Article
Environmental Sciences
Yiru Ma, Qiang Zhang, Xiang Yi, Lulu Ma, Lifu Zhang, Changping Huang, Ze Zhang, Xin Lv
Summary: This study utilized a UAV equipped with a hyperspectral sensor to obtain hyperspectral images of cotton canopy and constructed an LAI monitoring model based on spectral reflectance and vegetation indices. The results showed that the model achieved the best performance after noise reduction and feature selection.
Article
Agronomy
Yoshihiro Hirooka, Koki Homma, Tatsuhiko Shiraiwa
Summary: Leaf canopy dynamics play a crucial role in crop productivity, and quantifying leaf area growth characteristics with a plant canopy analyzer and mathematical model can help understand the relationship between leaf area growth parameters and rice productivity. Variations in these parameters among rice cultivars, as well as differences in leaf nitrogen content, impact crop productivity and yield predictions. Ultimately, non-destructive measurements and parameterization provide an efficient method for monitoring crop growth and estimating productivity in field experiments.
Article
Agronomy
Lijun Su, Wanghai Tao, Yan Sun, Yuyang Shan, Quanjiu Wang
Summary: This paper analyzes the relationship between Leaf Area Index (LAI) and crop biomass production and yields. The researchers established universal models for LAI and accurately predicted LAI changes in extremely arid grape-growing areas using various models. The Michaelis-Menten model and quadratic polynomial function were used to predict dynamic changes in grapevine LAI, biomass, yields, and harvest index. This study provides insights for improving water use efficiency and determining optimal irrigation quotas in grape cultivation.
Article
Ecology
Demei Zhao, Jianing Zhen, Yinghui Zhang, Jing Miao, Zhen Shen, Xiapeng Jiang, Junjie Wang, Jincheng Jiang, Yuzhi Tang, Guofeng Wu
Summary: This study investigated the combined use of a radiative transfer model and a machine-learning model to estimate mangrove Leaf Area Index (LAI) using remote sensing images from different satellite sensors. The results showed that the Zhuhai-1 image had the best estimation accuracy, and newly developed three-band Vegetation Indices (VIs) proved effective in estimating mangrove LAI. Moreover, elevation and species composition were found to greatly influence the spatial distribution of mangrove LAI.
REMOTE SENSING IN ECOLOGY AND CONSERVATION
(2023)
Article
Geochemistry & Geophysics
Zhewei Zhang, Wenjie Jin, Ruyu Dou, Zhiwen Cai, Haodong Wei, Tongzhou Wu, Sen Yang, Meilin Tan, Zhijuan Li, Cong Wang, Gaofei Yin, Baodong Xu
Summary: Leaf area index (LAI) is an important indicator for monitoring vegetation growth and estimating crop yields. A novel chlorophyll-insensitive VI (CIVI) using red, red-edge, and near infrared (NIR) bands was proposed to improve regional LAI mapping. The CIVI showed good performance in capturing LAI variations while remaining insensitive to leaf chlorophyll content (C-ab) variations. It exhibited the best performance for LAI retrievals, especially for high LAI, among all selected VIs.
IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING
(2023)
Article
Geochemistry & Geophysics
Tian Liu, Huaan Jin, Xinyao Xie, Hongliang Fang, Dandan Wei, Ainong Li
Summary: This study proposes a bidirectional LSTM (Bi-LSTM) approach to improve the estimation of time series leaf area index (LAI). By integrating information from multiple satellite products, including Global Land Surface Satellite (GLASS), moderate-resolution imaging spectroradiometer (MODIS), and visible infrared imaging radiometer (VIIRS) LAI products, as well as MODIS reflectance, the Bi-LSTM method achieves better accuracy and smoother temporal profiles than other retrieval approaches.
IEEE GEOSCIENCE AND REMOTE SENSING LETTERS
(2022)
Article
Plant Sciences
Cong Zhou, Yan Gong, Shenghui Fang, Kaili Yang, Yi Peng, Xianting Wu, Renshan Zhu
Summary: This research developed a method to improve the accuracy of rice LAI estimation by combining spectral information and texture information. The results showed that the accuracy of LAI estimation by combining the two types of information was higher than using spectral information alone and not sensitive to the emergence of panicles. This method has great potential for large-scale auxiliary rice breeding and field management research.
FRONTIERS IN PLANT SCIENCE
(2022)
Article
Agronomy
Lidong Zou, Kayla Stan, Sen Cao, Zaichun Zhu
Summary: Systematic testing of vegetation and ecosystem response to environmental drivers in Dynamic Global Vegetation Models (DGVMs) is necessary to improve our understanding of the carbon cycle in Tropical Rainforests (TRFs). This study uses reprocessed monthly MODIS LAI products and in-situ data to evaluate the performance of 14 state-of-the-art models in simulating long-term trends, interannual variability, seasonality, and El Nin & SIM;o impacts on greenness in global TRFs. The results show that most DGVMs overestimate the long-term trend, fail to capture the fine-scale variability, and poorly represent the vegetation conditions during El Nin & SIM;o.
AGRICULTURAL AND FOREST METEOROLOGY
(2023)
Article
Environmental Studies
Gregoriy Kaplan, Offer Rozenstein
Summary: Satellite remote sensing is an effective tool for estimating crop variables such as Leaf Area Index (LAI). The study identified Sentinel-2 Band-8A as more accurate for LAI estimation compared to traditional Band-8, with Band-5 showing the lowest performance in tomato and cotton. A novel finding was the high correlation observed between Band 9 (Water vapor) and LAI, along with some bands showing saturation at specific LAI values in cotton and tomato. Additionally, new Vegetation Indices (VIs) like ReNDVI, WEVI, and WNEVI demonstrated higher LAI estimation performance than commonly used NDVI in agricultural monitoring.
Article
Remote Sensing
Huiren Tian, Pengxin Wang, Kevin Tansey, Dong Han, Jingqi Zhang, Shuyu Zhang, Hongmei Li
Summary: This study developed a novel deep learning framework, ALSTM model, to estimate winter wheat yield using remote sensing and meteorological data in the Guanzhong Plain. The ALSTM model showed improved estimation accuracy, generalization ability, and robustness, with LAI and VTCI identified as important features for yield estimation.
INTERNATIONAL JOURNAL OF APPLIED EARTH OBSERVATION AND GEOINFORMATION
(2021)
Article
Geography, Physical
Juan Li, Zhiqiang Xiao, Rui Sun, Jinling Song
Summary: In this study, a method based on deep transfer learning is proposed to retrieve LAI values from VIIRS surface reflectance data. The DBN is pretrained using an existing training dataset, and then fine-tuned using LAI ground measurement samples and corresponding VIIRS data. The results demonstrate the effectiveness of deep transfer learning in retrieving accurate LAI values.
ISPRS JOURNAL OF PHOTOGRAMMETRY AND REMOTE SENSING
(2023)
Article
Engineering, Electrical & Electronic
Yuanheng Sun, Binyu Wang, Zhaoxu Zhang
Summary: Leaf area index (LAI) is crucial for vegetation growth and health monitoring. In this study, a series of leaf chlorophyll insensitive red-edge vegetation indices (VIs) using Sentinel-2 and GF-6 multispectral images were developed to improve LAI estimation accuracy. The evaluation results show that these red-edge VIs perform better than other VIs, with the best regression coefficient (R-2 = 0.81 for Sentinel-2 and R-2 = 0.65 for GF-6) in crop LAI estimation.
IEEE JOURNAL OF SELECTED TOPICS IN APPLIED EARTH OBSERVATIONS AND REMOTE SENSING
(2023)
Article
Engineering, Electrical & Electronic
Ameni Mkaouar, Abdelaziz Kallel, Zouhaier Ben Rabah, Thouraya Sahli Chahed
Summary: This study proposed a method based on TLS point cloud to jointly estimate foliage density and leaf angle distribution, utilizing direct/inverse radiative transfer modeling and shuffled complex evolution method. The estimated values are close to the actual values, demonstrating the effectiveness of the approach in forest canopy characterization.
IEEE JOURNAL OF SELECTED TOPICS IN APPLIED EARTH OBSERVATIONS AND REMOTE SENSING
(2021)
Review
Construction & Building Technology
A. De Bock, B. Belmans, S. Vanlanduit, J. Blom, A. A. Alvarado-Alvarado, A. Audenaert
Summary: The leaf area index (LAI) is a crucial parameter in Vertical Greening Systems (VGS) that quantifies the total leaf area in the canopy and determines the co-benefits of VGS. However, there is limited understanding of the LAI parameter itself, its determination process, and the monitoring techniques for continuous LAI monitoring in VGS. This paper focuses on the LAI of VGS and its monitoring techniques, providing an overview of existing techniques and proposing guidelines for standardized LAI determination and reporting in VGS.
BUILDING AND ENVIRONMENT
(2023)
Article
Engineering, Electrical & Electronic
Bowen Song, Liangyun Liu, Jingjing Zhao, Xidong Chen, Helin Zhang, Yuan Gao, Xiao Zhang
Summary: The study assessed the accuracy of four global leaf area index (LAI) products over croplands in China, finding that GEOV2 had the highest accuracy compared to the other products. There were uncertainties in different regions, with the products showing overestimation or underestimation. The seasonal variation was minor, but scaling effects led to varying degrees of overestimation or underestimation in different areas.
IEEE JOURNAL OF SELECTED TOPICS IN APPLIED EARTH OBSERVATIONS AND REMOTE SENSING
(2021)
Article
Remote Sensing
Bashir Adamu, Kevin Tansey, Booker Ogutu
INTERNATIONAL JOURNAL OF REMOTE SENSING
(2018)
Article
Environmental Sciences
Yitong Zheng, Huazhong Ren, Jinxin Guo, Darren Ghent, Kevin Tansey, Xingbang Hu, Jing Nie, Shanshan Chen
Article
Computer Science, Information Systems
Yiyuan Sun, Qiang Wang, Kevin Tansey, Sana Ullah, Fan Liu, Haimeng Zhao, Lei Yan
ISPRS INTERNATIONAL JOURNAL OF GEO-INFORMATION
(2019)
Article
Environmental Sciences
Joao M. B. Carreiras, Shaun Quegan, Kevin Tansey, Susan Page
ENVIRONMENTAL RESEARCH LETTERS
(2020)
Article
Environmental Sciences
Mihai A. Tanase, Miguel A. Belenguer-Plomer, Ekhi Roteta, Aitor Bastarrika, James Wheeler, Angel Fernandez-Carrillo, Kevin Tansey, Werner Wiedemann, Peter Navratil, Sandra Lohberger, Florian Siegert, Emilio Chuvieco
Article
Environmental Sciences
Polyanna da Conceicao Bispo, Pedro Rodriguez-Veiga, Barbara Zimbres, Sabrina Couto de Miranda, Cassio Henrique Giusti Cezare, Sam Fleming, Francesca Baldacchino, Valentin Louis, Dominik Rains, Mariano Garcia, Fernando Del Bon Espirito-Santo, Iris Roitman, Ana Maria Pacheco-Pascagaza, Yaqing Gou, John Roberts, Kirsten Barrett, Laerte Guimaraes Ferreira, Julia Zanin Shimbo, Ane Alencar, Mercedes Bustamante, Iain Hector Woodhouse, Edson Eyji Sano, Jean Pierre Ometto, Kevin Tansey, Heiko Balzter
Article
Remote Sensing
Sa'Ad Ibrahim, Jorg Kaduk, Kevin Tansey, Heiko Balzter, Umar Mohammed Lawal
Summary: Phenology plays a key role in controlling vegetation feedbacks to the climate system, and detecting phenological variations in plant functional types (PFTs) is crucial for conservation planning. Using MODIS NDVI data, Savitzky-Golay filtering, and BFAST algorithms, this study reveals distinct phenological events and their impact on the growing season length (GSL) in a West African savannah landscape dominated by woody species. Woody species show early green-up dates and prolonged senescence, while the relationship between SOS or EOS and GSL varies among different PFTs. Vegetation changes estimated by BFAST differ by PFT, with grassland being more vulnerable to disturbances compared to woody species.
INTERNATIONAL JOURNAL OF REMOTE SENSING
(2021)
Article
Green & Sustainable Science & Technology
Francisco J. Areal, Wantao Yu, Kevin Tansey, Jiahuan Liu
Summary: Farm-level sustainable intensification metrics are essential for evaluating farm performance and supporting policy-making processes. This study demonstrates how satellite-based remote sensing information combined with farm efficiency analysis can be used to obtain a sustainable intensification indicator. The results show that satellite-based information can account for environmental impacts in agriculture production, and the derived environmental impact metrics can be used to measure farm-level sustainable intensification.
Article
Water Resources
Xin Pan, Suyi Liu, Kevin Tansey, Xingwang Fan, Zi Yang, Jie Yuan, Zhanchuan Wang, Yingbao Yang, Yuanbo Liu
Summary: This study estimated the evapotranspiration in the Poyang Lake wetland from 2008 to 2017 using remote sensing and a nonparametric approach. The results showed that downwelling shortwave radiation and water area were the main factors affecting evapotranspiration variability.
JOURNAL OF HYDROLOGY-REGIONAL STUDIES
(2023)
Article
Green & Sustainable Science & Technology
Heiko Balzter, Mateus Macul, Beth Delaney, Kevin Tansey, Fernando Espirito-Santo, Chidiebere Ofoegbu, Sergei Petrovskii, Bernhard Forchtner, Nicholes Nicholes, Emilio Payo, Pat Heslop-Harrison, Moya Burns, Laura Basell, Ella Egberts, Emma Stockley, Molly Desorgher, Caroline Upton, Mick Whelan, Ayse Yildiz
Summary: Loss and damage from climate change have become a prominent issue on the international agenda. COP27 in 2022 ratified a decision to establish a fund compensating low- and middle-income countries for the negative impacts of climate change, addressing the Global Adaptation Gap. This essay emphasizes the interdisciplinary perspective and research agenda on loss and damage from climate change, highlighting the need to understand the complex interactions between people, politics, nature, and climate.
Article
Engineering, Electrical & Electronic
Yue Zhang, Pengxin Wang, Kevin Tansey, Dong Han, Chi Chen, Junming Liu, Hongmei Li
Summary: An approach for estimating regional yields of winter wheat at different growth stages was developed by assimilating the CERES-Wheat model simulations and remotely sensed observations. Cross-wavelet transform analysis was used to determine the variation relationships between assimilated variables and yield at multiple time scales. Assimilated VTCI and LAI showed specific resonance periods with time series yields at each growth stage, and had higher weights at key growth stages, enhancing feature extraction and improving the accuracy of yield estimation.
IEEE JOURNAL OF SELECTED TOPICS IN APPLIED EARTH OBSERVATIONS AND REMOTE SENSING
(2023)
Article
Engineering, Electrical & Electronic
Dong Han, Pengxin Wang, Kevin Tansey, Junming Liu, Yue Zhang, Shuyu Zhang, Hongmei Li
Summary: This article proposes a new method for estimating the leaf area index and canopy chlorophyll content of winter wheat using a combined SAR and optical imagery approach. The results show that this method is more accurate than traditional machine learning models, particularly during the green-up stage of winter wheat.
IEEE JOURNAL OF SELECTED TOPICS IN APPLIED EARTH OBSERVATIONS AND REMOTE SENSING
(2022)
Article
Engineering, Electrical & Electronic
Dong Han, Pengxin Wang, Kevin Tansey, Shuyu Zhang, Huiren Tian, Yue Zhang, Hongmei Li
Summary: The proposed SAFY-V model, integrating VTCI, can better estimate winter wheat yields, especially in arid areas, thereby improving estimation accuracy.
IEEE JOURNAL OF SELECTED TOPICS IN APPLIED EARTH OBSERVATIONS AND REMOTE SENSING
(2021)
Article
Engineering, Electrical & Electronic
Xijia Zhou, Pengxin Wang, Kevin Tansey, Darren Ghent, Shuyu Zhang, Hongmei Li, Lei Wang
IEEE JOURNAL OF SELECTED TOPICS IN APPLIED EARTH OBSERVATIONS AND REMOTE SENSING
(2020)
Article
Agronomy
Wenyi Xu, Bo Elberling, Per Lennart Ambus
Summary: The frequency and extent of wildfires in the Arctic have been increasing due to climate change. In this study, researchers conducted experiments in West Greenland to investigate the long-term impacts of climate warming on post-fire carbon dioxide exchange in arctic tundra ecosystems. They found that fire increased soil organic phosphorus concentrations and burned areas remained a net CO2 source five years after the fire. However, with four to five years of summer warming, the burned areas turned into a net CO2 sink.
AGRICULTURAL AND FOREST METEOROLOGY
(2024)
Article
Agronomy
Yuanhang Yang, Jiabo Yin, Shengyu Kang, Louise J. Slater, Xihui Gu, Aliaksandr Volchak
Summary: This study investigates the impacts of water and heat stress on carbon uptake in China and explores the driving mechanisms of droughts using a machine learning model. The results show that droughts are mostly driven by atmospheric dryness, with precipitation, relative humidity, and temperature playing dominant roles. Water and heat stress have negative impacts on carbon assimilation, and drought occurrence is projected to increase significantly in the future. Improving ecosystem resilience to climate warming is crucial in mitigating the negative effects of droughts on carbon uptake.
AGRICULTURAL AND FOREST METEOROLOGY
(2024)
Article
Agronomy
Ningbo Cui, Shunsheng Zheng, Shouzheng Jiang, Mingjun Wang, Lu Zhao, Ziling He, Yu Feng, Yaosheng Wang, Daozhi Gong, Chunwei Liu, Rangjian Qiu
Summary: This study proposes a method to partition evapotranspiration (ET) into its components in agroforestry systems. The method is based on water-carbon coupling theory and flux conservation hypothesis. The results show that the partitioned components agree well with measurements from other sensors. The study also finds that atmospheric evaporation demand and vegetation factors greatly influence the components of ET, and increased tree leaf area limits understory grass transpiration.
AGRICULTURAL AND FOREST METEOROLOGY
(2024)
Article
Agronomy
Xinhao Li, Tianshan Zha, Andrew Black, Xin Jia, Rachhpal S. Jassal, Peng Liu, Yun Tian, Chuan Jin, Ruizhi Yang, Feng Zhang, Haiqun Yu, Jing Xie
Summary: With the rapid increase of urbanization, evapotranspiration (ET) in urban forests has become increasingly important in urban hydrology and climate. However, there is still a large uncertainty regarding the factors that regulate ET in urban areas. This study investigates the temporal variations of ET in an urban forest park in Beijing using the eddy-covariance technique. The results show that daily ET is close to zero during winter but reaches 3-6 mm day-1 in summer. Daily ET increases with vapor pressure deficit (VPD) and soil water content (SWC). Monthly ET increases linearly with normalized difference vegetation index and shows a strong correlation with surface conductance (gs), while exhibiting saturated responses to increasing monthly precipitation (PPT). Annual ET ranges from 326 to 566 mm, and soil water replenishment through PPT from the previous year is responsible for the generally higher monthly ET in spring relative to PPT. Biotic factors and PPT seasonality play essential roles in regulating ET at different scales.
AGRICULTURAL AND FOREST METEOROLOGY
(2024)
Article
Agronomy
Zhaogang Liu, Zhi Chen, Meng Yang, Tianxiang Hao, Guirui Yu, Xianjin Zhu, Weikang Zhang, Lexin Ma, Xiaojun Dou, Yong Lin, Wenxing Luo, Lang Han, Mingyu Sun, Shiping Chen, Gang Dong, Yanhong Gao, Yanbin Hao, Shicheng Jiang, Yingnian Li, Yuzhe Li, Shaomin Liu, Peili Shi, Junlei Tan, Yakun Tang, Xiaoping Xin, Fawei Zhang, Yangjian Zhang, Liang Zhao, Li Zhou, Zhilin Zhu
Summary: This study investigates the responses of temperate grassland (TG) and alpine grassland (AG) to climate change by studying carbon (C) fluxes across different regions in China. The results reveal that water factors consistently increase C fluxes, while temperature factors have opposite effects on TG and AG. The study enhances our understanding of C sinks and grassland sensitivity to climate change.
AGRICULTURAL AND FOREST METEOROLOGY
(2024)
Article
Agronomy
Peng Li, Huijie Li, Bingcheng Si, Tao Zhou, Chunhua Zhang, Min Li
Summary: This study mapped the distribution of forest age on the Chinese Loess Plateau using the LandTrendr algorithm. The results show that the LT algorithm is a convenient, efficient, and reliable method for identifying forest age. The findings have important implications for assessing and quantifying biomass and carbon sequestration in afforestation efforts on the Chinese Loess Plateau.
AGRICULTURAL AND FOREST METEOROLOGY
(2024)
Review
Agronomy
Yean-Uk Kim, Heidi Webber, Samuel G. K. Adiku, Rogerio de S. Noia Junior, Jean-Charles Deswarte, Senthold Asseng, Frank Ewert
Summary: As climate change is expected to increase the intensity and frequency of extreme weather events, it is crucial to assess their impact on cropping systems and explore adaptation options. Process-based crop models (PBCMs) have improved in simulating the impacts of major extreme weather events, but still struggle to reproduce low crop yields under wet conditions. This article provides an overview of the yield-loss mechanisms of excessive rainfall in cereals and the associated modelling approaches, aiming to guide improvements in PBCMs.
AGRICULTURAL AND FOREST METEOROLOGY
(2024)
Article
Agronomy
Xiaodong Liu, Yingjie Feng, Xinyu Zhao, Zijie Cui, Peiling Liu, Xiuzhi Chen, Qianmei Zhang, Juxiu Liu
Summary: Understanding the impact of climate on litterfall production is crucial for simulating nutrient cycling in forest ecosystems. This study analyzed a 14-year litterfall dataset from two subtropical forests in South China and found that litterfall was mainly influenced by wind speed during the wet season and by temperature during the dry season. These findings have potential significance in improving our understanding of carbon and nutrient cycling in subtropical forest ecosystems under climate change conditions.
AGRICULTURAL AND FOREST METEOROLOGY
(2024)
Article
Agronomy
Ruonan Chen, Liangyun Liu, Zhunqiao Liu, Xinjie Liu, Jongmin Kim, Hyun Seok Kim, Hojin Lee, Genghong Wu, Chenhui Guo, Lianhong Gu
Summary: Solar-induced chlorophyll fluorescence (SIF) has the potential to estimate gross primary production (GPP), but the quantitative relationship between them is not constant. In this study, a mechanistic model for SIF-based GPP estimation in evergreen needle forests (ENF) was developed, considering the seasonal variation in a key parameter of the model. The GPP estimates from this model were more accurate compared to other benchmark models, especially in extreme conditions.
AGRICULTURAL AND FOREST METEOROLOGY
(2024)
Article
Agronomy
Jingyi Zhu, Yanzheng Yang, Nan Meng, Ruonan Li, Jinfeng Ma, Hua Zheng
Summary: This study developed a random forest model using climate station and satellite data to generate high-precision precipitation datasets for the Qinghai-Tibet Plateau. By incorporating multisource satellite data, the model achieved a significant enhancement in precipitation accuracy and showed promising results in regions with limited meteorological stations and substantial spatial heterogeneity in precipitation patterns.
AGRICULTURAL AND FOREST METEOROLOGY
(2024)
Article
Agronomy
Yulin Yan, Youngryel Ryu, Bolun Li, Benjamin Dechant, Sheir Afgen Zaheer, Minseok Kang
Summary: Sustainable rice farming practices are urgently needed to meet increasing food demand, cope with water scarcity, and mitigate climate change. Traditional farming methods that prioritize a single objective have proven to be insufficient, while simultaneously optimizing multiple competing objectives remains less explored. This study optimized farm management to increase rice yield, reduce irrigation water consumption, and tackle the dilemma of reducing GHG emissions. The results suggest that the optimized management can maintain or even increase crop yield, while reducing water demand and GHG emissions by more than 50%.
AGRICULTURAL AND FOREST METEOROLOGY
(2024)
Article
Agronomy
Sasha D. Hafner, Jesper N. Kamp, Johanna Pedersen
Summary: This study compared micrometeorological and wind tunnel measurements using a semi-empirical model to understand wind tunnel measurement error. The results showed differences in emission estimates between the two methods, but the ALFAM2 model was able to reproduce emission dynamics for both methods when considering differences in mass transfer. The study provides a template for integrating and comparing measurements from different methods, suggesting the use of wind tunnel measurements for model evaluation and parameter estimation.
AGRICULTURAL AND FOREST METEOROLOGY
(2024)
Article
Agronomy
Wenfang Xu, Wenping Yuan, Donghai Wu, Yao Zhang, Ruoque Shen, Xiaosheng Xia, Philippe Ciais, Juxiu Liu
Summary: In the summer of 2022, China experienced record-breaking heatwaves and droughts, which had a significant impact on plant growth. The study also found that heatwaves were more critical than droughts in limiting vegetation growth.
AGRICULTURAL AND FOREST METEOROLOGY
(2024)
Article
Agronomy
Jiaqi Guo, Xiaohong Liu, Wensen Ge, Liangju Zhao, Wenjie Fan, Xinyu Zhang, Qiangqiang Lu, Xiaoyu Xing, Zihan Zhou
Summary: Vegetation photosynthetic phenology is an important indicator for understanding the impacts of climate change on terrestrial carbon cycle. This study evaluated and compared the abilities of different spectral indices to model photosynthetic phenology, and found that NIRv and PRI are effective proxies for monitoring photosynthetic phenology.
AGRICULTURAL AND FOREST METEOROLOGY
(2024)
Article
Agronomy
Arango Ruda Elizabeth, M. Altaf Arain
Summary: Temperate deciduous forests have significant impacts on regional and global water cycles. This study examined the effects of climate change and extreme weather events on the water use and evapotranspiration of a temperate deciduous forest in eastern North America. The results showed that photosynthetically active radiation and air temperature were the primary drivers of evapotranspiration, while vapor pressure deficit regulated water use efficiency. The study also found a changing trend in water use efficiency over the years, influenced by extreme weather conditions.
AGRICULTURAL AND FOREST METEOROLOGY
(2024)