Improved Gaussian mixture model to map the flooded crops of VV and VH polarization data

Remote Sensing of Environment, Год журнала: 2023, Номер 295, С. 113714 - 113714

Опубликована: Июль 18, 2023

Язык: Английский

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Monitoring Low-Temperature Stress in Winter Wheat Using TROPOMI Solar-Induced Chlorophyll Fluorescence

IEEE Transactions on Geoscience and Remote Sensing, Год журнала: 2024, Номер 62, С. 1 - 11

Опубликована: Янв. 1, 2024

Solar-induced chlorophyll fluorescence (SIF) shows potential in exploring plant responses to environmental changes caused by extreme climatic factors. However, how accurately assess climate stresses (especially the low-temperature stress) suffered on crops at regional scale a systematic approach has not been extensively explored. In this study, we developed vegetation stress index (CVSI) and quantify impacts of large scales combining TROPOspheric Monitoring Instrument (TROPOMI) SIF land surface temperature (LST) data through an easy-to-operate approach. This was employed identify conditions Henan Province's winter wheat 2018. Results indicate that, influenced characteristics, northern part Province experienced more severe than those southern part. The daily average values reductions 0.74, 0.45, 0.61, 0.86 mW $\cdot ~\text{m}^{-2}~\cdot $ sr notation="LaTeX">$^{-1}~\cdot nm−1 during four cooling episodes within two phenological periods, respectively. As intensified, growth hindered, reducing grain yield. Indeed, CVSI provides accurate depiction crop levels patterns. areas with high-CVSI values, yield losses are particularly severe. addition, significant positive correlation between net primary productivity (NPP), along similar spatial intensity pattern, effectiveness monitoring stress. new understand change overwintering offers practical reference for effects scale.

Язык: Английский

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Performance of GEDI data combined with Sentinel-2 images for automatic labelling of wall-to-wall corn mapping

International Journal of Applied Earth Observation and Geoinformation, Год журнала: 2024, Номер 127, С. 103643 - 103643

Опубликована: Янв. 9, 2024

Corn is the dominant crop planted in Northeast China, and its accurate timely mapping important for food security agricultural management China. However, absence of enough labels challenging corn a regional area using machine learning methods or deep methods. In this study, an efficient way automatic labelling areas by combining Global Ecosystem Dynamics Investigation (GEDI) data Sentinel-2 images proposed. We explore height vertical structure differences between other crops derived from GEDI features generate automatically referencing type products transferring models historical years. The trained networks points decision trees Random Forest (RF) classifier can be transferred to arbitrary target are combined perform wall-to-wall random forest algorithm GEDI-based labels. This approach used map China 2019 2022, classification results validated independent collected field campaigns 2023, published maps, official statistics. Our reveal that our proposed method achieves high accuracy robustness with average overall 0.91 testing spatial-type stratified sampling. correlation coefficient (R2) classified result statistical reach 0.96 0.98, respectively. These demonstrate potential label collection vegetation difference provide new on large-scale.

Язык: Английский

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Automatic Rice Early-Season Mapping Based on Simple Non-Iterative Clustering and Multi-Source Remote Sensing Images

Remote Sensing, Год журнала: 2024, Номер 16(2), С. 277 - 277

Опубликована: Янв. 10, 2024

Timely and accurate rice spatial distribution maps play a vital role in food security social stability. Early-season mapping is of great significance for yield estimation, crop insurance, national policymaking. Taking Tongjiang City Heilongjiang Province with strong heterogeneity as study area, hierarchical K-Means binary automatic classification method based on phenological feature optimization (PFO-HKMAR) proposed, using Google Earth Engine platform Sentinel-1/2, Landsat 7/8 data. First, SAR backscattering intensity time series reconstructed used to construct optimize polarization characteristics. A new index named VH-sum built, which defined the summation VH specific periods temporal changes characteristics different land cover types. Then comes selection, optimization, reconstruction optical Finally, PFO-HKMAR established Simple Non-Iterative Clustering. can achieve early-season one month before harvest, overall accuracy, Kappa, F1 score reaching 0.9114, 0.8240 0.9120, respectively (F1 greater than 0.9). Compared two datasets Northeast China ARM-SARFS, scores are improved by 0.0507–0.1957, 0.1029–0.3945, 0.0611–0.1791, respectively. The results show that be promoted enable mapping, provide valuable timely information stakeholders decision makers.

Язык: Английский

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Early-Season Crop Classification Based on Local Window Attention Transformer with Time-Series RCM and Sentinel-1

Remote Sensing, Год журнала: 2024, Номер 16(8), С. 1376 - 1376

Опубликована: Апрель 13, 2024

Crop classification is indispensable for agricultural monitoring and food security, but early-season mapping has remained challenging. Synthetic aperture radar (SAR), such as RADARSAT Constellation Mission (RCM) Sentinel-1, can meet higher requirements on the reliability of satellite data acquisition with all-weather all-day imaging capability to supply dense observations in early crop season. This study applied local window attention transformer (LWAT) time-series SAR data, including RCM classification. The performance this integration was evaluated over crop-dominated regions (corn, soybean wheat) southwest Ontario, Canada. Comparative analyses against several machine learning deep methods revealed superiority LWAT, achieving an impressive F1-score 97.96% a Kappa coefficient 97.08% northern region F1-scores 98.07% 97.02% southern when leveraging from respectively. Additionally, by incremental procedure, evolution accuracy determined Sentinel-1 analyzed, which demonstrated that performed better at beginning season could achieve comparable achieved utilizing both datasets. Moreover, stem elongation corn identified crucial phenological stage acquire acceptable maps explores potential provide reliable prior information enough assist in-season production forecasting decision making.

Язык: Английский

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A novel and robust method for large-scale single-season rice mapping based on phenology and statistical data

ISPRS Journal of Photogrammetry and Remote Sensing, Год журнала: 2024, Номер 213, С. 14 - 32

Опубликована: Май 25, 2024

Язык: Английский

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Mapping annual 10-m soybean cropland with spatiotemporal sample migration

Scientific Data, Год журнала: 2024, Номер 11(1)

Опубликована: Май 2, 2024

Abstract China, as the world’s biggest soybean importer and fourth-largest producer, needs accurate mapping of its planting areas for global food supply stability. The challenge lies in gathering collating ground survey data different crops. We proposed a spatiotemporal migration method leveraging vegetation indices’ temporal characteristics. This uses feature space six integrals from crops’ phenological curves concavity-convexity index to distinguish non-soybean samples cropland. Using limited number actual our method, we extracted features optical time-series images throughout growing season. cloud rain-affected were supplemented with SAR data. then used random forest algorithm classification. Consequently, developed 10-meter resolution ChinaSoybean10 maps ten primary soybean-producing provinces 2019 2022. map showed an overall accuracy about 93%, aligning significantly statistical yearbook data, confirming reliability. research aids growth monitoring, yield estimation, strategy development, resource management, scarcity mitigation, promotes sustainable agriculture.

Язык: Английский

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An automated sample generation method by integrating phenology domain optical-SAR features in rice cropping pattern mapping

Remote Sensing of Environment, Год журнала: 2024, Номер 314, С. 114387 - 114387

Опубликована: Авг. 28, 2024

Язык: Английский

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Customized crop feature construction using genetic programming for early- and in-season crop mapping

Computers and Electronics in Agriculture, Год журнала: 2025, Номер 231, С. 109949 - 109949

Опубликована: Янв. 23, 2025

Язык: Английский

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Large-Scale Monitoring of Potatoes Late Blight Using Multi-Source Time-Series Data and Google Earth Engine

Remote Sensing, Год журнала: 2025, Номер 17(6), С. 978 - 978

Опубликована: Март 11, 2025

Effective monitoring and management of potato late blight (PLB) is essential for sustainable agriculture. This study describes a methodology to improve PLB identification on large scale. The method combines unsupervised supervised machine learning algorithms. To the accuracy regression model, used K-Means algorithm in conjunction with morphological operations identify growth areas. Input data consisted monthly NDVI from Sentinel-2 VH bands Sentinel-1 (covering year 2021). results were validated 221 field survey samples an F1 score 0.95. monitor disease severity, we compared seven models: CART decision trees (CART), Gradient Tree Boosting (GTB), Random Forest (RF), single optical Time series model (TS–RF), radar (STS–RF), multi-source (MSTS–GTB), (MSTS–RF). MSTS–RF was best performer, validation RMSE 20.50 R² 0.71. input spectral indices (NDVI, NDWI, NDBI, etc.), features (VH-band VV-band), texture features, synthesized as time May September 2021. feature importance analysis highlights key identification: NIR band (B8) Sentinel-2, DVI, SAVI, Sentinel-1. Notably, blue (458–523 nm) critical during month May. These are related vegetation health soil moisture early detection. presents first large-scale map distribution China 10 m 26.52. provides valuable support agricultural management, demonstrating effectiveness practical potential proposed monitoring.

Язык: Английский

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