Review
Agriculture, Multidisciplinary
Mengqi Li, Meiling Liu, Xiangnan Liu, Tao Peng, Shuyu Wang
Summary: This study aimed to distinguish heavy metal stress in rice using agricultural remote sensing and physiological function variability. Monitoring physiological functions can detect heavy metal contamination even in early stages or at low stress levels. The results showed that combining spatiotemporal features with signal decomposition can accurately identify heavy metal stress in rice and describe the temporal stability of spatial aggregation.
COMPUTERS AND ELECTRONICS IN AGRICULTURE
(2022)
Review
Green & Sustainable Science & Technology
Rongkun Zhao, Yuechen Li, Mingguo Ma
Summary: Paddy rice is a staple food for three billion people worldwide, and there are various mapping methods available for estimating paddy rice planting area and yield. The best methods include multisource data integration, machine learning, and radar mapping.
Article
Environmental Sciences
Jing Zhang, Huaqing Wu, Zhao Zhang, Liangliang Zhang, Yuchuan Luo, Jichong Han, Fulu Tao
Summary: Detecting changes in crop calendar is crucial for crop monitoring and management. However, the lack of annual, Asia-wide, and long-term rice calendar datasets limits our understanding of rice phenological changes and their climate drivers. This study analyzed rice phenological changes from 1995 to 2015 and identified solar radiation, temperature, and other climate variables as the key drivers of rice calendar changes.
Article
Biodiversity Conservation
Anna Kato, Kimberly M. Carlson, Tomoaki Miura
Summary: The study reveals that satellite vegetation indices can effectively explain GPP and characterize phenological transition dates in dryland ecosystems. Among the indices, EVI performs the best at explaining variance in daily GPP. While most phenological events in GPP data can be detected from VI data, the accuracy varies among different vegetation indices.
ECOLOGICAL INDICATORS
(2021)
Article
Geochemistry & Geophysics
Xun Liu, Danfeng Hong, Jocelyn Chanussot, Baojun Zhao, Pedram Ghamisi
Summary: The study explores the modality translation issues in remote sensing and proposes a novel multimodality image translation framework that utilizes time-series data to resolve ambiguities. The results show that the proposed model performs superiorly in two cross-modality translation tasks.
IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING
(2022)
Article
Agronomy
Nirajan Luintel, Weiqiang Ma, Yaoming Ma, Binbin Wang, Jie Xu, Binod Dawadi, Bhogendra Mishra
Summary: This study investigated the spatial and temporal variation of rice cultivation in Nepal between 2003 and 2018 using MODIS data and the PhenoRice algorithm. The results showed that rice cultivation is concentrated in the low elevation belt in the south, with differences in cultivation timing between regions. Over the past decade, there has been a significant decrease in rice cultivated area, particularly in the eastern plains, while expanding in the mid-hills in the western region.
AGRICULTURAL AND FOREST METEOROLOGY
(2021)
Article
Environmental Sciences
Shuai Zhang, Tamlin M. Pavelsky, Christopher D. Arp, Xiao Yang
Summary: A remote sensing-derived lake ice phenology database covering all lakes in Alaska from 2000 to 2019 was constructed to analyze the trends of earlier breakup and later freezeup of lake ice in the region. The dataset showed significant trends towards earlier or later ice breakup and freezeup for various lakes, with most significant trends observed in lakes north of the Brooks Range. This dataset contributes to the understanding of interactions between lake processes and climate change, supporting research on biogeochemical, limnological, and ecological regimes in Alaska and pan-Arctic regions.
ENVIRONMENTAL RESEARCH LETTERS
(2021)
Article
Agriculture, Multidisciplinary
Yi Peng, Alexei Solovchenko, Chaoran Zhang, Boris Shurygin, Xiaojuan Liu, Xianting Wu, Yan Gong, Shenghui Fang, Anatoly Gitelson
Summary: Rice is a vital staple crop that feeds over half of the global population. A systematic application of canopy-level reflectance-derived absorption coefficients was used to monitor rice stands with unmanned aerial vehicle multispectral sensors. This approach was capable of assessing rice phenology and physiology traits, such as canopy absorption and crop yield, providing valuable insights into rice phenology and physiology in the field.
PRECISION AGRICULTURE
(2023)
Review
Plant Sciences
Miao Xu, Qi Zhang, Xiuyun Lin, Yuqing Shang, Xiyan Cui, Liquan Guo, Yuanrui Huang, Ming Wu, Kai Song
Summary: This research provides a detailed explanation of the hazardous and advantageous impacts of common metal oxide nanomaterials, such as iron oxide, copper oxide, and zinc oxide, on the life cycle of rice. The study analyzes various aspects including nanoparticle transport patterns in rice, plant reactions to stress, reduction of heavy metal stress, and improvement of rice quality by metal oxide nanoparticles. It emphasizes the need for further research on the molecular mechanisms of the effects of metal oxide nanoparticles on rice and the combined use with other biological media.
Article
Engineering, Environmental
Yue You, Lijuan Liu, Yu Wang, Jiaxin Li, Zhining Ying, Zhilin Hou, Huijun Liu, Shaoting Du
Summary: The study found that while graphene oxide (GO) reduced the accumulation of Cd in rice seedlings, it had adverse effects on plant growth under co-contamination conditions with Cd. Therefore, the impacts of GO on crop production, both positive and negative, should be a concern.
JOURNAL OF HAZARDOUS MATERIALS
(2021)
Article
Environmental Sciences
Li Pan, Haoming Xia, Xiaoyang Zhao, Yan Guo, Yaochen Qin
Summary: By utilizing time series data and phenological characteristics, this study extracted winter crop planting information in China in 2019. It shows that the phenology-based algorithm is reliable and accurate for large area crop classification.
Article
Environmental Sciences
Lin Long, Yuanyuan Chen, Shaojun Song, Xiaoli Zhang, Xiang Jia, Yagang Lu, Gao Liu
Summary: Under the influence of climate change and human activities, the frequency and intensity of disturbance events in the forest ecosystem are increasing. The pine wood nematode is a major invasive species in China and has caused significant damage to coniferous forests. We conducted a study in Anhui Province using remote sensing data and statistical analysis to monitor and predict pine wilt disease. Our results show that the proposed monitoring model based on random forest classification can accurately extract coniferous forest areas and monitor the infection stage of pine wilt disease. High temperature and low humidity contribute to the spread of pine wilt disease. The study highlights the importance of remote sensing data and monitoring for effective control of pine wilt disease.
Article
Biodiversity Conservation
Jingjing Cao, Xin Xu, Li Zhuo, Kai Liu
Summary: This study investigated the phenological characteristics of mangrove forests in coastal China using remote sensing technology. The results showed that Sentinel-2 data was the most effective in describing the phenological characteristics of mangroves. The study also identified the influence of meteorological factors on mangrove phenology.
ECOLOGICAL INDICATORS
(2023)
Article
Environmental Sciences
Longzhe Quan, Zhaoxia Lou, Xiaolan Lv, Deng Sun, Fulin Xia, Hailong Li, Wenfeng Sun
Summary: This study demonstrates the negative impact of weeds on maize yield through competition and proposes a multimodal deep learning model to predict weed competitiveness at different stages. The research provides a foundation for scientific farmland weed management.
JOURNAL OF ENVIRONMENTAL MANAGEMENT
(2023)
Article
Geography
Christoph Raab, Michael Spies
Summary: Following the dissolution of the Soviet Union, agricultural reforms in Central Asia led to the fragmentation of large fields into smaller shares. We have developed an approach using textural information from Landsat images to detect the timing of land fragmentation in four Central Asian countries. Our results correspond well with documented agrarian reform processes and show potential for accurately detecting changes on a local scale where land use information is limited.