Comparative Quality and Trend of Remotely Sensed Phenology and Productivity Metrics across the Western United States

Berman, Ethan E.; Graves, Tabitha A.; Mikle, Nathaniel; Merkle, Jerod A.; Johnston, Aaron N.; Chong, Geneva W.

doi:10.3390/rs12162538

Cited by 14 publications

(13 citation statements)

References 83 publications

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“…As reported by the authors, the LAI (leaf area index) product is more suitable for analyses than the analogous phenological product in the area of evergreen forests and crop fields, whereas the phenological product provides better results than LAI in areas covered by low vegetation cover and meadows. Regional studies on plant phenology have also been conducted in East Africa [360], West Africa [58], and North and East Australia [42,327]. In Iran [354], researchers used data from the MODIS spectroradiometer to analyze the development phases in orchards surrounding Lake Urmia.…”

Section: Research Scalementioning

confidence: 99%

“…Processed MODIS and SPOT data were used to monitor the functioning of pastures located in northern China [219]. Shen et al [327] used LST and EVI data in combination with meteorological data to determine the relationship between crop phenology and climatic conditions. To determine the beginning and middle of the growing season, Younes et al [42] used a combination of Sentinel 2 and Landsat data.…”

Section: Source Datamentioning

confidence: 99%

“…Monitoring of crops for the determination of the seasonality of vegetation was mainly related to the major cereals [327,362]. The analyses of wheat and rice represented over 50% of all studies of arable crops [13,24,28,34,38,39,41].…”

Section: Vegetation Typesmentioning

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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Section: Research Scalementioning

confidence: 99%

Section: Source Datamentioning

confidence: 99%

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

2022

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“…4a and c). In addition, since in a few studies (Ren et al, 2017;Berman et al, 2020), end of leaf coloration was defined as the dates when NDVI drops by 60% or 90% of its annual amplitude, we also calculated the partial correlations between each of those dates and the temperature, respectively, and the correlations were similar to the that between DLCE and the pre-DLCE temperature (Fig. S5).…”

Section: Correlation Between Dlco and Temperaturementioning

confidence: 99%

Warming does not delay the start of autumnal leaf coloration but slows its progress rate

Jiang¹,

Shen²,

Ciais³

et al. 2022

Preprint

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Aim: Initiation of autumnal leaf senescence is critical for plant overwintering and ecosystem dynamics. Previous studies focused solely on the advanced stages of autumnal leaf senescence and claimed that climatic warming delays senescence, despite the fundamental differences among the stages of senescence. However, the timing of onset of leaf coloration (DLCO), the earliest visual sign of senescence, has been rarely studied. Our aim is to assess the response of DLCO to temperature across the Northern Hemisphere.Location: 30–75°N in the Northern Hemisphere.Time period: 2000–2018.Major taxa studied: Deciduous vegetation.Methods: We retrieved DLCO from high temporal-resolution satellite data, which was then validated by PhenoCam observations. We then investigated the temporal changes in DLCO and the relationship between DLCO and temperature by using DLCO from satellite data and ground phenological observations. We further estimated the timing of onset of autumnal decline in maximum canopy photosynthetic capacity, an indicator of leaf senescence earlier than DLCO, from FLUXNET CO2 fluxes data, and assessed its response to temperature.Results: DLCO was not significantly (P < 0.05) delayed between 2000 and 2018 in 94% of the area. DLCO was positively correlated with pre-DLCO mean daily minimum temperature (Tmin) in only 9% of the area, likely because of the overriding photoperiodic control, particularly for woody vegetation, whereas the end of leaf coloration (DLCE) was positively correlated with pre-DLCE mean Tmin over a larger area (34%).Main conclusions: The coloration progress rate is more sensitive than its start date to temperature, indicating an extension of the duration of leaf senescence under warming. This study indicates that the leaf coloration onset was not responsive to climate warming and provides observational evidence of photoperiod control of autumnal leaf senescence at biome and continental scales.

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“…Some researchers and scientists defined the first appearance of green leaves as the green up date [135,145,146], which resembles the start of the season in LSP. However, due to scale mismatch and data uncertainties, the phenological phases observed and recorded on the ground may sometimes be inconsistent with satellite-based LSP estimations [40].…”

Section: Lsp Metrics Validationmentioning

confidence: 99%

Estimating and Monitoring Land Surface Phenology in Rangelands: A Review of Progress and Challenges

et al. 2021

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Land surface phenology (LSP) has been extensively explored from global archives of satellite observations to track and monitor the seasonality of rangeland ecosystems in response to climate change. Long term monitoring of LSP provides large potential for the evaluation of interactions and feedbacks between climate and vegetation. With a special focus on the rangeland ecosystems, the paper reviews the progress, challenges and emerging opportunities in LSP while identifying possible gaps that could be explored in future. Specifically, the paper traces the evolution of satellite sensors and interrogates their properties as well as the associated indices and algorithms in estimating and monitoring LSP in productive rangelands. Findings from the literature revealed that the spectral characteristics of the early satellite sensors such as Landsat, AVHRR and MODIS played a critical role in the development of spectral vegetation indices that have been widely used in LSP applications. The normalized difference vegetation index (NDVI) pioneered LSP investigations, and most other spectral vegetation indices were primarily developed to address the weaknesses and shortcomings of the NDVI. New indices continue to be developed based on recent sensors such as Sentinel-2 that are characterized by unique spectral signatures and fine spatial resolutions, and their successful usage is catalyzed with the development of cutting-edge algorithms for modeling the LSP profiles. In this regard, the paper has documented several LSP algorithms that are designed to provide data smoothing, gap filling and LSP metrics retrieval methods in a single environment. In the future, the development of machine learning algorithms that can effectively model and characterize the phenological cycles of vegetation would help to unlock the value of LSP information in the rangeland monitoring and management process. Precisely, deep learning presents an opportunity to further develop robust software packages such as the decomposition and analysis of time series (DATimeS) with the abundance of data processing tools and techniques that can be used to better characterize the phenological cycles of vegetation in rangeland ecosystems.

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Comparative Quality and Trend of Remotely Sensed Phenology and Productivity Metrics across the Western United States

Cited by 14 publications

References 83 publications

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

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

Warming does not delay the start of autumnal leaf coloration but slows its progress rate

Estimating and Monitoring Land Surface Phenology in Rangelands: A Review of Progress and Challenges

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