A novel algorithm for the generation of gap-free time series by fusing harmonized Landsat 8 and Sentinel-2 observations with PhenoCam time series for detecting land surface phenology

Tran, Khuong H.; Zhang, Xiaoyang; Ketchpaw, Alexander R.; Wang, Jianmin; Ye, Yongchang; Shen, Yu

doi:10.1016/j.rse.2022.113275

Cited by 24 publications

(8 citation statements)

References 109 publications

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“…Further, although the PhenoCam site is distributed locally, its imagery could capture diverse phenological events within a small area because the phenological variations exist in either heterogeneous or homogeneous vegetation types 50 , 51 . For instance, the phenological timing within one PhenoCam site could differ by approximately two weeks for greenup onset and almost a month for senescence onset 52 . As a result, the PhenoCam imagery in a single site is generally composed of many time series with diverse phenology developments.…”

Section: Background and Summarymentioning

confidence: 99%

“…Alternatively, the finer spatial resolution LSP, such as detections from Landsat or HLS data, has been regularly used to validate coarser spatial resolution LSP (e.g., 500 m MODIS/VIIRS) to reduce the spatial mismatch. Still, the gaps in finer spatial resolution satellite time series are generally larger than those in the coarser satellite observations, which induce much larger uncertainties in LSP detections 10 , 52 . Thus, the comparison with finer resolution LSP (e.g., Landsat or HLS alone) could not be able to validate the coarse resolution LSP.…”

Section: Background and Summarymentioning

confidence: 99%

“…Thus, the comparison with finer resolution LSP (e.g., Landsat or HLS alone) could not be able to validate the coarse resolution LSP. Although the multi-source LSP (MSLSP) at 30 m derived from the HLS observations is able to provide phenological timing for North America, the noise and gaps in the HLS time series continue to be substantial challenges that negatively influence the realism of LSP detections and cause large uncertainties 10 , 52 .…”

Section: Background and Summarymentioning

confidence: 99%

“…Therefore, building on the proof-of-concept provided by Tran et al . 52 , we here presented a reference dataset of vegetation phenology development at 30 m pixels that was produced by bridging the temporal HLS observations with near-surface PhenoCam time series, which is HLS-PhenoCam LSP dataset (hereafter called HP-LSP). The HP-LSP dataset consists of 78 regions (each region covers 10 × 10 km 2 with at least one PhenoCam site) across various plant functional types and climates in North America during 2019 and 2020, which leads to approximately 17 million samples with a spatial resolution of 30 m. The dataset includes two parts: (1) the 3-day synthetic gap-free HLS-PhenoCam EVI2 time series, and (2) spatially continuous and scalable phenometrics (greenup, maturity, senescence, and dormancy onsets) with up to three vegetation growing cycles.…”

Section: Background and Summarymentioning

confidence: 99%

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HP-LSP: A reference of land surface phenology from fused Harmonized Landsat and Sentinel-2 with PhenoCam data

Tran,

Zhang,

et al. 2023

Sci Data

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Land surface phenology (LSP) products are currently of large uncertainties due to cloud contaminations and other impacts in temporal satellite observations and they have been poorly validated because of the lack of spatially comparable ground measurements. This study provided a reference dataset of gap-free time series and phenological dates by fusing the Harmonized Landsat 8 and Sentinel-2 (HLS) observations with near-surface PhenoCam time series for 78 regions of 10 × 10 km2 across ecosystems in North America during 2019 and 2020. The HLS-PhenoCam LSP (HP-LSP) reference dataset at 30 m pixels is composed of: (1) 3-day synthetic gap-free EVI2 (two-band Enhanced Vegetation Index) time series that are physically meaningful to monitor the vegetation development across heterogeneous levels, train models (e.g., machine learning) for land surface mapping, and extract phenometrics from various methods; and (2) four key phenological dates (accuracy ≤5 days) that are spatially continuous and scalable, which are applicable to validate various satellite-based phenology products (e.g., global MODIS/VIIRS LSP), develop phenological models, and analyze climate impacts on terrestrial ecosystems.

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Section: Background and Summarymentioning

confidence: 99%

Section: Background and Summarymentioning

confidence: 99%

Section: Background and Summarymentioning

confidence: 99%

Section: Background and Summarymentioning

confidence: 99%

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HP-LSP: A reference of land surface phenology from fused Harmonized Landsat and Sentinel-2 with PhenoCam data

Tran,

Zhang,

et al. 2023

Sci Data

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“…2022, 14, 5721 3 of 30 Imaging Spectroradiometer (MODIS) data [44,45] or integration of Landsat-7, Landsat-8, and Sentinel-2 data [26]. This is mainly because of the high probability of cloud cover and rainy season in tropical areas with monsoon climates [46]. Specifically, 85% to 95% of the VMD is covered by a persistent cloud during the wet season, making it infeasible or low accuracy for various applications, such as land use land cover (LULC) classification and waterbodies extraction [47,48].…”

Section: Introductionmentioning

confidence: 99%

Surface Water Mapping and Flood Monitoring in the Mekong Delta Using Sentinel-1 SAR Time Series and Otsu Threshold

Tran

Menenti

2022

Remote Sensing

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The annual flood and the alteration in hydrological regimes are the most vital concerns in the Vietnamese Mekong Delta (VMD). Although synthetic aperture radar (SAR) Sentinel-1 imagery is widely used for water management, only a few studies have used Sentinel-1 data for mapping surface water and monitoring flood events in the VMD. This study developed an algorithm to implement (i) automatic Otsu threshold on a series of Sentinel-1 images to extract surface water and (ii) time series analyses on the derived surface water maps to detect flood water extent in near-real-time (NRT). Specifically, only cross-polarized VH was selected after an assessment of different Sentinel-1 polarizations. The dynamic Otsu thresholding algorithm was applied to identify an optimal threshold for each pre-processed Sentinel-1 VH image to separate water from non-water pixels for producing a time series of surface water maps. The derived Sentinel-1 surface water maps were visually compared with the Sentinel-2 Full Resolution Browse (FRB) and statistically examined with the Sentinel-2 Multispectral Instrument (MSI) surface water maps, which were generated by applying the Otsu threshold on the normalized difference water index (NDWI) and modified normalized difference water index (MNDWI) images. The visual comparison showed a strong correspondence between the Sentinel-1 surface water maps and Sentinel-2 FRB images in three periods, including rice’s sowing season, flood period, and rice’s maturation stage. A good statistical agreement suggested that the performance of the dynamic Otsu thresholding algorithm on Sentinel-1 image time series to map surface water is effective in river areas (R2 = 0.97 and RMSE = 1.18%), while it is somewhat lower in paddy field areas (R2 = 0.88 and RMSE = 3.88%). Afterward, a flood mapping algorithm in NRT was developed by applying the change-detection-based time series analyses on the derived Sentinel-1 surface water maps. Every single pixel at the time is respectively referred to its state in the water/non-water and flooded/non-flooded maps at the previous time to be classified into a flooded or non-flooded pixel. The flood mapping algorithm enables updates at each time step to generate temporal flood maps in NRT for monitoring flood water extent in large-scale areas. This study provides a tool to rapidly generate surface water and flood maps to support water management and risk reduction in the VMD. The future improvement of the current algorithm is discussed.

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Cropland expansion delays vegetation spring phenology according to satellite and in-situ observations

Zhao

Dong

Yang

et al. 2023

Agriculture, Ecosystems & Environment

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A novel algorithm for the generation of gap-free time series by fusing harmonized Landsat 8 and Sentinel-2 observations with PhenoCam time series for detecting land surface phenology

Cited by 24 publications

References 109 publications

HP-LSP: A reference of land surface phenology from fused Harmonized Landsat and Sentinel-2 with PhenoCam data

HP-LSP: A reference of land surface phenology from fused Harmonized Landsat and Sentinel-2 with PhenoCam data

Surface Water Mapping and Flood Monitoring in the Mekong Delta Using Sentinel-1 SAR Time Series and Otsu Threshold

Cropland expansion delays vegetation spring phenology according to satellite and in-situ observations

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