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Association of residential greenness exposures on disability: Findings from the cohort study on global AGEing and Adult Health (SAGE) in China

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ENVIRONMENTAL RESEARCH 2025年第PT 1期264卷 120358页

作者： Chen, Zhiqing Shi, Yan Guo, Yanfei Yu, Siwen Zhu, Qijiong Yang, Shangfeng Zheng, Yuan Li, Yayi Huang, Yixiang Peng, Wan He, Guanhao Hu, Jianxiong Dong, Xiaomei Wu, Fan Ma, Wenjun Liu, Tao Jinan Univ Sch Med Dept Publ Hlth & Prevent Med 601 West Huangpu Rd Guangzhou 510632 Peoples R China Jinan Univ Sch Med China Greater Bay Area Res Ctr Environm Hlth Guangzhou 510632 Peoples R China Jinan Univ Minist Educ Key Lab Viral Pathogenesis & Infect Prevent & Cont Guangzhou 510632 Peoples R China Shanghai Municipal Ctr Dis Control & Prevent Shanghai 200336 Peoples R China Fudan Univ Shanghai Med Coll 138 Yi Xue Yuan Rd Shanghai 200032 Peoples R China

Background: With the accelerating population ageing globally, disability has become a major public concern. Residential greenness may be one of the influencing factors of disability, but epidemiological evidence in the associations of residential greenness exposures with disability is limited. We aimed to investigate the associations of residential greenness exposures with the risk of disability in the elderly. Methods: Data of 8408 residents were obtained from the World Health Organization Study on Global AGEing and Adult Health (WHO SAGE) implemented in China during 2007-2018. Participants were matched to the normalized difference vegetation index (NDVI) and Enhanced vegetation index (EVI) at their residential address. Disability was measured by the 12-item Chinese version of the World Health Organization Disability Assessment Schedule (WHODAS 2.0). The associations were examined using a generalized linear mixed model with stratified analyses by the covariates. Results: We observed significantly negative associations of greenness exposures with the summary WHODAS score [NDVI500m:-0.290, 95% Confidence Intervals (95%CI): -0.510, -0.070;EVI500m:-0.453, 95%CI: -0.757, -0.149], and with the score of cognition (EVI500m:-0.472, 95%CI: -0.881, -0.063), mobility (NDVI500m:-0.632, 95%CI: -0.965, -0.299;EVI500m:-0.739, 95%CI: -1.199, -0.280), and participation (NDVI500m:-0.388, 95%CI: -0.651, -0.125;EVI500m:-0.530, 95%CI: -0.893, -0.166). People living alone had a more pronounced association in cognition (NDVI500m:-1.546, 95%CI: -2.471, -0.621). The associations with summary WHODAS score were stronger among participants living in rural areas (NDVI500m:-0.420, 95% CI: -0.683, -0.157), having less education level (NDVI500m:-0.618, 95%CI: -0.982, -0.253), and living in northern China (NDVI500m:-0.381, 95%CI: -0.776, 0.013). Conclusions: Residential greenness may reduce the onset and worsening of disability, particularly for domains of cognition, mobility, and social participat

关键词： Residential greenness Disability normalized difference vegetation index Enhanced vegetation index Cohort study

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Estimation and Evaluation of the FY-3D/MERSI-II Fractional Snow Cover in China

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IEEE JOURNAL OF SELECTED TOPICS IN APPLIED EARTH OBSERVATIONS AND REMOTE SENSING 2025年 18卷 2497-2511页

作者： Huang, Jinyu Jiang, Lingmei Pan, Fangbo Zhong, Bo Wu, Shengli Cui, Huizhen Beijing Normal Univ State Key Lab Remote Sensing Sci Beijing 100875 Peoples R China Chinese Acad Sci Beijing Normal Univ Fac Geog Sci Aerosp Informat Res Inst Beijing 100875 Peoples R China Chinese Acad Sci Aerosp Informat Res Inst Beijing 100101 Peoples R China Natl Satellite Meteorol Ctr Beijing 100081 Peoples R China Chinese Acad Sci Natl Space Sci Ctr Beijing 100190 Peoples R China

The frequent occurrence of intense snowfall events and extreme temperature fluctuations in China significantly hampers the precise monitoring of snow-covered areas. The provision of near-real-time monitoring of fractional snow cover (FSC) in China is essential to bolster disaster management initiatives. The medium-resolution spectral imager (MERSI-II), aboard the FengYun-3D satellite, presents significant capabilities for effective snow monitoring. The aim of this article was to enhance the multiple endmember spectral mixture algorithm based on automatic extraction of endmembers for estimating FY-3D/MERSI-II FSC in China. The accuracy of these estimations was corroborated using 30-m FSC maps derived from Landsat-8/Operational Land Imager (OLI) images. This validation process resulted in RMSE and R-2 values of 0.13 and 0.88, respectively, across 253 OLI-based FSC scenes. The article examined and analyzed the effects of diverse land cover types and complex terrain on the FSC estimates, focusing specifically on three major snow-covered areas in China. In comparison to areas with cultivated land and sparse vegetation, bare surfaces yielded the most accurate results, whereas forest regions showed the lowest accuracy. The comparative analysis revealed that the Tibetan Plateau and Northern Xinjiang achieved high accuracy in mapping snow cover. The maps of Northeast China were less precise, largely due to the prevalence of forests. Additionally, while the MERSI-II FSC is effective in monitoring snow cover on flat plains, its performance is less reliable in steep terrain. Moreover, the comparison between MERSI-II and moderate-resolution imaging spectroradiometer FSC indicated that MERSI-II has a marked advantage in areas with substantial snow cover.

关键词： Snow Power capacitors Earth Artificial satellites MODIS Reflectivity Monitoring Spatial resolution normalized difference vegetation index Accuracy Fractional snow cover (FSC) area FY-3D spectral mixture analysis (SMA) validation

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An Improved Spatiotemporal SavitzkyGolay (iSTSG) Method to Improve the Quality of vegetation index Time-Series Data on the Google Earth Engine

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IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING 2025年 63卷

作者： Wang, Weiyi Cao, Ruyin Liu, Licong Zhou, Ji Shen, Miaogen Zhu, Xiaolin Chen, Jin Univ Elect Sci & Technol China Sch Resources & Environm Chengdu 611731 Peoples R China Beijing Normal Univ Inst Remote Sensing Sci & Engn Fac Geog Sci State Key Lab Earth Surface Proc & Resource Ecol Beijing 100875 Peoples R China Hong Kong Polytech Univ Dept Land Surveying & Geoinformat Hong Kong Peoples R China

MODerate-resolution Imaging Spectroradiometer (MODIS) vegetation index (VI) time-series data are among the most widely utilized remote sensing datasets. To improve the quality of MODIS VI time-series data, most prior methods have focused on correcting negatively biased VI noise by approaching the upper envelope of the VI time series. Such treatment, however, may cause overcorrections on some true local low VI values, resulting in inaccurate simulations of vegetation phenological characteristics. In addition, another challenge in reconstructing MODIS VI time series is to fill temporally continuous gaps. The earlier spatiotemporal Savitzky-Golay (STSG) method tackled this problem by utilizing multiyear VI data, but its performance heavily relies on the consistency of data across different years. In this study, we proposed an improved STSG (iSTSG) method. The new method accounts for the autocorrelation within the VI time series and fills missing values in the VI time series by leveraging spatiotemporal VI data from the current year alone. Furthermore, iSTSG incorporates an indicator to quantify potential overcorrections in the VI time series, aiming to more accurately simulate phenological characteristics. The experiments to reconstruct MODIS normalized difference VI (NDVI) time-series product (MOD13A2) at four typical sites (a million square kilometers for each site) suggest two clear advantages in iSTSG over the iterative SG (called Chen-SG) and STSG methods. First, iSTSG more accurately reconstructs the annual NDVI time series, exhibiting the smallest mean absolute differences (MADs) between the smoothed and the simulated reference NDVI time series (0.012, 0.018, and 0.020 for iSTSG, STSG, and Chen-SG, respectively). Second, iSTSG more effectively simulates phenological characteristics in the NDVI time series, including the onset dates for vegetation greenup and dormancy, as well as the crop harvest period. The advantages of iSTSG were also demonstrated when applied

关键词： Time series analysis MODIS Spatiotemporal phenomena vegetation mapping Image reconstruction Filters normalized difference vegetation index Phenology Crops US Department of Defense Crop phenology normalized difference vegetation index (NDVI) reconstruction time-series smooth vegetation phenology Visible Infrared Imaging Radiometer Suite (VIIRS) NDVI

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How are green spaces associated with chronic disease incidence in Australia? Direct health benefits and interactive effects with socioeconomic status based on multiple green space indicators

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SUSTAINABLE CITIES AND SOCIETY 2025年 121卷

作者： Zhang, Yunzheng Luo, Fubin Univ New South Wales Sch Built Environm Sydney NSW 2052 Australia City Univ Hong Kong Dept Architecture & Civil Engn Kowloon Tat Chee Ave Hong Kong Peoples R China

The health benefits of urban greenery are well-documented, yet findings vary depending on the green space indicators used. Given the limited Australia-based research incorporating both traditional and emerging indicators, this study employs the proportion of parkland, the normalized difference vegetation index (NDVI), and Google Street View Greenness (SVG), to explore how green spaces and their interactions with socioeconomic status relate to chronic disease incidence rates in Sydney at Statistical Area Levels 1 and 2 (SA1 and SA2). The findings indicate that: (1) While green spaces are not significantly related to overall chronic diseases, they show strong associations with specific diseases, particularly dementia (SA1: Park: coef. _ -0.004, p < 0.001;NDVI: coef. _ -0.008, p <0.001;SVG: coef. _ -0.009, p <0.001;SA2: SVG: coef. _ -0.005, p <0.05) and diabetes (SA1: SVG: coef. _ -0.013, p <0.001;SA2: Park: coef. _ -0.009, p <0.05;NDVI: coef. _ -0.016, p <0.01;SVG: coef. _ -0.033, p < 0.001), with SVG being the most prevalent predictor. (2) Green spaces, particularly parkland, may mitigate chronic disease risks in lower socioeconomic regions, especially in aging areas (SA1: age#Park: coef. _ -0.125, p <0.001;age#NDVI: coef. _ -0.081, p <0.01;age#SVG: coef. _ -0.078, p < 0.01). Additionally, these associations are more pronounced at the neighborhood scale than at the suburb scale. This study examines multiple green space indicators from an Australian perspective, offering insights for international comparisons and public health improvements.

关键词： Green space Parkland normalized difference vegetation index Street view greenness chronic disease

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Exploring soil health and sustainability in the Northwestern Himalayas: assessing indicators amidst changing land use

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ENVIRONMENTAL EARTH SCIENCES 2025年第8期84卷 1-17页

作者： Kumar, K. Arun Jayanthi, J. Singh, Raj Deo Sahu, Sanjat Kumar Hasan, Azimul VIT Bhopal Univ Sch Biosci Engn & Technol Sehore 466114 Madhya Pradesh India VIT Bhopal Univ Sch Comp Sci & Engn Sehore 466114 Madhya Pradesh India Indian Agr Res Inst Div Soil Sci & Agr Chem New Delhi 110012 India Sambalpur Univ Dept Environm Sci Jyoti Vihar Burla 768019 Odisha India

Soil quality and health are fundamental to ecosystem sustainability, particularly in the Northwestern Himalayas, where steep slopes, high erosion rates, and land use changes accelerate soil degradation. Unlike stable lowland ecosystems, Himalayan soils are highly vulnerable to nutrient loss, organic matter depletion, and microbial instability due to extreme climatic variations and dynamic land use patterns. This review consolidates research on soil health indicators, emphasizing microbial biomass carbon (MBC), enzyme activity, and organic carbon content as essential metrics for assessing soil quality. These indicators reflect biological, chemical, and physical processes influencing nutrient cycling, microbial diversity, and soil functionality. However, their predictive power varies with land use shifts, seasonal changes, and the inherent variability of Himalayan soils, necessitating region-specific calibration for improved accuracy. Organic matter decomposition is a key indicator of soil health, yet its assessment in rugged terrains is challenging. The normalized difference vegetation index (NDVI) provides a remote sensing solution, helping identify degraded soil zones and guiding targeted restoration efforts. NDVI alone cannot fully assess soil health, as vegetation cover may not always correspond to soil quality. This review evaluates NDVI's effectiveness across various land use systems for distinguishing vegetated from non-vegetated areas, facilitating reforestation and land use monitoring. Given the region's ecological sensitivity, a single indicator approach is insufficient. We advocate for a composite soil health assessment framework incorporating biochemical indicators such as enzyme activity, phospholipid fatty acid (PLFA) profiles, DNA profiling, carbon pools, and nitrogen levels. A gradient-based soil sampling strategy, rather than random sampling, is recommended to capture altitude and slope effects on microbial dynamics and soil function. While these app

关键词： Soil health indicators Ecosystem sustainability normalized difference vegetation index Soil enzyme activities Northwestern Himalayas Soil quality assessment

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Spatial Downscaling of Gridded Soil Moisture Products Using Optical and Thermal Satellite Data: Effect of Using Different vegetation Indices

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IEEE JOURNAL OF SELECTED TOPICS IN APPLIED EARTH OBSERVATIONS AND REMOTE SENSING 2025年 18卷 7728-7741页

作者： Alexander, Tomas Halldorsson Luan, Haijun Jin, Hongxiao Duan, Zheng Lund Univ Dept Phys Geog & Ecosyst Sci S-22362 Lund Sweden Xiamen Univ Technol Sch Comp & Informat Engn Xiamen 361024 Peoples R China

Satellite remote sensing offers global-scale soil moisture (SM) estimation to assess water and energy cycles. However, the coarse resolution of SM products from microwave remote sensing is unsuitable for fine-scale analysis. This study explored spatial downscaling methods to refine the 0.25 degrees ESA CCI SM product to a 1-km resolution, utilizing optical and thermal remote sensing data, including the normalized difference vegetation index (NDVI), enhanced vegetation index (EVI), kernel NDVI (kNDVI), and plant phenology index (PPI), together with land surface temperature from MODIS products over two study areas in Europe. The vegetation temperature condition index based approach was used for downscaling, in which the wet and dry edges of the triangular feature space were determined by fitting a line to the maximum and minimum temperatures, respectively, for each vegetation index. The PPI-based downscaling showed consistent results between the two study areas, having a good correlation coefficient and unbiased root-mean-square deviation (ubRMSD) against the in-situ measurements. The NDVI-based downscaling had poor performance overall in terms of ubRMSD and correlation. Results from the EVI- and kNDVI-based methods varied in the two study areas. Compared with the original coarse SM product, spatially downscaled SM products exhibited inferior performance against in-situ SM measurements in terms of evaluation metrics.

关键词： Spatial resolution normalized difference vegetation index vegetation mapping Enhanced vegetation index Satellites MODIS Remote sensing Land surface temperature Ocean temperature Polynomials Surface soil moisture remote sensing spatial downscaling vegetation index vegetation temperature condition index (VTCI)

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Efficient and Effective NDVI Time-Series Reconstruction by Combining Deep Learning and Tensor Completion

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IEEE JOURNAL OF SELECTED TOPICS IN APPLIED EARTH OBSERVATIONS AND REMOTE SENSING 2025年 18卷 191-205页

作者： Li, Ang Jiang, Menghui Chu, Dong Guan, Xiaobin Li, Jie Shen, Huanfeng Wuhan Univ Sch Resource & Environm Sci Wuhan 430079 Peoples R China Anhui Normal Univ SchGeog & Tourism Key Lab Earth Surface Proc & Reg Response Yangtze Wuhu 241002 Anhui Peoples R China Wuhan Univ Sch Geodesy & Geomat Wuhan 430079 Peoples R China Wuhan Univ Sch Resource & Environm Sci Wuhan 430079 Peoples R China Wuhan Univ Key Lab Geog Informat Syst Minist Educ Wuhan 430079 Peoples R China Wuhan Univ Key Lab Digital Mapping & Land Informat Applicat Minist Nat Resources Wuhan 430079 Peoples R China

Reconstruction of normalized difference vegetation index (NDVI) time series plays an imperative part in the inference of vegetation dynamics. However, it is challenging for the existing methods to achieve a good balance between accuracy and efficiency. In this article, we novelly combine deep learning with a high-precision spatiotemporal adaptive tensor completion (ST-Tensor) method and propose an end-to-end NDVI time-series reconstruction network (NIT-Net). The ST-Tensor method is first used to generate high-quality seamless NDVI data as the label data to construct sample pairs, along with the original degraded observations. A handcrafted time-series processing network is further employed for effective and rapid reconstruction of the NDVI time series. Considering the temporal continuity and spatial correlation of NDVI time-series data, we combine long short-term memory with a convolution (LSTM-Conv) structure and utilize residual learning and dense connection strategies to mine the spatiotemporal features in depth. Multidimensional gradient constraints are introduced in the loss function to retain critical information. The experiments conducted on moderate resolution imaging spectroradiometer NDVI data show that the NIT-Net framework is superior to most of the comparison methods. The mean correlation coefficient between the reconstruction results of NIT-Net and ST-Tensor can reach 0.9955, while NIT-Net achieves a more than 14 times speed-up on a CPU, compared with ST-Tensor, and a 115 times speed-up on a GPU, which fully demonstrates its efficient performance and great practical application value.

关键词： Image reconstruction normalized difference vegetation index Tensors Deep learning Time series analysis vegetation mapping Correlation Training Fitting Accuracy long short-term memory (LSTM)-conv normalized difference vegetation index (NDVI) time-series reconstruction tensor completion

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Enhancing Urban Heat Island Analysis Through Multisensor Data Fusion and GRU-Based Deep Learning Approaches for Climate Modeling

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IEEE JOURNAL OF SELECTED TOPICS IN APPLIED EARTH OBSERVATIONS AND REMOTE SENSING 2025年 18卷 9279-9296页

作者： Tang, Ning Farhan, Muhammad Mohammad, Pir Abdullah-Al-Wadud, M. Hussain, Saddam Hamza, Umair Zulqarnain, Rana Muhammad Rebouh, Nazih Yacer Southwest Univ Sci & Technol Sch Environm & Resources Mianyang 621010 Peoples R China Hohai Univ Sch Earth Sci & Engn Nanjing 2100987 Peoples R China Hong Kong Polytech Univ Dept Land Surveying & Geoinformat JC STEM Lab Earth Observat Hong Kong Peoples R China Hong Kong Polytech Univ Res Ctr Artificial Intelligence Geomat Hong Kong Peoples R China Hong Kong Polytech Univ Res Inst Land & Space Hong Kong Peoples R China Hong Kong Polytech Univ Dept Land Surveying & Geoinformat Hong Kong Peoples R China King Saud Univ Coll Comp & Informat Sci Dept Software Engn Riyadh 11543 Saudi Arabia Tongji Univ Coll Civil Engn Dept Geotech Engn Shanghai 200092 Peoples R China Henan Univ Coll Geog & Environm Sci Kaifeng 475004 Peoples R China SIMATS Thandalam Saveetha Sch Engn Dept Math Chennai 602105 India RUDN Univ Inst Environm Engn Dept Environm Management Moscow 117198 Russia

Rapid urbanization and land-use changes have exacerbated the urban heat island (UHI) effect, threatening urban sustainability and climate resilience. This study uses a novel gated recurrent unit (GRU)-based deep learning model in addition to the Mann-Kendall trend, Pearson correlation, and continuous wavelet to investigate the UHI phenomenon in Multan city of Pakistan. The approach utilizes the normalized difference vegetation index (NDVI) and normalized difference built-up index (NDBI) as essential variables to forecast UHI accurately using a GRU-based deep learning model using a monthly Landsat dataset from 2001 to 2023. The results from the Mann-Kendall test indicated a minor increase in monthly UHI values, accompanied by notable seasonal fluctuations with a substantial decrease in winter (Tau = -3.486), whereas a notable increase is observed in the summer season (Tau = 0.158). The NDVI exhibited a notable annual increase (Tau = 3.43), suggesting enhanced vegetation health. Conversely, NDBI showed a significant decrease (Tau = -0.907). The result of Pearson's correlation study showed that UHI is significantly negatively correlated with NDVI and positively with NDBI, with a correlation coefficient of -0.540 and 0.344, respectively. Wavelet coherence analysis revealed considerable seasonal and annual relationships between UHI, NDVI, and NDBI. The GRU-based model achieved a coefficient of determination (R-2) of 0.90 with an RMSE value of 0.09, indicating robust predictive performance. The SHAP (SHapley Additive explanations) analysis revealed that NDVI is the predictor with the most significant influence. The adopted approach emphasizes vegetation's crucial function in reducing UHI's effects and offers valuable insights for urban planning and measures to mitigate climate change.

关键词： Deep learning Wavelet analysis Climate change Urban areas Sustainable development normalized difference vegetation index Predictive models Urban planning Meteorology gated recurrent unit (GRU)-based modeling normalized difference vegetation index (NDVI) urban heat island (UHI) wavelet coherence

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Mapping Invasive Spartina alterniflora Using Phenological Information and Red-Edge Bands of Sentinel-2 Time-Series Data

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IEEE JOURNAL OF SELECTED TOPICS IN APPLIED EARTH OBSERVATIONS AND REMOTE SENSING 2025年 18卷 13-24页

作者： Ma, Yiwei Zhuo, Li Cao, Jingjing Sun Yat Sen Univ Guangdong Prov Engn Res Ctr Publ Secur & Disaster Sch Geog & Planning Guangzhou 510006 Peoples R China Southern Marine Sci & Engn Guangdong Lab Zhuhai Zhuhai 519082 Peoples R China Guangdong Polytech Normal Univ Coll Comp Sci Guangzhou 510665 Peoples R China

The accurate mapping of Spartina alterniflora (S. alterniflora) invasion is crucial for controlling its spread and reducing severe ecological problems. Satellite images have been extensively employed for S. alterniflora invasion monitoring;however, there are still several issues that need to be addressed. The spectral similarities between S. alterniflora and surrounding ground objects make it challenging for traditional classifiers to achieve satisfactory extraction accuracy. Since the phenological information and red-edge spectral differences have been considered as informative features for identifying S. alterniflora, current studies mainly used them separately as classification features and seldom considered the differences of red-edge information at different phenological periods. Therefore, we proposed a pixel-based phenological and red-edge feature composite method (PpRef-CM) for S. alterniflora extraction considering both phenological information and red-edge bands derived from Sentinel-2 time series based on the existing pixel-based phenological feature composite method (Ppf-CM). The proposed PpRef-CM and machine-learning algorithms were employed for S. alterniflora extraction in two typical mangrove forests along coastal China. Results indicated that red-edge information at different phenological periods is essential for detecting S. alterniflora. S. alterniflora extraction achieved the highest accuracy of 96.57% by using the eXtreme gradient boost algorithm when compared with other machine-learning algorithms. The PpRef-CM gave 2.72% and 2.61% more extraction accuracies of S. alterniflora than the Ppf-CM in two study sites, separately. These findings provide insights for selecting suitable classification features for S. alterniflora extraction studies and serve as an effective control and management of S. alterniflora.

关键词： Feature extraction normalized difference vegetation index Forests Time series analysis Sea measurements vegetation mapping Monitoring Remote sensing Earth Data mining Invasive species phenology red-edge bands Sentinel-2 time series Spartina alterniflora (S. alterniflora )

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Cloud gap-filling with deep learning for improved grassland monitoring

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COMPUTERS AND ELECTRONICS IN AGRICULTURE 2025年 230卷

作者： Tsardanidis, Iason Koukos, Alkiviadis Sitokonstantinou, Vasileios Drivas, Thanassis Kontoes, Charalampos Natl Observ Athens Inst Astron Astrophys Space Applicat & Remote Sens Operat Unit Ctr EO Res & Satellite Remote Sensing Athens 11523 Greece DHI Water & Environm DK-2970 Horsholm Denmark Univ Valencia Image Proc Lab IPL Parc Cientif Valencia 46980 Spain

Uninterrupted optical image time series are crucial for the timely monitoring of agricultural land changes, particularly in grasslands. However, the continuity of such time series is often disrupted by clouds. In response to this challenge, we propose an innovative deep learning method that integrates cloud-free optical (Sentinel-2) observations and weather-independent (Sentinel-1) Synthetic Aperture Radar (SAR) data. Our approach employs a hybrid architecture combining Convolutional Neural Networks (CNNs) and Recurrent Neural Networks (RNNs) to generate continuous normalized difference vegetation index (NDVI) time series, highlighting the role of NDVI in the synergy between SAR and optical data. We demonstrate the significance of observation continuity by assessing the impact of the generated NDVI time series on the downstream task of grassland mowing event detection. We conducted our study in Lithuania, a country characterized by extensive cloud coverage, and compared our approach with alternative interpolation techniques (i.e., linear, Akima, quadratic). Our method outperformed these techniques, achieving an average Mean Absolute Error (MAE) of 0.024 and a coefficient of determination (R2) of 0.92. Additionally, our analysis revealed improvement in the performance of the mowing event detection, with F1-score up to 84% using two widely applied mowing detection methodologies. Our method also effectively mitigated sudden shifts and noise originating from cloudy observations, which are often missed by conventional cloud masks and adversely affect mowing detection precision.

关键词： Satellite image time series normalized difference vegetation index Mowing detection Synthetic aperture radar imagery Image fusion

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