Estimation of vegetation traits with kernel NDVI

ISPRS Journal of Photogrammetry and Remote Sensing, Journal Year: 2022, Volume and Issue: 195, P. 408 - 417

Published: Dec. 27, 2022

Vegetation indices computed from spectral signatures are vastly used for monitoring the terrestrial biosphere. Indices convenient proxies canopy structure, and leaf pigment content, consequently to estimate photosynthetic activity of vegetation. Owing its simplicity, celebrated Normalized Difference Index (NDVI) has been as a proxy greenness structure. Unfortunately, NDVI can only capture linear relationships near infrared (NIR) - red difference with parameter interest. To account higher-order relations between channels, kernel (kNDVI) was proposed in (Camps-Valls et al., 2021). In this work, we give useful prescriptions proper use show good performance wider set applications. We discuss characteristics index like boundedness, low error propagation. Furthermore, empirical evidence estimating in-situ vegetation parameters (leaf area (LAI), gross primary productivity (GPP), leaf, chlorophyll green total LAI fraction absorbed photosynthetically active radiation (fAPAR)) well estimation latent heat at flux tower level. confirm generally (correlation coefficient kNDVI content is 0.919 0.933 maize over two sites, correlation carotenoid, 0.816, 0.520 0.579 three forest sites) highlight convenience ecosystems. foster adoption new family index, provide source code 6 programming languages efficient implementations Google Earth Engine (GEE) platform https://github.com/IPL-UV/kNDVI.

Language: Английский

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Exceptional heat and atmospheric dryness amplified losses of primary production during the 2020 U.S. Southwest hot drought

Global Change Biology, Journal Year: 2022, Volume and Issue: 28(16), P. 4794 - 4806

Published: April 22, 2022

Earth's ecosystems are increasingly threatened by "hot drought," which occurs when hot air temperatures coincide with precipitation deficits, intensifying the hydrological, physiological, and ecological effects of drought enhancing evaporative losses soil moisture (SM) increasing plant stress due to higher vapor pressure deficit (VPD). Drought-induced reductions in gross primary production (GPP) exert a major influence on terrestrial carbon sink, but extent hotter atmospherically drier conditions will amplify deficits cycle remains largely unknown. During summer autumn 2020, U.S. Southwest experienced one most intense droughts record, record-low record-high temperature VPD across region. Here, we use this natural experiment evaluate GPP further decompose those negative anomalies into their constituent meteorological hydrological drivers. We found 122 Tg C (>25%) reduction below 2015-2019 mean, far lowest regional over Soil Moisture Active Passive satellite record. Roughly half estimated loss was attributable low SM (likely combination warming-enhanced depletion), record-breaking amplified GPP, contributing roughly 40% anomaly. Both very likely continue next century, leading more frequent substantially drought-induced reductions.

Language: Английский

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From remotely‐sensed solar‐induced chlorophyll fluorescence to ecosystem structure, function, and service: Part II—Harnessing data

Global Change Biology, Journal Year: 2023, Volume and Issue: 29(11), P. 2893 - 2925

Published: Feb. 18, 2023

Abstract Although our observing capabilities of solar‐induced chlorophyll fluorescence (SIF) have been growing rapidly, the quality and consistency SIF datasets are still in an active stage research development. As a result, there considerable inconsistencies among diverse at all scales widespread applications them led to contradictory findings. The present review is second two companion reviews, data oriented. It aims (1) synthesize variety, scale, uncertainty existing datasets, (2) sector ecology, agriculture, hydrology, climate, socioeconomics, (3) clarify how such inconsistency superimposed with theoretical complexities laid out (Sun et al., 2023) may impact process interpretation various contribute inconsistent We emphasize that accurate functional relationships between other ecological indicators contingent upon complete understanding uncertainty. Biases uncertainties observations can significantly confound their respond environmental variations. Built syntheses, we summarize gaps current observations. Further, offer perspectives on innovations needed help improve informing ecosystem structure, function, service under climate change, including enhancing in‐situ capability especially “data desert” regions, improving cross‐instrument standardization network coordination, advancing by fully harnessing theory data.

Language: Английский

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Satellite remote sensing of vegetation phenology: Progress, challenges, and opportunities

ISPRS Journal of Photogrammetry and Remote Sensing, Journal Year: 2024, Volume and Issue: 217, P. 149 - 164

Published: Aug. 29, 2024

Language: Английский

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Proximal remote sensing: an essential tool for bridging the gap between high‐resolution ecosystem monitoring and global ecology

New Phytologist, Journal Year: 2025, Volume and Issue: unknown

Published: Jan. 23, 2025

Summary A new proliferation of optical instruments that can be attached to towers over or within ecosystems, ‘proximal’ remote sensing, enables a comprehensive characterization terrestrial ecosystem structure, function, and fluxes energy, water, carbon. Proximal sensing bridge the gap between individual plants, site‐level eddy‐covariance fluxes, airborne spaceborne by providing continuous data at high‐spatiotemporal resolution. Here, we review recent advances in proximal for improving our mechanistic understanding plant processes, model development, validation current upcoming satellite missions. We provide best practices availability metadata sensing: spectral reflectance, solar‐induced fluorescence, thermal infrared radiation, microwave backscatter, LiDAR. Our paper outlines steps necessary making these streams more widespread, accessible, interoperable, information‐rich, enabling us address key ecological questions unanswerable from space‐based observations alone and, ultimately, demonstrate feasibility technologies critical local global ecology.

Language: Английский

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Indicators of water use efficiency across diverse agroecosystems and spatiotemporal scales

The Science of The Total Environment, Journal Year: 2022, Volume and Issue: 864, P. 160992 - 160992

Published: Dec. 17, 2022

Understanding the relationship between water and production within across agroecosystems is essential for addressing several agricultural challenges of 21st century: providing food, fuel, fiber to a growing human population, reducing environmental impacts production, adapting food systems climate change. Of all activities, agriculture has highest demand globally. Therefore, increasing use efficiency (WUE), or producing 'more crop per drop', been long-term goal management, engineering, breeding. WUE widely used term applied diverse array spatial scales, spanning from leaf globe, over temporal scales ranging seconds months years. The measurement, interpretation, complexity varies enormously these challenging comparisons agroecosystems. goals this review are evaluate common indicators in assess tradeoffs when applying amidst changing climate. We examine three questions: (1) what uses limitations indicators, (2) how can be agroecosystems, (3) help adapt change? Addressing will require land managers, producers, policy makers, researchers, consumers costs benefits practices innovations production. Clearly defining interpreting most scale-appropriate way crucial advancing agroecosystem sustainability.

Language: Английский

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Spatio-Temporal Variation and Climatic Driving Factors of Vegetation Coverage in the Yellow River Basin from 2001 to 2020 Based on kNDVI

Forests, Journal Year: 2023, Volume and Issue: 14(3), P. 620 - 620

Published: March 20, 2023

The Yellow River Basin (YRB) is a fundamental ecological barrier in China and one of the regions where environment relatively fragile. Studying spatio-temporal variations vegetation coverage YRB their driving factors through long-time-series dataset great significance to eco-environmental construction sustainable development YRB. In this study, we sought characterize variation its climatic from 2001 2020 by constructing new kernel normalized difference index (kNDVI) based on MOD13 A1 V6 data Google Earth Engine (GEE) platform. Using Theil–Sen median trend analysis, Mann–Kendall test, Hurst exponent, investigated characteristics future trends coverage. were obtained via partial correlation analysis complex associations between kNDVI both temperature precipitation. results reveal following: spatial distribution pattern showed that was high southeast low northwest. Vegetation fluctuated 2020, with main significant increasing growth at rate 0.0995/5a. response strong YRB, stronger precipitation than temperature. Additionally, found be non-climatic factors, which mainly distributed Henan, southern Shaanxi, Shanxi, western Inner Mongolia, Ningxia, eastern Gansu. areas driven northern Shandong, Qinghai, Gansu, northeastern Sichuan. Our findings have implications for ecosystem restoration

Language: Английский

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Deep Learning for Multi-Source Data-Driven Crop Yield Prediction in Northeast China

Agriculture, Journal Year: 2024, Volume and Issue: 14(6), P. 794 - 794

Published: May 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.

Language: Английский

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Response of Vegetation Productivity to Greening and Drought in the Loess Plateau Based on VIs and SIF

Forests, Journal Year: 2024, Volume and Issue: 15(2), P. 339 - 339

Published: Feb. 9, 2024

In the context of global warming, frequent occurrence drought has become one main reasons affecting loss gross primary productivity (GPP) terrestrial ecosystems. Under influence human activities, vegetation greening trend Loess Plateau increased significantly. Therefore, it is great significance to study response GPP in under trend. Here, we comprehensively assessed ability indices (VIs) and solar-induced chlorophyll fluorescence (SIF) capture changes at different seasonal scales during drought. Specifically, utilized three indices: normalized difference index (NDVI), near-infrared reflectance (NIRV), kernel NDVI (kNDVI), determined period 2001 based on standardized precipitation evapotranspiration (SPEI) soil moisture (SSMI). Moreover, anomalies VIs SIF relationship with were compared. The results showed that both able as well normal years. Overall, captured better due water heat stress compared VIs. Across time scales, strongest (meanR2 = 0.85), followed by NIRV 0.84), 0.76), kNDVI 0.74), suggesting more sensitive physiological vegetation. Notably, performed best sparse 0.85). drought, less productive land classes; superior use class increased. addition, correlated 0.50) than other anomalies. future, efforts integrate respective strengths SIF, NIRV, will improve our understanding changes.

Language: Английский

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Dryland evapotranspiration from remote sensing solar-induced chlorophyll fluorescence: Constraining an optimal stomatal model within a two-source energy balance model

Remote Sensing of Environment, Journal Year: 2024, Volume and Issue: 303, P. 113999 - 113999

Published: Jan. 26, 2024

Language: Английский

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