Search Results - "Agricultural and forest meteorology"

Integrating satellite and climate data to predict wheat yield in Australia using machine learning approaches by Cai, Yaping, Guan, Kaiyu, Lobell, David, Potgieter, Andries B., Wang, Shaowen, Peng, Jian, Xu, Tianfang, Asseng, Senthold, Zhang, Yongguang, You, Liangzhi, Peng, Bin

“…•Machine learning algorithms have been applied to estimate wheat yield for Australia.•Combining climate and satellite data achieves high performance for yield…”
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Representativeness of Eddy-Covariance flux footprints for areas surrounding AmeriFlux sites by Chu, Housen, Luo, Xiangzhong, Ouyang, Zutao, Chan, W. Stephen, Dengel, Sigrid, Biraud, Sébastien C., Torn, Margaret S., Metzger, Stefan, Kumar, Jitendra, Arain, M. Altaf, Arkebauer, Tim J., Baldocchi, Dennis, Bernacchi, Carl, Billesbach, Dave, Black, T. Andrew, Blanken, Peter D., Bohrer, Gil, Bracho, Rosvel, Brown, Shannon, Brunsell, Nathaniel A., Chen, Jiquan, Chen, Xingyuan, Clark, Kenneth, Desai, Ankur R., Duman, Tomer, Durden, David, Fares, Silvano, Forbrich, Inke, Gamon, John A., Gough, Christopher M., Griffis, Timothy, Helbig, Manuel, Hollinger, David, Humphreys, Elyn, Ikawa, Hiroki, Iwata, Hiroki, Ju, Yang, Knowles, John F., Knox, Sara H., Kobayashi, Hideki, Kolb, Thomas, Law, Beverly, Lee, Xuhui, Litvak, Marcy, Liu, Heping, Munger, J. William, Noormets, Asko, Novick, Kim, Oberbauer, Steven F., Oechel, Walter, Oikawa, Patty, Papuga, Shirley A., Pendall, Elise, Prajapati, Prajaya, Prueger, John, Quinton, William L, Richardson, Andrew D., Russell, Eric S., Scott, Russell L., Starr, Gregory, Staebler, Ralf, Stoy, Paul C., Stuart-Haëntjens, Ellen, Sonnentag, Oliver, Sullivan, Ryan C., Suyker, Andy, Ueyama, Masahito, Vargas, Rodrigo, Wood, Jeffrey D., Zona, Donatella

“…•Large-scale eddy-covariance flux datasets need to be used with footprint-awareness•Using a fixed-extent target area across sites can bias model-data…”
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Assimilation of remote sensing into crop growth models: Current status and perspectives by Huang, Jianxi, Gómez-Dans, Jose L., Huang, Hai, Ma, Hongyuan, Wu, Qingling, Lewis, Philip E., Liang, Shunlin, Chen, Zhongxin, Xue, Jing-Hao, Wu, Yantong, Zhao, Feng, Wang, Jing, Xie, Xianhong

“…•Provides strategies of choice of remote sensing-crop model data assimilation methods.•Focuses on the key issues to improving the performance of data…”
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Evaluation of SVM, ELM and four tree-based ensemble models for predicting daily reference evapotranspiration using limited meteorological data in different climates of China by Fan, Junliang, Yue, Wenjun, Wu, Lifeng, Zhang, Fucang, Cai, Huanjie, Wang, Xiukang, Lu, Xianghui, Xiang, Youzhen

“…•Potential of tree-based ensemble models for daily ET0 estimation with limited climatic data is explored.•Proposed ensemble models are compared with their…”
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Review of indirect optical measurements of leaf area index: Recent advances, challenges, and perspectives by Yan, Guangjian, Hu, Ronghai, Luo, Jinghui, Weiss, Marie, Jiang, Hailan, Mu, Xihan, Xie, Donghui, Zhang, Wuming

“…•Latest progress and prospects of indirect LAI measurement.•Comprehensive review of theories, algorithms, instruments, and challenges.•Appealing new means of…”
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Grain yield prediction of rice using multi-temporal UAV-based RGB and multispectral images and model transfer – a case study of small farmlands in the South of China by Wan, Liang, Cen, Haiyan, Zhu, Jiangpeng, Zhang, Jiafei, Zhu, Yueming, Sun, Dawei, Du, Xiaoyue, Zhai, Li, Weng, Haiyong, Li, Yijian, Li, Xiaoran, Bao, Yidan, Shou, Jianyao, He, Yong

“…lNDYI could be proxy for leaf chlorophyll content to monitor rice growth.lFusion of multi-temporal UAV image data achieved the best prediction of grain…”
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Satellite-based soybean yield forecast: Integrating machine learning and weather data for improving crop yield prediction in southern Brazil by Schwalbert, Raí A., Amado, Telmo, Corassa, Geomar, Pott, Luan Pierre, Prasad, P.V.Vara, Ciampitti, Ignacio A.

“…•Soybean yield at municipality-level was forecasted using satellite and weather data.•LSTM neural networks outperformed conventional machine learning…”
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Increasing Tibetan Plateau terrestrial evapotranspiration primarily driven by precipitation by Ma, Ning, Zhang, Yongqiang

“…•Long-term mean TP-averaged annual ET is 353±24 mm, with 64% from soil evaporation.•Annual ET from TP increased significantly during 1982–2016.•Precipitation…”
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The impact of the 2009/2010 drought on vegetation growth and terrestrial carbon balance in Southwest China by Li, Xiangyi, Li, Yue, Chen, Anping, Gao, Mengdi, Slette, Ingrid J., Piao, Shilong

“…•The 2009/2010 drought in Southwest China has damaged vegetation growth.•Ecosystem impacts by drought vary across space and vegetation types.•Precipitation…”
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Integrating Multi-Source Data for Rice Yield Prediction across China using Machine Learning and Deep Learning Approaches by Cao, Juan, Zhang, Zhao, Tao, Fulu, Zhang, Liangliang, Luo, Yuchuan, Zhang, Jing, Han, Jichong, Xie, Jun

“…•Adapting three machine/deep learning models to predict rice yield at county-level•LSTM performs better than RF and LASSO models•Combining EVI and SIF together…”
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Inter-annual variability of net and gross ecosystem carbon fluxes: A review by Baldocchi, Dennis, Chu, Housen, Reichstein, Markus

“…•Interannual variability in net carbon exchange is large relative to its mean.•Many biophysical variables explain this variability, and differ by…”
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Ear density estimation from high resolution RGB imagery using deep learning technique by Madec, Simon, Jin, Xiuliang, Lu, Hao, De Solan, Benoit, Liu, Shouyang, Duyme, Florent, Heritier, Emmanuelle, Baret, Frédéric

“…•The density of wheat ears was estimated from high resolution RGB imagery.•The Faster-RCNN object detection model was more robust than TasselNet counting by…”
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Tree mortality of European beech and Norway spruce induced by 2018-2019 hot droughts in central Germany by Obladen, Nora, Dechering, Pia, Skiadaresis, Georgios, Tegel, Willy, Keßler, Joachim, Höllerl, Sebastian, Kaps, Sven, Hertel, Martin, Dulamsuren, Choimaa, Seifert, Thomas, Hirsch, Mareike, Seim, Andrea

“…•Extensive beech and spruce dieback observed after hot droughts in 2018 and 2019.•Dead and healthy spruce and beech trees from central Germany were…”
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Hybrid artificial intelligence models based on a neuro-fuzzy system and metaheuristic optimization algorithms for spatial prediction of wildfire probability by Jaafari, Abolfazl, Zenner, Eric K., Panahi, Mahdi, Shahabi, Himan

“…[Display omitted] •Illustration of the utility of intelligent hybrid ensemble modeling of natural hazards.•Optimization of ANFIS parameters using four…”
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Detecting and attributing vegetation changes on China’s Loess Plateau by Li, Jingjing, Peng, Shouzhang, Li, Zhi

“…•A new method is developed to separate the impacts of environmental changes on vegetation.•Vegetation has been increasing after 1999 on China’s Loess…”
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Diverse responses of vegetation growth to meteorological drought across climate zones and land biomes in northern China from 1981 to 2014 by Xu, Hao-jie, Wang, Xin-ping, Zhao, Chuan-yan, Yang, Xue-mei

“…•Remotely sensed metrics are effective for monitoring vegetation responses to meteorological drought.•Drought resistance is associated with water balance and…”
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Climate change, phenology, and phenological control of vegetation feedbacks to the climate system by Richardson, Andrew D., Keenan, Trevor F., Migliavacca, Mirco, Ryu, Youngryel, Sonnentag, Oliver, Toomey, Michael

“…► The sensitivity of vegetation phenology to climate change varies among biomes. ► Key weaknesses in our current understanding of phenology drivers are…”
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Transpiration in the global water cycle by Schlesinger, William H., Jasechko, Scott

“…•A literature survey revealed 81 studies that have partitioned T and E as components of ET in various ecosystems worldwide.•Transpiration accounts for 39% of…”
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Spatiotemporal vegetation cover variations associated with climate change and ecological restoration in the Loess Plateau by Sun, Wenyi, Song, Xiaoyan, Mu, Xingmin, Gao, Peng, Wang, Fei, Zhao, Guangju

“…•The greenness of vegetation exhibited a significant increase from 1981 to 2010.•The vegetation growing periods were prolonged during the past three…”
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A hybrid artificial intelligence approach using GIS-based neural-fuzzy inference system and particle swarm optimization for forest fire susceptibility modeling at a tropical area by Tien Bui, Dieu, Bui, Quang-Thanh, Nguyen, Quoc-Phi, Pradhan, Biswajeet, Nampak, Haleh, Trinh, Phan Trong

“…•PSO-NF is proposed for forest fire modeling.•PSO-NF has high performance on the training and the validation datasets.•PSO-NF outperforms benchmark models, RF…”
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