture,and canopy conductance interactions into ESMs enhances the prediction of terrestrial ecosystem responses to climate change.

关键词： atmospheric water demands soil moisture stomatal conductance photosynthesis climate warming

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RGB-guided hyperspectral image super-resolution with deep progressive learning

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CAAI Transactions on Intelligence Technology 2024年第3期9卷 679-694页

作者： Tao Zhang Ying Fu Liwei Huang Siyuan Li Shaodi You Chenggang Yan School of Computer Science and Technology Beijing Institute of TechnologyBeijingChina Satellite information Intelligent Processing and application Research Laboratory Beijing Institute of Remote SensingBeijingChina Key Laboratory of Spectral Imaging Technology Xi'an Institute of Optics and Precision Mechanics of Chinese Academy of SciencesXi'anChina institute of Informatics University of AmsterdamAmsterdamNetherlands School of Communication Engineering Hangzhou Dianzi UniversityHangzhouChina

Due to hardware limitations,existing hyperspectral(HS)camera often suffer from low spatial/temporal ***,it has been prevalent to super-resolve a low reso-lution(LR)HS image into a high resolution(HR)HS image with a HR RGB(or mul-tispectral)image *** approaches for this guided super-resolution task often model the intrinsic characteristic of the desired HR HS image using hand-crafted ***,researchers pay more attention to deep learning methods with direct supervised or unsupervised learning,which exploit deep prior only from training dataset or testing *** this article,an efficient convolutional neural network-based method is presented to progressively super-resolve HS image with RGB image ***-ically,a progressive HS image super-resolution network is proposed,which progressively super-resolve the LR HS image with pixel shuffled HR RGB image ***,the super-resolution network is progressively trained with supervised pre-training and un-supervised adaption,where supervised pre-training learns the general prior on training data and unsupervised adaptation generalises the general prior to specific prior for variant testing *** proposed method can effectively exploit prior from training dataset and testing HS and RGB images with spectral-spatial *** has a good general-isation capability,especially for blind HS image *** experimental results show that the proposed deep progressive learning method out-performs the existing state-of-the-art methods for HS image super-resolution in non-blind and blind cases.

关键词： computer vision deep neural networks image processing image resolution unsupervised learning

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Data error propagation in stacked bioclimatic envelope models

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Integrative Zoology 2024年第2期19卷 262-276页

作者： Xueyan LI Babak NAIMI Peng GONG Miguel B.ARAÚJO Guangdong Provincial Key Laboratory of remote sensing and Geographical information System Guangdong Open Laboratory of Geospatial Information Technology and ApplicationGuangzhou Institute of GeographyGuangdong Academy of ScienceGuangzhouChina ‘Rui Nabeiro’Biodiversity Chair CHANGE-MED InstituteUniversity ofÉvoraÉvoraPortugal Department of Geography and Department of Earth Sciences University of Hong KongHong KongChina Department of Biogeography and Global Change National Museum of Natural SciencesCSICMadridSpain

Stacking is the process of overlaying inferred species potential distributions for multiple species based on outputs of bioclimatic envelope models(BEMs).The approach can be used to investigate patterns and processes of species *** data limitations on individual species distributions are inevitable,but how do they affect inferences of patterns and processes of species richness?We investigate the influence of different data sources on estimated species richness gradients in *** fitted BEMs using species distributions data for 334 bird species obtained from(1)global range maps,(2)regional checklists,(3)museum records and surveys,and(4)citizen science data using presence-only(Mahalanobis distance),presence-background(MAXENT),and presence–absence(GAM and BRT)*** species predictions were stacked to generate species richness ***,we show that different data sources and BEMs can generate spatially varying gradients of species *** environmental predictors that best explained species distributions also differed between data *** using citizen-based data had the highest accuracy,whereas those using range data had the lowest *** richness patterns estimated by GAM and BRT models were robust to data *** multiple data sets exist for the same region and taxa,we advise that explicit treatments of uncertainty,such as sensitivity analyses of the input data,should be conducted during the process of modeling.

关键词： richness patterns species distribution stacked bioclimatic envelope models uncertainty

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Quantifying the agreement and accuracy characteristics of four satellite-based LULC products for cropland classification in China

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Journal of Integrative Agriculture 2024年第1期23卷 283-297页

作者： Jie Xue Xianglin Zhang Songchao Chen Bifeng Hu Nan Wang Zhou Shi Department of Land Management Zhejiang UniversityHangzhou 310058China institute of Applied remote sensing and information Technology College of Environmental and Resource SciencesZhejiang UniversityHangzhou 310058China ZJU-Hangzhou Global Scientific and Technological Innovation Center Zhejiang UniversityHangzhou 311215China Department of Land Resource Management School of Public Finance and Public AdministrationJiangxi University of Finance and EconomicsNanchang 330013China Key Laboratory of Data Science in Finance and Economics Jiangxi University of Finance and EconomicsNanchang 330013China Key Laboratory of Spectroscopy sensing Ministry of Agriculture and Rural AffairsHangzhou 310058China

Various land use and land cover(LULC)products have been produced over the past decade with the development of remote sensing *** the differences in LULC classification schemes,there is a lack of research on assessing the accuracy of their application to croplands in a unified ***,this study evaluated the spatial and area accuracies of cropland classification for four commonly used global LULC products(i.e.,MCD12Q1V6,GlobCover2009,FROM-GLC and GlobeLand30)based on the harmonised FAO criterion,and quantified the relationships between four factors(i.e.,slope,elevation,field size and crop system)and cropland classification *** validation results indicated that MCD12Q1 and GlobeLand30 performed well in cropland classification regarding spatial consistency,with overall accuracies of 94.90 and 93.52%,*** FROMGLC showed the worst performance,with an overall accuracy of 83.17%.Overlaying the cropland generated by the four global LULC products,we found the proportions of complete agreement and disagreement were 15.51 and 44.72% for the cropland classification,*** consistency was mainly observed in the Northeast China Plain,the Huang-Huai-Hai Plain and the northern part of the Middle-lower Yangtze Plain,*** contrast,low consistency was detected primarily on the eastern edge of the northern and semiarid region,the Yunnan-Guizhou Plateau and southern *** size was the most important factor for mapping *** area accuracy,compared with China Statistical Yearbook data at the provincial scale,the accuracies of different products in descending order were:GlobeLand30,FROM-GLC,MCD12Q1,and *** cropland classification schemes mainly caused large area deviations among the four products,and they also resulted in the different ranks of spatial accuracy and area accuracy among the four *** results can provide valuable suggestions for selecting cropland products at the national or provinc

关键词： global LULC products cropland mapping accuracy evaluation food security China

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功能-要素-流视角下的乡村活力空间识别与评价——以中国杭州市临安区为例

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Journal of Geographical Sciences 2024年第6期34卷 1228-1250页

作者：张晶管晨悦章琳虞舟鲁叶成煊朱从谋李思楠王珂干牧野 institute of Applied remote sensing and information Technology College of Environmental and Resource SciencesZhejiang UniversityHangzhou 310058China zhejiang Key Research Laboratory of agricultural remote sensing and information Technology Hangzhou 310058China Shandong(Linyi)institute of Modern Agriculture Linyi 276000ShandongChina Huzhou Spatial Planning Preparation and Research Center Huzhou 313000ZhejiangChina zhejiang Digital Governance Space Planning and Design Co. Ltd.Hangzhou 310000China Department of Land Resources Management Zhejiang Gongshang UniversityHangzhou 310018China

Rural vitality is the life force expressed by a combination of endogenous dynamics and external influences. Exploring the complex relationship between rural functions, elements and flows could achieve sustainable rural development. This study constructed a theoretical framework guided by the four functions of production, living, ecology and culture with the support of mobile big data. Furthermore, the network centrality of villages was estimated to reflect the intensity of urban-rural social mobility ties. The results indicated marked spatial disparities in rural vitality, and the coupling of ecological-cultural vitality has a high degree of coherence. Four rural vitality grades were identified: high level(38, 14.08%), medium-high level(66, 24.44%), medium-low level(110, 40.74%) and low level(56, 20.74%), covering 270 administrative village units. The flow intensity of social linkage elements is consistent with rural vitality and the socioeconomic spillover effect of urban centers on neighboring villages was noticeable. Topographic complexity negatively affected the living function, mainly in the T1 and T2 terrain gradients;the rural ecological function was not fully correlated with urban adjacency, and proximity could lead to adverse effects such as urban sprawl and resource destruction. The application of this study is to explore the importance of “flow” by utilizing mobile big data to refine the evaluation unit to the village scale. Accelerating the construction of network coverage and information interconnection and promoting the elemental flow of people, transportation and information between urban and rural areas are important ways to enhance rural vitality.

关键词： rural vitality rural multifunction elements urban-rural flows mobile big data

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Unsupervised Color Segmentation with Reconstructed Spatial Weighted Gaussian Mixture Model and Random Color Histogram

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Computers, Materials & Continua 2024年第3期78卷 3323-3348页

作者： Umer Sadiq Khan Zhen Liu Fang Xu Muhib Ullah Khan Lerui Chen Touseef Ahmed Khan Muhammad Kashif Khattak Yuquan Zhang School of Computer and information Science Hubei Engineering UniversityXiaogan432000China institute for AI Industrial Technology Research Hubei Engineering UniversityXiaogan432000China Key Lab of Mountain Hazards and Surface Processes Institute of Mountain Hazards and EnvironmentChinese Academy of SciencesChengdu610041China university of Chinese Academy of Sciences Beijing100049China College of Aviation Zhongyuan University of TechnologyZhengzhou451191China Research Center of Digital Mountain and remote sensing application Institute of Mountain Hazards and EnvironmentChinese Academy of SciencesChengdu610041China School of Computer Science University of PoonchAzad Jammu Kashmir12350Pakistan School of Computer Science and information Engineering Hubei UniversityWuhan430062China

Image classification and unsupervised image segmentation can be achieved using the Gaussian mixture *** the Gaussian mixture model enhances the flexibility of image segmentation,it does not reflect spatial information and is sensitive to the segmentation *** this study,we first present an efficient algorithm that incorporates spatial information into the Gaussian mixture model(GMM)without parameter *** proposed model highlights the residual region with considerable information and constructs color ***,we incorporate the content-based color saliency as spatial information in the Gaussian mixture *** segmentation is performed by clustering each pixel into an appropriate component according to the expectation maximization and maximum ***,the random color histogram assigns a unique color to each cluster and creates an attractive color by default for segmentation.A random color histogram serves as an effective tool for data visualization and is instrumental in the creation of generative art,facilitating both analytical and aesthetic *** experiments,we have used the Berkeley segmentation dataset BSDS-500 and Microsoft Research in Cambridge *** the study,the proposed model showcases notable advancements in unsupervised image segmentation,with probabilistic rand index(PRI)values reaching 0.80,BDE scores as low as 12.25 and 12.02,compactness variations at 0.59 and 0.7,and variation of information(VI)reduced to 2.0 and 1.49 for the BSDS-500 and MSRC datasets,respectively,outperforming current leading-edge methods and yielding more precise segmentations.

关键词： Unsupervised segmentation color saliency spatial weighted GMM random color histogram

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Cloud Top Pressure Retrieval Using Polarized and Oxygen A-band Measurements from GF5 and PARASOL Satellites

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Advances in Atmospheric Sciences 2024年第4期41卷 680-700页

作者： Lesi WEI Huazhe SHANG Jian XU Chong SHI Gegen TANA Kefu CHAO Shanhu BAO Liangfu CHEN Husi LETU State Key Laboratory of remote sensing Science Aerospace Information Research InstituteChinese Academy of SciencesBeijing 100101China National Space Science Center Chinese Academy of SciencesBeijing 101407China College of Physics and Electronic information Inner Mongolia Normal UniversityHohhot 010022China College of Geographical Science Inner Mongolia Normal UniversityHohhot 010022China university of Chinese Academy of Sciences Beijing 100101China

Cloud top pressure(CTP)is one of the critical cloud properties that significantly affects the radiative effect of ***-angle polarized sensors can employ polarized bands(490 nm)or O_(2)A-bands(763 and 765 nm)to retrieve the ***,the CTP retrieved by the two methods shows inconsistent results in certain cases,and large uncertainties in low and thin cloud retrievals,which may lead to challenges in subsequent *** study proposes a synergistic algorithm that considers both O_(2)A-bands and polarized bands using a random forest(RF)*** CTP data are used as the true values and the polarized and non-polarized measurements are concatenated to train the RF model to determine ***,through analysis,we proposed that the polarized signal becomes saturated as the cloud optical thickness(COT)increases,necessitating a particular treatment for cases where COTapplication of DPC *** algorithm is expected to provide data support for atmosphere-related fields as an atmospheric remote sensing algorithm within the Cloud application for remote sensing,Atmospheric Radiation,and Updating Energy(CARE)platform.

关键词： atmospheric remote sensing cloud top pressure multi-angle polarized O_(2)A-band GF-5/DPC PARASOL/POLDER-3

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A rapid field crop data collection method for complexity cropping patterns using UAV and YOLOv3

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Frontiers of Earth Science 2024年第1期18卷 242-255页

作者： Huifang ZHANG Binyao WANG Zhonggang TANG Jiahui XUE Ruihang CHEN Hongcheng KAN Shumiao LU Lina FENG Ye HE Shuhua YI institute of Fragile Eco-environment Nantong UniversityNantong 226019China School of Geographic Science Nantong UniversityNantong 226019China Nantong Key Laboratory of Spatial information Technology R&D and application School of Geographic ScienceNantong UniversityNantong 226019China

Crop type mapping using remote sensing is critical for global agricultural monitoring and food ***,the complexity of crop planting patternsand spatial heterogeneity pose significant challenges to field data collection,thereby limiting the accuracy of remotely sensed crop *** study proposed a new approach for rapidly collecting field crop data by integrating unmanned aerial vehicle(UAV)images with the YOLOv3(You Only Look Once version 3)*** impacts of UAV flight altitude and the number of training samples on the accuracy of crop identification models were investigated using peanut,soybean,and maize as *** results showed that the average Fl-score for crop type detection accuracy reached 0.91 when utilizing UAV images captured at an altitude of 20 *** addition,a positive correlation was observed between identification accuracy and the number of training *** model developed in this study can rapidly and automatically identify crop types from UAV images,which significantly improves the survey efficiency and provides an innovative solution for acquiring field crop data in large areas.

关键词： deep learning flight altitude crop survey peanut soybean maize

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Knowledge-Based Classification in Automated Soil Mapping

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Pedosphere 2003年第3期13卷 209-218页

作者： ZHOU BIN and WANG RENCHAOinstitute of agricultural remote sensing and information Technology application, zhejiang university, Hangzhou 310029 (China) institute of agricultural remote sensing and information Technology application Zhejiang University Hangzhou 310029

A machine-learning approach was developed for automated building of knowledgebases for soil resources mapping by using a classification tree to generate knowledge from trainingdata. With this method, building a knowledge base for automated soil mapping was easier than usingthe conventional knowledge acquisition approach. The knowledge base built by classification tree wasused by the knowledge classifier to perform the soil type classification of Longyou County,zhejiang Province, China using Landsat TM bi-temporal images and CIS data. To evaluate theperformance of the resultant knowledge bases, the classification results were compared to existingsoil map based on a field survey. The accuracy assessment and analysis of the resultant soil mapssuggested that the knowledge bases built by the machine-learning method was of good quality formapping distribution model of soil classes over the study area.

关键词： classification classification tree knowledge-based rule extracting soilmapping

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