A method for quality management of vegetation phenophases derived from satellite remote sensing data

Ruan, Yongjian; Zhang, Xinchang; Xin, Qinchuan; Sun, Ying; Ao, Zurui; Jiang, Xin

doi:10.1080/01431161.2021.1931534

Cited by 10 publications

(5 citation statements)

References 36 publications

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“…Thirdly, the GLSD dataset used in this study lacks an evaluation of ground-based phenological observations in temperate China. Future research should utilize various available data sources, such as phenology cameras (Richardson et al, 2009;Nasahara and Nagai, 2015), in-situ phenological observations (Nagai et al, 2014;Nagai et al, 2017;Vitasse et al, 2018;Xin et al, 2020;Ruan et al, 2021), and internet-published phenological information (Nagai et al, 2020;Tsutsumida et al, 2022), to effectively assess satellite-retrieved phenological metrics. In summary, there are significant variations in the autumn phenology across the different categories of vegetated regions.…”

Section: Discussionmentioning

confidence: 99%

Understanding spatio-temporal variation of autumn phenology in temperate China from 1982 to 2018

Wu,

2024

Front. Ecol. Evol.

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Land surface phenology plays a crucial role in accurately parameterizing interactions between land ecosystems and the atmosphere. Changes in autumn phenology directly impact the length of vegetation growing season and year-to-year changes in carbon uptake. Previous studies have often focused solely on the end of growing season when characterizing autumn phenology, neglecting the onset and duration of autumn. Here, we extracted the annual autumn phenological metrics, i.e., the start of brown-down phase (SOB), the end of brown-down phase (EOB), and the length of brown-down phase (LOB), for temperate China from 1982 to 2018 based on our self-developed global LSP dataset. We conducted a comparative analysis of SOB, EOB, and LOB in the field of their spatial distribution, temporal trends, and performance on various categories of vegetated regions (i.e., forests, grasslands, croplands, and vegetated areas in urban lands (V_Urban)). The results showed a significant negative correlation between the timing of autumn phenological metrics and latitude in temperate China. Between 1982 and 2018, there were significant positive increasing trends in EOB and LOB in V_Urban, as well as in LOB in forests in temperate China. However, the annual mean SOB, EOB, and LOB did not show significant trends across the entire study area. At the local pixel scale, SOB, EOB, and LOB exhibited a combination of advanced and delayed trends within various vegetated categories. The trends of the same phenological metric were not uniform across these diverse vegetated regions. For instance, the majority of pixels with significant trends in SOB exhibited delayed trends in forests and croplands, while displaying advanced trends in grasslands and V_Urban. In this study, employing three metrics (SOB, EOB, and LOB) to describe autumn phenology enhances our understanding of the impacts of global climate change on ecosystems, offering a more comprehensive and detailed perspective. In the future, satellite-based monitoring and phenological modeling should contemplate incorporating additional potential phenological metrics.

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Section: Discussionmentioning

confidence: 99%

Understanding spatio-temporal variation of autumn phenology in temperate China from 1982 to 2018

Wu,

2024

Front. Ecol. Evol.

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“…Data quality is another significant concern. Satellite-derived data, commonly used for calculating VIs, can be affected by cloud cover, sensor noise, or temporal-resolution limitations, leading to gaps or inaccuracies in the data [78]. These issues need to be carefully considered when using VIs for decision-making or scientific research.…”

Section: Data-quality Issuesmentioning

confidence: 99%

Beyond Vegetation: A Review Unveiling Additional Insights into Agriculture and Forestry through the Application of Vegetation Indices

Vélez

Martínez-Peña

Castrillo

2023

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Vegetation indices (VIs) have long been a crucial tool for monitoring plant growth and health, assessing the impact of environmental factors on vegetation, and supporting decision-making processes in agriculture and forestry. Traditionally, these mathematical formulations, leveraging the spectral response of plants to sunlight, have been instrumental in assessing vegetation health. However, emerging research suggests some unconventional applications that extend the scope of VIs. This article surveys the traditional and novel uses of VIs in various fields, including other applications such as climate change studies, disaster management, or microorganism assessment. It underscores the value of VIs, such as the normalized difference vegetation index (NDVI), or the enhanced vegetation index (EVI), in tracking climate-related shifts, mitigating the impact of disasters, assessing microbial terroir, organic production, and even aiding the wine industry. Nonetheless, despite their promise, the practical application of VIs also presents interpretational and technical challenges that need to be addressed. Hence, while the vast potential of VIs is underscored in this article, it also calls for continued technological advancements and research efforts to fully harness their benefits, ultimately aiding in the sustainable management of our environment. The objective of this review is not only to reflect on the current situation, but also to explore innovative and unconventional applications of vegetation indices. This involves anticipating the potential of this dynamic and rapidly advancing scientific tool, rather than solely following mainstream approaches.

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“…Many studies, both those regarded as 'classic' and cited repeatedly [234] and the contemporary ones [341], present the problem of the methodology of determination of season metrics based on remote sensing data. There are also attempts to improve the existing algorithm models [49]. An interesting example of combining various data sources and their mutual validation is the use of low-budget photo-traps, which facilitate local analysis of plant development phases [64] and comparison of the indicators obtained with satellite data.…”

Section: Season Metricsmentioning

confidence: 99%

“…Plant analyses are mainly focused on the chlorophyll content [25] or substances contained in plants [91] and, consequently, the condition of plants [24,27]. Research on the growth and development of vegetation can be carried out in both strictly controlled laboratory conditions [29,79,92,93] and using satellite techniques [42,44,46,[49][50][51]56,59,61,62,[64][65][66]68,71,[75][76][77][78]80,88, or other means of transporting remote sensing devices (UAV, airships, airplanes) [43,47,54,55,65,84,[108][109][110]118,[140][141][142][143][144][145][146][147][148]…”

Section: Introductionmentioning

confidence: 99%

“…For many years, measurements were based on traditional sources of information (i.e., direct phenological observations combined with meteorological data). The dynamic development of technology in recent decades has allowed for the use of remote sensing data, mainly properly processed satellite data with different temporal and spatial resolutions [10,14,23,26,28,[30][31][32][33][34][35][40][41][42][43][44][45][46][47][48][49][50][51][52][53][54][55][56][58][59][60][61][62][63][64][65][66][67][68][69][70][71][72]94,95,[134][135][136][137]…”

Section: Introductionmentioning

confidence: 99%

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Remote Sensing in Studies of the Growing Season: A Bibliometric Analysis

2022

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Analyses of climate change based on point observations indicate an extension of the plant growing season, which may have an impact on plant production and functioning of natural ecosystems. Analyses involving remote sensing methods, which have added more detail to results obtained in the traditional way, have been carried out only since the 1980s. The paper presents the results of a bibliometric analysis of papers related to the growing season published from 2000–2021 included in the Web of Science database. Through filtering, 285 publications were selected and subjected to statistical processing and analysis of their content. This resulted in the identification of author teams that mostly focused their research on vegetation growth and in the selection of the most common keywords describing the beginning, end, and duration of the growing season. It was found that most studies on the growing season were reported from Asia, Europe, and North America (i.e., 32%, 28%, and 28%, respectively). The analyzed articles show the advantage of satellite data over low-altitude and ground-based data in providing information on plant vegetation. Over three quarters of the analyzed publications focused on natural plant communities. In the case of crops, wheat and rice were the most frequently studied plants (i.e., they were analyzed in over 30% and over 20% of publications, respectively).

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A method for quality management of vegetation phenophases derived from satellite remote sensing data

Cited by 10 publications

References 36 publications

Understanding spatio-temporal variation of autumn phenology in temperate China from 1982 to 2018

Understanding spatio-temporal variation of autumn phenology in temperate China from 1982 to 2018

Beyond Vegetation: A Review Unveiling Additional Insights into Agriculture and Forestry through the Application of Vegetation Indices

Remote Sensing in Studies of the Growing Season: A Bibliometric Analysis

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