Continental-scale land surface phenology from harmonized Landsat 8 and Sentinel-2 imagery

Accurate and timely land surface phenology (LSP) provides essential information for investigating the responses of terrestrial ecosystems to climate changes and quantifying carbon and surface energy cycles on the Earth. LSP has been widely investigated using daily Visible Infrared Imaging Radiometer Suite (VIIRS) or Moderate Resolution Imaging Spectroradiometer (MODIS) observations, but the resultant phenometrics are frequently influenced by surface heterogeneity and persistent cloud contamination in the time series observations. Recently, LSP has been derived from Landsat-8 and Sentinel-2 time series providing detailed spatial pattern, but the results are of high uncertainties because of poor temporal resolution. With the availability of data from Advanced Baseline Imager (ABI) onboard a new generation of geostationary satellites that observe the earth every 10–15 min, daily cloud-free time series could be obtained with high opportunities. Therefore, this study investigates the generation of synthetic high spatiotemporal resolution time series by fusing the harmonized Landsat-8 and Sentinel-2 (HLS) time series with the temporal shape of ABI data for monitoring field-scale (30 m) LSP. The algorithm is verified by detecting the timings of greenup and senescence onsets around north Wisconsin/Michigan states, United States, where cloud cover is frequent during spring rainy season. The LSP detections from HLS-ABI are compared with those from HLS or ABI alone and are further evaluated using PhenoCam observations. The result indicates that (1) ABI could provide ~3 times more high-quality observations than HLS around spring greenup onset; (2) the greenup and senescence onsets derived from ABI and HLS-ABI are spatially consistent and statistically comparable with a median difference less than 1 and 10-days, respectively; (3) greenup and senescence onsets derived from HLS data show sharp boundaries around the orbit-overlapped areas and shifts of ~13 days delay and ~15 days ahead, respectively, relative to HLS-ABI detections; and (4) HLS-ABI greenup and senescence onsets align closely to PhenoCam observations with an absolute average difference of less than 2 days and 5 days, respectively, which are much better than phenology detections from ABI or HLS alone. The result suggests that the proposed approach could be implemented the monitor of 30 m LSP over regions with persistent cloud cover.

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Multiscale assessment of land surface phenology from harmonized Landsat 8 and Sentinel-2, PlanetScope, and PhenoCam imagery

Remote Sensing of Environment ◽

10.1016/j.rse.2021.112716 ◽

2021 ◽

Vol 266 ◽

pp. 112716

Author(s):

Minkyu Moon ◽

Andrew D. Richardson ◽

Mark A. Friedl

Keyword(s):

Land Surface ◽

Landsat 8 ◽

Land Surface Phenology ◽

Sentinel 2

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Evaluation of TsHARP Utility for Thermal Sharpening of Sentinel-3 Satellite Images Using Sentinel-2 Visual Imagery

Remote Sensing ◽

10.3390/rs11192304 ◽

2019 ◽

Vol 11 (19) ◽

pp. 2304 ◽

Cited By ~ 3

Author(s):

Hanna Huryna ◽

Yafit Cohen ◽

Arnon Karnieli ◽

Natalya Panov ◽

William P. Kustas ◽

...

Keyword(s):

Spatial Resolution ◽

Land Surface ◽

Near Infrared ◽

Landsat 8 ◽

Large Area ◽

Primary Methods ◽

Thermal Images ◽

Spatially Distributed ◽

The Difference ◽

Sentinel 2

A spatially distributed land surface temperature is important for many studies. The recent launch of the Sentinel satellite programs paves the way for an abundance of opportunities for both large area and long-term investigations. However, the spatial resolution of Sentinel-3 thermal images is not suitable for monitoring small fragmented fields. Thermal sharpening is one of the primary methods used to obtain thermal images at finer spatial resolution at a daily revisit time. In the current study, the utility of the TsHARP method to sharpen the low resolution of Sentinel-3 thermal data was examined using Sentinel-2 visible-near infrared imagery. Compared to Landsat 8 fine thermal images, the sharpening resulted in mean absolute errors of ~1 °C, with errors increasing as the difference between the native and the target resolutions increases. Part of the error is attributed to the discrepancy between the thermal images acquired by the two platforms. Further research is due to test additional sites and conditions, and potentially additional sharpening methods, applied to the Sentinel platforms.

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Exploring the potential of land surface phenology and seasonal cloud free composites of one year of Sentinel-2 imagery for tree species mapping in a mountainous region

International Journal of Applied Earth Observation and Geoinformation ◽

10.1016/j.jag.2020.102208 ◽

2021 ◽

Vol 94 ◽

pp. 102208 ◽

Cited By ~ 1

Author(s):

Andreas Kollert ◽

Magnus Bremer ◽

Markus Löw ◽

Martin Rutzinger

Keyword(s):

Tree Species ◽

Land Surface ◽

Mountainous Region ◽

Land Surface Phenology ◽

One Year ◽

Species Mapping ◽

Sentinel 2

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Quantifying latitudinal variation in land surface phenology of Spartina alterniflora saltmarshes across coastal wetlands in China by Landsat 7/8 and Sentinel-2 images

Remote Sensing of Environment ◽

10.1016/j.rse.2021.112810 ◽

2022 ◽

Vol 269 ◽

pp. 112810

Author(s):

Xi Zhang ◽

Xiangming Xiao ◽

Shiyun Qiu ◽

Xiao Xu ◽

Xinxin Wang ◽

...

Keyword(s):

Spartina Alterniflora ◽

Land Surface ◽

Coastal Wetlands ◽

Latitudinal Variation ◽

Land Surface Phenology ◽

Landsat 7 ◽

Sentinel 2

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A RADIOMETRIC NORMALIZATION METHOD OF CONTROLLING NO-CHANGED SET (CNCS) FOR DIVERSE LANDCOVER USING MULTI-SENSOR DATA

ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences ◽

10.5194/isprs-archives-xlii-3-w10-863-2020 ◽

2020 ◽

Vol XLII-3/W10 ◽

pp. 863-870

Author(s):

L. T. Huang ◽

W. L. Jiao ◽

T. F. Long ◽

C. L. Kang

Keyword(s):

Remote Sensing ◽

Linear Relationship ◽

Land Surface ◽

Remote Sensing Data ◽

Normalization Method ◽

Sensor Data ◽

Landsat 8 ◽

Landsat 8 Oli ◽

Sensing Data ◽

Sentinel 2

Abstract. The accurate acquisition of land surface reflectance (SR) data determines the accuracy of ground objects recognition, classification and land surface parameter inversion using remote sensing data, which is the basis of remote sensing data application. In this study, a Control No-Changed Set (CNCS) radiometric normalization method is proposed to realize spectral information transformation of multi-sensor data, which is based on the Iteratively Reweighted Multivariate Alteration Detection (IR-MAD), and includes automatic selection and step-by-step optimization of no-change pixels. The No-Changed set (NC) is obtained by selecting the original no-change pixels between the target image and the reference image according to the linear relationship. In the obtained original no-change regions, IR-MAD rules with iterative control are used to fix the final no-change pixels, after regression modeling and calculation, the normalized images are obtained. The method is tested on multi-images from multi-sensors in three groups of experiments (GF-1 WFV and Landsat-8 OLI, GF-1 PMS and Sentinel-2 MSI, and Landsat-8 OLI and Sentinel-2 MSI) with different landcover areas. The results of radiometric normalization are evaluated qualitatively and quantitatively. The data of the three groups of experiments have a high correlation (correlation coefficient r values > 0.85), indicating that they can be used together as complementary data. The Root Mean Squared Error (RMSE) values calculate from the NC between the reference and normalized target images are much smaller than those between the reference and original target images. The radiometric colour composition effects, and the typical ground objects spectral reflective curves of the reference and normalized target images are very similar after radiometric normalization. These results indicate that the CNCS method considers the linear relationship of the no-change pixels and is effective, stable, and can be used to improve the consistency of SR of multi-images from multi-sensors.

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Automated mapping of supraglacial hydrology using Machine Learning

10.5194/egusphere-egu21-7866 ◽

2021 ◽

Author(s):

Diarmuid Corr ◽

Amber Leeson ◽

Malcolm McMillan ◽

Ce Zhang

Keyword(s):

Machine Learning ◽

Sea Level Rise ◽

Sea Level ◽

False Negative ◽

Ice Sheets ◽

Landsat 8 ◽

Continental Scale ◽

Global Sea Level ◽

Sentinel 2 ◽

Antarctic Ice Sheets

Mass loss from Greenlandic and Antarctic ice sheets are predicted to be the dominant contribution to global sea level rise in coming years. Supraglacial lakes and channels are thought to play a significant role in ice sheet mass balance by causing the speed-up of grounded ice and weakening, floating ice shelves to the point of collapse. Identifying the location, distribution and life cycle of these hydrological features on both the Greenland and Antarctic ice sheets is therefore important in understanding their present and future contribution to global sea level rise. Supraglacial hydrological features can be easily identified by eye in optical satellite imagery. However, given that there are many thousands of these features, and they appear in many hundreds of satellite images, automated approaches to mapping these features in such images are urgently needed.&#160;Current automated approaches in mapping supraglacial hydrology tend to have high false positive and false negative rates, which are often followed by manual corrections and quality control processes. Given the scale of the data however, methods such as those that require manual post-processing are not feasible for repeat monitoring of surface hydrology at continental scale. Here, we present initial results from our work conducted as part of the 4D Greenland and 4D Antarctica projects, which increases the accuracy of supraglacial lake and channel delineation using Sentinel-2 and Landsat-7/8 imagery, while reducing the need for manual intervention. We use Machine Learning approaches including a Random Forest algorithm trained to recognise water, ice, cloud, rock, shadow, blue-ice and crevassed regions. Both labelled optical imagery and auxiliary data (e.g. digital elevation models) are used in our approach. Our methods are trained and validated using data covering a range of glaciological and climatological conditions, including images of both ice sheets and those acquired at different points during the melt-season. The workflow, developed under Google Cloud Platform, which hosts the entire archive of Sentinel-2 and Landsat-8 data, allows for large-scale application over Greenlandic and Antarctic ice sheets, and is intended for repeated use throughout future melt-seasons.

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