Monitoring and Prediction of Land Surface Phenology Using Satellite Earth Observations—A Brief Review DOI Creative Commons
Mateo Gašparović, Ivan Pilaš, Dorijan Radočaj

et al.

Applied Sciences, Journal Year: 2024, Volume and Issue: 14(24), P. 12020 - 12020

Published: Dec. 22, 2024

Monitoring and predicting land surface phenology (LSP) are essential for understanding ecosystem dynamics, climate change impacts, forest agricultural productivity. Satellite Earth observation (EO) missions have played a crucial role in the advancement of LSP research, enabling global continuous monitoring vegetation cycles. This review provides brief overview key EO satellite missions, including advanced very-high resolution radiometer (AVHRR), moderate imaging spectroradiometer (MODIS), Landsat program, which an important capturing dynamics at various spatial temporal scales. Recent advancements machine learning techniques further enhanced prediction capabilities, offering promising approaches short-term cropland suitability assessment. Data cubes, organize multidimensional data, provide innovative framework enhancing analyses by integrating diverse data sources simplifying access processing. highlights potential satellite-based monitoring, models, cube infrastructure advancing research insights into current trends, challenges, future directions.

Language: Английский

Four ways to define the growing season DOI Creative Commons
Christian Körner, Patrick Möhl, Erika Hiltbrunner

et al.

Ecology Letters, Journal Year: 2023, Volume and Issue: 26(8), P. 1277 - 1292

Published: June 14, 2023

What is addressed as growing season in terrestrial ecosystems one of the main determinants annual plant biomass production globally. However, there no well-defined concept behind. Here, we show different facets what might be termed season, each with a distinct meaning: (1) time period during which or part it actually grows and produces new tissue, irrespective net carbon gain (growing sensu stricto). (2) The defined by developmental, that is, phenological markers (phenological season). (3) vegetation whole achieves its primary (NPP) ecosystem (NEP), expressed (productive season) (4) plants could potentially grow based on meteorological criteria (meteorological We hypothesize duration such 'window opportunity' strong predictor for NPP at global scale, especially forests. These definitions have implications understanding modelling growth production. common view variation phenology proxy productivity misleading, often resulting unfounded statements potential consequences climatic warming sequestration.

Language: Английский

Citations

74
Satellite remote sensing of vegetation phenology: Progress, challenges, and opportunities DOI
Zheng Gong, Wenyan Ge, Jiaqi Guo

et al.

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

Published: Aug. 29, 2024

Language: Английский

Citations

28
Monitoring nature's calendar from space: Emerging topics in land surface phenology and associated opportunities for science applications DOI Creative Commons
Xuanlong Ma, Xiaolin Zhu, Qiaoyun Xie

et al.

Global Change Biology, Journal Year: 2022, Volume and Issue: 28(24), P. 7186 - 7204

Published: Sept. 17, 2022

Abstract Vegetation phenology has been viewed as the nature's calendar and an integrative indicator of plant‐climate interactions. The correct representation vegetation is important for models to accurately simulate exchange carbon, water, energy between vegetated land surface atmosphere. Remote sensing advanced monitoring by providing spatially temporally continuous data that together with conventional ground observations offers a unique contribution our knowledge about environmental impact on ecosystems well ecological adaptations feedback global climate change. Land (LSP) defined use satellites monitor seasonal dynamics in surfaces estimate phenological transition dates. LSP, interdisciplinary subject among remote sensing, ecology, biometeorology, undergone rapid development over past few decades. Recent advances sensor technologies, fusion techniques, have enabled novel retrieval algorithms refine details at even higher spatiotemporal resolutions, new insights into ecosystem dynamics. As such, here we summarize recent LSP associated opportunities science applications. We focus remaining challenges, promising emerging topics believe will truly form very frontier research field.

Language: Английский

Citations

41
A deep learning approach for deriving winter wheat phenology from optical and SAR time series at field level DOI Creative Commons
Felix Lobert,

Johannes Löw,

Marcel Schwieder

et al.

Remote Sensing of Environment, Journal Year: 2023, Volume and Issue: 298, P. 113800 - 113800

Published: Sept. 21, 2023

Information on crop phenology is essential when aiming to better understand the impacts of climate and change, management practices, environmental conditions agricultural production. Today's novel optical radar satellite data with increasing spatial temporal resolution provide great opportunities derive such information. However, so far, we largely lack methods that leverage this detailed information at field level. We here propose a method based dense time series from Sentinel-1, Sentinel-2, Landsat 8 detect start seven phenological stages winter wheat seeding harvest. built different feature sets these input compared their performance for training one-dimensional U-Net. The model was evaluated using comprehensive reference set national network covering 16,000 observations 2017 2020 in Germany against baseline by Random Forest model. Our results show are differently well suited detection due unique characteristics signal processing. combination both types showed best 50.1% 65.5% being predicted an absolute error less than six days. Especially late can be with, e.g., coefficient determination (R2) between 0.51 0.62 harvest, while earlier like stem elongation remain challenge (R2 0.06 0.28). Moreover, our indicate meteorological have comparatively low explanatory potential fine-scale developments wheat. Overall, demonstrate image Sentinel sensor constellations versatility deep learning models determining timing.

Language: Английский

Citations

27
From Roots to Leaves: Tree Growth Phenology in Forest Ecosystems DOI
Roberto Silvestro, Annie Deslauriers, Peter Prislan

et al.

Current Forestry Reports, Journal Year: 2025, Volume and Issue: 11(1)

Published: Jan. 29, 2025

Language: Английский

Citations

1
Land surface phenology indicators retrieved across diverse ecosystems using a modified threshold algorithm DOI Creative Commons
Qiaoyun Xie, Caitlin E. Moore, James Cleverly

et al.

Ecological Indicators, Journal Year: 2023, Volume and Issue: 147, P. 110000 - 110000

Published: Feb. 13, 2023

Land surface phenology (LSP), the study of seasonal vegetation dynamics from remote sensing imagery, provides crucial information for plant monitoring and reflects responses ecosystems to climate change. The Moderate Resolution Imaging Spectroradiometer (MODIS) product (MCD12Q2) global LSP information, but it has large spatial gaps in many regions, especially where rainfall influences more than temperature. This aimed improve coverage retrieval these ecosystems. To do so, we used a regionally modified threshold algorithm retrievals, which were tested over continental Australia as includes diverse landscapes arid, mesic, forest environments. We generated metrics annually 2003 2018 using satellite Enhanced Vegetation Index (EVI) time series at 500 m resolution, including start, peak, end, length growing seasons, minimum EVI value prior after peak date, maximum value, integral during season (an approximation productivity), amplitude (maximum minus EVI). Our optimised improved only 26 % continent 70 averaged across 16 years. results showed that was low (EVI < 0.1) arid/semi-arid shrublands savannas, tropical subtropical temperate evergreen forests, whose captured by our regional not product. Some ecosystems, such irregular with dynamics, seasons could skip year or occur once depending on conditions. sensitive amplitudes. found detectability increases increases, regardless cover. Evaluation eddy covariance flux tower measurements gross primary productivity (GPP) demonstrated reliability accuracy algorithm. These retrievals provide greater understanding savanna, shrubland, cover 30 land globally. essential ecological agricultural studies quantifying bushfire fuel accumulation carbon cycling, whilst enhancing capacity

Language: Английский

Citations

20
Assessing the quality ecology of endemic tree species in China based on machine learning models and UPLC methods: The example of Eucommia ulmoides Oliv. DOI
Huihui Zhang, Xinke Zhang, Guoshuai Zhang

et al.

Journal of Cleaner Production, Journal Year: 2024, Volume and Issue: 452, P. 142021 - 142021

Published: April 2, 2024

Language: Английский

Citations

8
Slower changes in vegetation phenology than precipitation seasonality in the dry tropics DOI Creative Commons
Jiaqi Tian, Xiangzhong Luo, Hao Xu

et al.

Global Change Biology, Journal Year: 2024, Volume and Issue: 30(1)

Published: Jan. 1, 2024

Abstract The dry tropics occupy ~40% of the tropical land surface and play a dominant role in trend interannual variability global carbon cycle. Previous studies have reported considerable changes precipitation seasonality due to climate change, however, accompanied length vegetation growing season (LGS)—the key period sequestration—have not been examined. Here, we used long‐term satellite observations along with in‐situ flux measurements investigate phenological over past 40 years. We found that only ~18% show significant ( p ≤ .1) increasing LGS, while ~13% decreasing trend. direction LGS change depended on but also water use strategy (i.e. isohydricity) as an adaptation average whether most is wet or season). Meanwhile, rate was ~23% slower than seasonality, caused by buffering effect from soil moisture. This study uncovers potential mechanisms driving tropics, offering guidance for regional cycle studies.

Language: Английский

Citations

7
Floating in the air: forecasting allergenic pollen concentration for managing urban public health DOI Creative Commons
Xiaoyu Zhu, Xuanlong Ma, Zhengyang Zhang

et al.

International Journal of Digital Earth, Journal Year: 2024, Volume and Issue: 17(1)

Published: Jan. 23, 2024

The presence of airborne allergenic pollen causes a variety immune reactions and respiratory diseases, threatening human life in severe cases. Climate change is exacerbating the pollen-induced health risks adding significant economic burden to societies. Despite pressing threats, vital health-related information not available public date, reshaping future geographic patterns remains unknown. To help establish critical forecasting capacity, systematic review was conducted three promising directions were identified: (1) resolving heterogeneous urban plant species distribution phenology using fine-resolution satellite constellations; (2) acquiring ancillary about patient symptoms from emerging geospatial big data, such as social media; (3) deciphering coupled effect climate urbanization on species. On this basis, we recommend an optimized workflow that combines real-time monitoring networks with high-resolution vegetation weather forecast systems, comprehensively considering production diffusion process advanced prediction models. By focusing knowledge gaps, provides much needed insight propel research eventually benefit management health.

Language: Английский

Citations

6
Conflicting Changes of Vegetation Greenness Interannual Variability on Half of the Global Vegetated Surface DOI Creative Commons
Jiaqi Tian, Xiangzhong Luo

Earth s Future, Journal Year: 2024, Volume and Issue: 12(5)

Published: April 26, 2024

Abstract Changes in the interannual variability (IAV) of vegetation greenness and carbon sequestration are key indicators stability climate sensitivities terrestrial ecosystems. Recent studies have examined changes IAV using atmospheric CO 2 observations dynamic global models (DGVMs), however, reported different even contradictory trends. Here, we investigate greenness, quantified as coefficient (CV), over past few decades based on multiple satellite remote sensing products DGVMs. Our results suggested that, half vegetated surface (mostly tropics), CV trends detected by conflicting. We found that 22.20% 28.20% non‐tropical land surface) show significant positive negative ( p ≤ 0.1), respectively. Regions with higher air temperature greater aridity tend to increasing trends, whereas greening trend nitrogen deposition lead smaller DGVMs generally cannot capture obtained from products, while inconsistency among is likely caused their process algorithms rather than sensors utilized. study closely examines highlights substantial uncertainty when response ecosystems change.

Language: Английский

Citations

6