Comparing the quantum use efficiency of red and far-red sun-induced fluorescence at leaf and canopy under heat-drought stress DOI Creative Commons
Sebastian Wieneke, Javier Pacheco‐Labrador, Miguel D. Mahecha

и другие.

Remote Sensing of Environment, Год журнала: 2024, Номер 311, С. 114294 - 114294

Опубликована: Июнь 27, 2024

Sun-Induced chlorophyll Fluorescence (SIF) is the most promising remote sensing signal to monitor photosynthesis in space and time. However, under stress conditions its interpretation often complicated by factors such as light absorption plant morphological physiological adaptations. To ultimately derive quantum yield of fluorescence (ΦF) at photosystem from canopy measurements, so-called escape probability (fesc) needs be accounted for. In this study, we aim compare ΦF measured leaf- canopy-scale evaluate influence responses on two signals based a potato mesocosm heat-drought experiment. First, compared performance recently proposed reflectance-based approaches estimate leaf red fesc using data-supported simulations radiative transfer model SCOPE. While showed strong correlation (r2 ≥ 0.76), exhibited no relationship with SCOPE retrieved our We therefore propose modifications address limitation. then used modified models fesc, along an existing for far-red analyse dynamics increasing drought heat conditions. By incorporating obtained closer agreement between measurements. Specifically, r2 variables increased 0.3 0.50, 0.36 0.48. When comparing (ΦF,687 ΦF,760) stress, observed statistically significant decrease both ΦF,687 well ΦF,760, intensified. Canopy contrary, did not exhibit same trend, since measurements low wider spread lower median than high Finally, analysed sensitivity ΦF,760 changing solar incidence angle, variability without rotation. Our results suggest that variation strongly angle. These findings highlight need further research understand causes discrepancies scale ΦF,760. On underutilised understudied great potential assessing stress.

Язык: Английский

Satellite remote sensing of vegetation phenology: Progress, challenges, and opportunities DOI
Zheng Gong, Wenyan Ge, Jiaqi Guo

и другие.

ISPRS Journal of Photogrammetry and Remote Sensing, Год журнала: 2024, Номер 217, С. 149 - 164

Опубликована: Авг. 29, 2024

Язык: Английский

Процитировано

28
Global increase in the occurrence and impact of multiyear droughts DOI
Liangzhi Chen, Philipp Brun,

Pascal Buri

и другие.

Science, Год журнала: 2025, Номер 387(6731), С. 278 - 284

Опубликована: Янв. 16, 2025

Persistent multiyear drought (MYD) events pose a growing threat to nature and humans in changing climate. We identified inventoried global MYDs by detecting spatiotemporally contiguous climatic anomalies, showing that have become drier, hotter, led increasingly diminished vegetation greenness. The terrestrial land affected has increased at rate of 49,279 ± 14,771 square kilometers per year from 1980 2018. Temperate grasslands exhibited the greatest declines greenness during MYDs, whereas boreal tropical forests had comparably minor responses. With becoming more common, this quantitative inventory occurrence, severity, trend, impact provides an important benchmark for facilitating effective collaborative preparedness toward mitigation adaptation such extreme events.

Язык: Английский

Процитировано

6
Comparing the performance of vegetation indices for improving urban vegetation GPP estimation via eddy covariance flux data and Landsat 5/7 data DOI Creative Commons
Qi Zeng, Xuehe Lu, Sanmei Chen

и другие.

Ecological Informatics, Год журнала: 2025, Номер unknown, С. 103023 - 103023

Опубликована: Янв. 1, 2025

Язык: Английский

Процитировано

2
Globally quantitative analysis of the impact of atmosphere and spectral response function on 2-band enhanced vegetation index (EVI2) over Sentinel-2 and Landsat-8 DOI
Zhijun Zhen, Shengbo Chen, Tiangang Yin

и другие.

ISPRS Journal of Photogrammetry and Remote Sensing, Год журнала: 2023, Номер 205, С. 206 - 226

Опубликована: Окт. 14, 2023

Язык: Английский

Процитировано

40
Deep Learning for Multi-Source Data-Driven Crop Yield Prediction in Northeast China DOI Creative Commons
Jian Lü, Jian Li,

Hongkun Fu

и другие.

Agriculture, Год журнала: 2024, Номер 14(6), С. 794 - 794

Опубликована: Май 22, 2024

The accurate prediction of crop yields is crucial for enhancing agricultural efficiency and ensuring food security. This study assesses the performance CNN-LSTM-Attention model in predicting maize, rice, soybeans Northeast China compares its effectiveness with traditional models such as RF, XGBoost, CNN. Utilizing multi-source data from 2014 to 2020, which include vegetation indices, environmental variables, photosynthetically active parameters, our research examines model’s capacity capture essential spatial temporal variations. integrates Convolutional Neural Networks, Long Short-Term Memory, an attention mechanism effectively process complex datasets manage non-linear relationships within data. Notably, explores potential using kNDVI multiple crops, highlighting effectiveness. Our findings demonstrate that advanced deep-learning significantly enhance yield accuracy over methods. We advocate incorporation sophisticated technologies practices, can substantially improve production strategies.

Язык: Английский

Процитировано

14
Global patterns and drivers of post-fire vegetation productivity recovery DOI
Hongtao Xu, Hans W. Chen, Deliang Chen

и другие.

Nature Geoscience, Год журнала: 2024, Номер 17(9), С. 874 - 881

Опубликована: Авг. 23, 2024

Язык: Английский

Процитировано

13
Detection and Attribution of Vegetation Dynamics in the Yellow River Basin Based on Long-Term Kernel NDVI Data DOI Creative Commons
Haiying Yu,

Qianhua Yang,

Shouzheng Jiang

и другие.

Remote Sensing, Год журнала: 2024, Номер 16(7), С. 1280 - 1280

Опубликована: Апрель 5, 2024

Detecting and attributing vegetation variations in the Yellow River Basin (YRB) is vital for adjusting ecological restoration strategies to address possible threats posed by changing environments. On basis of kernel normalized difference index (kNDVI) key climate drivers (precipitation (PRE), temperature (TEM), solar radiation (SR), potential evapotranspiration (PET)) basin during period from 1982 2022, we utilized multivariate statistical approach analyze spatiotemporal patterns dynamics, identified variables, discerned respective impacts change (CC) human activities (HA) on these variations. Our analysis revealed a widespread greening trend across 93.1% YRB, with 83.2% exhibiting significant increases kNDVI (p < 0.05). Conversely, 6.9% vegetated areas displayed browning trend, particularly concentrated alpine urban areas. With Hurst exceeding 0.5 97.5% areas, YRB tends be extensively greened future. Climate variability emerges as pivotal determinant shaping diverse spatial temporal patterns, PRE exerting dominance 41.9% followed TEM (35.4%), SR (13%), PET (9.7%). Spatially, increased significantly enhanced growth arid zones, while controlled non-water-limited such irrigation zones. Vegetation dynamics were driven combination CC HA, relative contributions 55.8% 44.2%, respectively, suggesting that long-term dominant force. Specifically, contributed seen region southeastern part basin, human-induced factors benefited Loess Plateau (LP) inhibiting pastoral These findings provide critical insights inform formulation adaptation conservation thereby enhancing resilience environmental conditions.

Язык: Английский

Процитировано

11
Ecological assessment and driver analysis of high vegetation cover areas based on new remote sensing index DOI Creative Commons
Xiaoyong Zhang, Weiwei Jia,

Shixin Lu

и другие.

Ecological Informatics, Год журнала: 2024, Номер 82, С. 102786 - 102786

Опубликована: Авг. 23, 2024

Язык: Английский

Процитировано

11
Nonlinear effects of agricultural drought on vegetation productivity in the Yellow River Basin, China DOI
Yu‐Jie Ding, Lifeng Zhang, Yi He

и другие.

The Science of The Total Environment, Год журнала: 2024, Номер 948, С. 174903 - 174903

Опубликована: Июль 20, 2024

Язык: Английский

Процитировано

10
Identification of Ecological Sources Using Ecosystem Service Value and Vegetation Productivity Indicators: A Case Study of the Three-River Headwaters Region, Qinghai–Tibetan Plateau, China DOI Creative Commons

Xinyi Feng,

Huiping Huang,

Yingqi Wang

и другие.

Remote Sensing, Год журнала: 2024, Номер 16(7), С. 1258 - 1258

Опубликована: Апрель 2, 2024

As a crucial component of the ecological security pattern, source (ES) plays vital role in providing ecosystem service value (ESV) and conserving biodiversity. Previous studies have mostly considered ES only from either landscape change pattern or function perspectives, ignored their integration spatio-temporal evolutionary modeling. In this study, we proposed multi-perspective framework for characteristics by ESV incorporating aesthetics, carbon sink characteristics, quality, kernel NDVI (kNDVI). By integrating revised normalized difference vegetation index as foundation, employed spatial priority model to identify ES. This improvement aims yield more practical specific result. Applying Three-River Headwaters Region (TRHR), significant sources has been observed 2000 2020. performance provided reference conservation TRHR. The results indicate that identification reliable accuracy efficiency compared with existing NRs method could reveal precise distributions ES, enhancing integrity technical modeling support developing cross-scale planning management strategies nature reserve boundaries. our research serve building networks other ecologically fragile areas.

Язык: Английский

Процитировано

9