Regional evaluation of satellite‐based methods for identifying end of vegetation growing season

Shen, Ruoque; Lu, Haibo; Yuan, Wenping; Chen, Xiuzhi; He, Bin

doi:10.1002/rse2.223

Cited by 7 publications

(3 citation statements)

References 40 publications

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“…Such patterns have also been documented in Tibetan alpine grassland, where MODIS Normalized Difference Vegetation Index (NDVI) data captured the advancement of SOS throughout 2000-2014, but a delaying trend of the GIMMS NDVI estimated SOS was observed [39]. Discrepancies between phenology trends based on different denoising methods or extraction methods varied in research areas, research periods, and vegetation types [40][41][42][43]. For example, Zhu et al [8] applied several commonly utilized vegetation phenology extraction methods on MOD09A1 (8-day) and MOD13A2 (16-day) datasets and found no significant differences between SOS or EOS trends derived from asymmetric Gaussian function, double logistic function, and the piecewise logistic function method.…”

Section: Introductionmentioning

confidence: 77%

Assessing the Effects of Time Interpolation of NDVI Composites on Phenology Trend Estimation

Zhu

Xie

et al. 2021

Remote Sensing

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The accurate evaluation of shifts in vegetation phenology is essential for understanding of vegetation responses to climate change. Remote-sensing vegetation index (VI) products with multi-day scales have been widely used for phenology trend estimation. VI composites should be interpolated into a daily scale for extracting phenological metrics, which may not fully capture daily vegetation growth, and how this process affects phenology trend estimation remains unclear. In this study, we chose 120 sites over four vegetation types in the mid-high latitudes of the northern hemisphere, and then a Moderate Resolution Imaging Spectroradiometer (MODIS) MCD43A4 daily surface reflectance data was used to generate a daily normalized difference vegetation index (NDVI) dataset in addition to an 8-day and a 16-day NDVI composite datasets from 2001 to 2019. Five different time interpolation methods (piecewise logistic function, asymmetric Gaussian function, polynomial curve function, linear interpolation, and spline interpolation) and three phenology extraction methods were applied to extract data from the start of the growing season and the end of the growing season. We compared the trends estimated from daily NDVI data with those from NDVI composites among (1) different interpolation methods; (2) different vegetation types; and (3) different combinations of time interpolation methods and phenology extraction methods. We also analyzed the differences between the trends estimated from the 8-day and 16-day composite datasets. Our results indicated that none of the interpolation methods had significant effects on trend estimation over all sites, but the discrepancies caused by time interpolation could not be ignored. Among vegetation types with apparent seasonal changes such as deciduous broadleaf forest, time interpolation had significant effects on phenology trend estimation but almost had no significant effects among vegetation types with weak seasonal changes such as evergreen needleleaf forests. In addition, trends that were estimated based on the same interpolation method but different extraction methods were not consistent in showing significant (insignificant) differences, implying that the selection of extraction methods also affected trend estimation. Compared with other vegetation types, there were generally fewer discrepancies between trends estimated from the 8-day and 16-day dataset in evergreen needleleaf forest and open shrubland, which indicated that the dataset with a lower temporal resolution (16-day) can be applied. These findings could be conducive for analyzing the uncertainties of monitoring vegetation phenology changes.

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Section: Introductionmentioning

confidence: 77%

Assessing the Effects of Time Interpolation of NDVI Composites on Phenology Trend Estimation

Zhu

Xie

et al. 2021

Remote Sensing

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“…Consequently, observations in these datasets are often missing or contaminated [7]. In addition, these datasets are acquired relatively infrequently compared to low spatial resolution datasets, generally limiting their ability to capture key vegetation phenological phases [8]. Numerous studies have been attempting to develop methods for reproducing NDVI time-series images with both high spatial and temporal resolutions, which can be mainly divided into the temporal interpolation methods and the spatiotemporal fusion methods [9], [10].…”

Section: Introductionmentioning

confidence: 99%

Incorporating Environmental Variables Into Spatiotemporal Fusion Model to Reconstruct High-Quality Vegetation Index Data

Li,

Peng,

Zheng

et al. 2024

IEEE Trans. Geosci. Remote Sensing

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Restricted by the design of satellite sensors, the existing satellite-based Normalized Difference Vegetation Index (NDVI) cannot simultaneously have a high temporal resolution and spatial resolution, which substantially limits its applications. In recent years, several spatiotemporal fusion models have been developed to produce vegetation index datasets with both high spatial and temporal resolutions, but large uncertainties remain. This study proposes a spatiotemporal fusion model (i.e., Integrating ENvironmental VarIable spatiotemporal fusion model, InENVI) based on a machine-learning method by incorporating environmental variables to reconstruct NDVI data. Over 14 study areas covering various vegetation types globally, the InENVI method was validated for reproducing spatiotemporal variations in NDVI. On average, the determining coefficients (R 2 ) of the reconstructed NDVI compared with satellite-based NDVI observations were above 0.90, reflecting the spatiotemporal variations over all study sites. In addition, we compared the performance of the InENVI model with seven other fusion models over two cropland areas with high vegetation heterogeneity. The results showed the newly developed InENVI method had the best performance, and the reconstruction error of the InENVI method decreased about 23.68-59.63% on average over two study areas compared to the other seven methods. Our analyses also highlighted that the integration of environmental variables into spatiotemporal fusion is necessary to improve reconstruction accuracy. The InENVI model provides an alternative approach forThe research is funded by the China National Funds for Distinguished Young Scientists (41925001). Xiangqian Li and Qiongyan Peng contribute equally.

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“…Most existing studies used remote‐sensing data to study the beginning and termination of the growing season. Remote sensing evidently provides important and practical tools in broad‐scale vegetation studies (Babst et al., 2019; Shen et al., 2021). However, the major drawback of using remote‐sensing data is the so‐called ‘scale issue’ (Wu & Li, 2009).…”

Section: Introductionmentioning

confidence: 99%

INTRAGRO: A machine learning approach to predict future growth of trees under climate change

Aryal,

Grießinger,

Dyola

et al. 2023

Ecology and Evolution

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The escalating impact of climate change on global terrestrial ecosystems demands a robust prediction of the trees' growth patterns and physiological adaptation for sustainable forestry and successful conservation efforts. Understanding these dynamics at an intra‐annual resolution can offer deeper insights into tree responses under various future climate scenarios. However, the existing approaches to infer cambial or leaf phenological change are mainly focused on certain climatic zones (such as higher latitudes) or species with foliage discolouration during the fall season. In this study, we demonstrated a novel approach (INTRAGRO) to combine intra‐annual circumference records generated by dendrometers coupled to the output of climate models to predict future tree growth at intra‐annual resolution using a series of supervised and unsupervised machine learning algorithms. INTRAGRO performed well using our dataset, that is dendrometer data of P. roxburghii Sarg. from the subtropical mid‐elevation belt of Nepal, with robust test statistics. Our growth prediction shows enhanced tree growth at our study site for the middle and end of the 21st century. This result is remarkable since the predicted growing season by INTRAGRO is expected to shorten due to changes in seasonal precipitation. INTRAGRO's key advantage is the opportunity to analyse changes in trees' intra‐annual growth dynamics on a global scale, regardless of the investigated tree species, regional climate and geographical conditions. Such information is important to assess tree species' growth performance and physiological adaptation to growing season change under different climate scenarios.

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Regional evaluation of satellite‐based methods for identifying end of vegetation growing season

Cited by 7 publications

References 40 publications

Assessing the Effects of Time Interpolation of NDVI Composites on Phenology Trend Estimation

Assessing the Effects of Time Interpolation of NDVI Composites on Phenology Trend Estimation

Incorporating Environmental Variables Into Spatiotemporal Fusion Model to Reconstruct High-Quality Vegetation Index Data

INTRAGRO: A machine learning approach to predict future growth of trees under climate change

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