The relationship between threshold-based and inflexion-based approaches for extraction of land surface phenology

Shang, Rong; Liu, Ronggao; Xu, Mingzhu; Liu, Yang; Zuo, Lijun; Ge, Quansheng

doi:10.1016/j.rse.2017.07.020

Cited by 44 publications

(39 citation statements)

References 10 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The SOS was identified with the date when the reconstructed NDVI first reaches 9.18% of VGA. This threshold was used because it has been proven to be equivalent to the inflexion point (maximum rate of change in curvature) for extracting SOS at a global scale [34], which is commonly used for phenology retrieval [13][14][15].…”

Section: Methodsmentioning

confidence: 99%

“…The reference NDVI was calculated as the average NDVI on the same day-of-year (DOY) across the complete time series (2000-2016) for each pixel location [34] using MODIS Collection 6 MOD09A1 data. The gaps were created by excluding the NDVI values following the temporal distributions of real clouds in 2006.…”

Section: Comparison With Other Approaches For Modeling the Varied Vegmentioning

confidence: 99%

See 1 more Smart Citation

Determining the Start of the Growing Season from MODIS Data in the Indian Monsoon Region: Identifying Available Data in the Rainy Season and Modeling the Varied Vegetation Growth Trajectories

Shang

Liu

et al. 2018

Remote Sensing

Self Cite

View full text Add to dashboard Cite

Abstract:In the Indian monsoon region, frequent cloud cover in the rainy season results in less valid satellite observations during the vegetation growth period, making it difficult to extract land surface phenology (LSP). Even worse, many valid but humid observations were misidentified as clouds in the MODIS cloud mask, causing severe gaps in the LSP product. Using a refined cloud detection approach to separate clear-sky and cloudy observations, this study found that potentially valid observations during the vegetation growth period could be identified. Furthermore, the varied vegetation growth trajectories cannot be well-fitted by a global curve-fitting approach, but can be modelled by using the locally adjusted cubic-spline capping approach, which performed well for any seasonal patterns. Applying this approach, the start of growing season (SOS) was determined with 9.18% of vegetation growth amplitude between the maximum and minimum NDVI to generate the SOS product (2000)(2001)(2002)(2003)(2004)(2005)(2006)(2007)(2008)(2009)(2010)(2011)(2012)(2013)(2014)(2015)(2016). The valid percentage of this regional product largely increased from 29.30% to 69.76% compared with the MCD12Q2 product, and its reliability was approximate to that of deciduous broadleaf forest in North America and Europe. This product could serve as a basis for understanding the response of terrestrial ecosystems to the changing Indian monsoon.

show abstract

Section: Methodsmentioning

confidence: 99%

Section: Comparison With Other Approaches For Modeling the Varied Vegmentioning

confidence: 99%

Determining the Start of the Growing Season from MODIS Data in the Indian Monsoon Region: Identifying Available Data in the Rainy Season and Modeling the Varied Vegetation Growth Trajectories

Shang

Liu

et al. 2018

Remote Sensing

Self Cite

View full text Add to dashboard Cite

show abstract

“…The double logistic model (DL) approach was applied for the gap filling of NDVI time series. With reference to Shang et al [7], the threshold at the inflexion point is 9.18% of vegetation growth amplitude, and thus, a dynamic threshold of 9.18% was used to extract inflexion point of NDVI. Seasonal phenological metrics for start-of-season (SOS) and end-of-season (EOS) were extracted pixel-by-pixel for 2001 through 2017.…”

Section: Methodsmentioning

confidence: 99%

Phenophase shifts across elevations on major mountains in North China

Dai

Zhu

Wang

et al. 2018

Self Cite

View full text Add to dashboard Cite

Previous studies have reported plant phenological changes along horizontal belts in North China, however, little is known about elevation effects on mountain phenophases in China, such as how vegetation phenophases shift across elevation on mountains, and how they change under background of global change. In this context, by application of remote sensing data, Moderate Resolution Imaging Spectroradiometer (MODIS), changes of spring phenophases across elevation on 6 typical mountains in North China, namely Wuling, Xiaowutai, Guandi, Migang, Huashan and Taibai Moutians, and the effects of elevation on phenophases along altitudinal gradients, were studied in current work. Preliminary results showed that, similar to our fi ndings of phenological changes in plain area in North China, the onset of vegetation phenophases in spring advanced on these mountains, while the ending time for autumn phenophases delayed in the past two decades. Trends for advanced spring phenophase increased signifi cantly with altitude in some mountain regions, and spring phenophase sensitivities to altitude are stronger in lower latitude than in higher latitude regions. Similar to foreign studies, it is reported for the fi rst time that global warming has led to a more uniform spring phenology across elevation in North China mountains in recent years. Findings will not only benefi t policy making for the government in the fi eld of ecological constructions, but also will be helpful to evaluate future climate change on vegetation in such areas.

show abstract

“…The spatial distributions of spring phenology of all vegetation types (including deciduous trees and crops) extracted by seasonal amplitude method (threshold: 0.3) in TIMESAT software from images with different spatial resolutions are shown in Figure 7. Seasonal amplitude method (also known as relative threshold method) to extract vegetation phenology is widely used, and it has been validated by ground truth and another phenological extraction method (e.g., inflexion-based method) [59][60][61]. Moreover, the parameter (0.3) is an empirical threshold that is more suitable for phenology monitoring in the north of China according to the previous studies [23].…”

Section: Urbanization Effects On Vegetation Spring Phenology At Diffementioning

confidence: 99%

Coarse-Resolution Satellite Images Overestimate Urbanization Effects on Vegetation Spring Phenology

Tian

Zhu

et al. 2020

Remote Sensing

View full text Add to dashboard Cite

Numerous investigations of urbanization effects on vegetation spring phenology using satellite images have reached a consensus that vegetation spring phenology in urban areas occurs earlier than in surrounding rural areas. Nevertheless, the magnitude of this rural–urban difference is quite different among these studies, especially for studies over the same areas, which implies large uncertainties. One possible reason is that the satellite images used in these studies have different spatial resolutions from 30 m to 1 km. In this study, we investigated the impact of spatial resolution on the rural–urban difference of vegetation spring phenology using satellite images at different spatial resolutions. To be exact, we first generated a dense 10 m NDVI time series through harmonizing Sentinel-2 and Landsat-8 images by data fusion method, and then resampled the 10 m time series to coarser resolutions from 30 m to 8 km to simulate images at different resolutions. Afterwards, to quantify urbanization effects, vegetation spring phenology at each resolution was extracted by a widely used tool, TIMESAT. Last, we calculated the difference between rural and urban areas using an urban extent map derived from NPP VIIRS nighttime light data. Our results reveal: (1) vegetation spring phenology in urban areas happen earlier than rural areas no matter which spatial resolution from 10 m to 8 km is used, (2) the rural–urban difference in vegetation spring phenology is amplified with spatial resolution, i.e., coarse satellite images overestimate the urbanization effects on vegetation spring phenology, and (3) the underlying reason of this overestimation is that the majority of urban pixels in coarser images have higher diversity in terms of spring phenology dates, which leads to spring phenology detected from coarser NDVI time series earlier than the actual dates. This study indicates that spatial resolution is an important factor that affects the accuracy of the assessment of urbanization effects on vegetation spring phenology. For future studies, we suggest that satellite images with a fine spatial resolution are more appropriate to explore urbanization effects on vegetation spring phenology if vegetation species in urban areas is very diverse.

show abstract

The relationship between threshold-based and inflexion-based approaches for extraction of land surface phenology

Cited by 44 publications

References 10 publications

Determining the Start of the Growing Season from MODIS Data in the Indian Monsoon Region: Identifying Available Data in the Rainy Season and Modeling the Varied Vegetation Growth Trajectories

Determining the Start of the Growing Season from MODIS Data in the Indian Monsoon Region: Identifying Available Data in the Rainy Season and Modeling the Varied Vegetation Growth Trajectories

Phenophase shifts across elevations on major mountains in North China

Coarse-Resolution Satellite Images Overestimate Urbanization Effects on Vegetation Spring Phenology

Contact Info

Product

Resources

About