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

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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Forest Stand Species Mapping Using the Sentinel-2 Time Series

Remote Sensing ◽

10.3390/rs11101197 ◽

2019 ◽

Vol 11 (10) ◽

pp. 1197 ◽

Cited By ~ 32

Author(s):

Ewa Grabska ◽

Patrick Hostert ◽

Dirk Pflugmacher ◽

Katarzyna Ostapowicz

Keyword(s):

Time Series ◽

Tree Species ◽

Forest Tree ◽

Species Discrimination ◽

Accurate Information ◽

Silver Fir ◽

Forest Tree Species ◽

Wide Range ◽

Species Mapping ◽

Sentinel 2

Accurate information regarding forest tree species composition is useful for a wide range of applications, both for forest management and scientific research. Remote sensing is an efficient tool for collecting spatially explicit information on forest attributes. With the launch of the Sentinel-2 mission, new opportunities have arisen for mapping tree species owing to its spatial, spectral, and temporal resolution. The short revisit cycle (five days) is crucial in vegetation mapping because of the reflectance changes caused by phenological phases. In our study, we evaluated the utility of the Sentinel-2 time series for mapping tree species in the complex, mixed forests of the Polish Carpathian Mountains. We mapped the following nine tree species: common beech, silver birch, common hornbeam, silver fir, sycamore maple, European larch, grey alder, Scots pine, and Norway spruce. We used the Sentinel-2 time series from 2018, with 18 images included in the study. Different combinations of Sentinel-2 imagery were selected based on mean decrease accuracy (MDA) and mean decrease Gini (MDG) measures, in addition to temporal phonological pattern analysis. Tree species discrimination was performed using the Random Forest classification algorithm. Our results showed that the use of the Sentinel-2 time series instead of single date imagery significantly improved forest tree species mapping, by approximately 5–10% of overall accuracy. In particular, combining images from spring and autumn resulted in better species discrimination.

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

Remote Sensing of Environment ◽

10.1016/j.rse.2020.111685 ◽

2020 ◽

Vol 240 ◽

pp. 111685 ◽

Cited By ~ 28

Author(s):

Douglas K. Bolton ◽

Josh M. Gray ◽

Eli K. Melaas ◽

Minkyu Moon ◽

Lars Eklundh ◽

...

Keyword(s):

Land Surface ◽

Landsat 8 ◽

Land Surface Phenology ◽

Continental Scale ◽

Sentinel 2

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Characterising the spring and autumn land surface phenology of Macaronesian species using Sentinel-2 data: the case of Canary Island

10.1117/12.2600125 ◽

2021 ◽

Author(s):

Lorenzo C. Quesada-Ruiz ◽

Jose A. Caparros-Santiago ◽

Miguel A. Garcia-Perez ◽

Victor Rodriguez-Galiano

Keyword(s):

Land Surface ◽

Canary Island ◽

Land Surface Phenology ◽

Sentinel 2

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OPTIMAL DATES FOR DECIDUOUS TREE SPECIES MAPPING USING FULL YEARS SENTINEL-2 TIME SERIES IN SOUTH WEST FRANCE

ISPRS Annals of Photogrammetry Remote Sensing and Spatial Information Sciences ◽

10.5194/isprs-annals-v-3-2020-469-2020 ◽

2020 ◽

Vol V-3-2020 ◽

pp. 469-476

Author(s):

N. Karasiak ◽

M. Fauvel ◽

J.-F. Dejoux ◽

C. Monteil ◽

D. Sheeren

Keyword(s):

Time Series ◽

Species Distribution ◽

Tree Species ◽

Satellite Image ◽

Deciduous Tree ◽

Support Vector ◽

Red Oak ◽

Spectral Bands ◽

One Year ◽

Sentinel 2

Abstract. The free to use Sentinel-2 (S2) sensors with 5-day revisit time at high spatial resolution in 10 spectral bands is a revolution in the remote sensing domain. Including 6 spectral bands in the near infrared, with 3 dedicated for the red-edge (where the vegetation significatively increases), these european satellites are very promising for mapping tree species distribution at a national scale. Here, we study the contribution of three one-year S2 Satellite Image Time Series (SITS) for mapping deciduous species distribution in the southwest of France. The annual cycle of vegetation (called phenology) can contribute to the identification of tree species. For some specific dates, species can have different phenological behaviours (senesence, flowering…). To train and validate the maps, we used the Support Vector Machine algorithm with a spatial cross-validation method. To train the algorithm with the same number of samples per species, we decided to undersample each class to the smallest class using a K-means clustering method. Moreover, a Sequential Feature Selection (SFS) has been implemented to detect the optimal dates per species. Our results are promising with high accuracy for Red oak andWillow (average score of the three one-year respectively F1 = 0.99, F1 = 0.94) based on the optimal dates. However, it appears that the performances when using the each full SITS are far below the optimal dates models (average ΔF1 = 0.32). We did not find, except for Willow and Red oak, that the optimal dates were the same for each year. Perspectives is to find an algorithm robust to temporal or spectral noise and to smooth the time series.

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FORCE—Landsat + Sentinel-2 Analysis Ready Data and Beyond

Remote Sensing ◽

10.3390/rs11091124 ◽

2019 ◽

Vol 11 (9) ◽

pp. 1124 ◽

Cited By ~ 33

Author(s):

David Frantz

Keyword(s):

Land Surface ◽

Data Availability ◽

Large Area ◽

Scientific Software ◽

Radiometric Correction ◽

Land Surface Phenology ◽

Aggregation Techniques ◽

Wide Range ◽

Level 2 ◽

Sentinel 2

Ever increasing data volumes of satellite constellations call for multi-sensor analysis ready data (ARD) that relieve users from the burden of all costly preprocessing steps. This paper describes the scientific software FORCE (Framework for Operational Radiometric Correction for Environmental monitoring), an ‘all-in-one’ solution for the mass-processing and analysis of Landsat and Sentinel-2 image archives. FORCE is increasingly used to support a wide range of scientific to operational applications that are in need of both large area, as well as deep and dense temporal information. FORCE is capable of generating Level 2 ARD, and higher-level products. Level 2 processing is comprised of state-of-the-art cloud masking and radiometric correction (including corrections that go beyond ARD specification, e.g., topographic or bidirectional reflectance distribution function correction). It further includes data cubing, i.e., spatial reorganization of the data into a non-overlapping grid system for enhanced efficiency and simplicity of ARD usage. However, the usage barrier of Level 2 ARD is still high due to the considerable data volume and spatial incompleteness of valid observations (e.g., clouds). Thus, the higher-level modules temporally condense multi-temporal ARD into manageable amounts of spatially seamless data. For data mining purposes, per-pixel statistics of clear sky data availability can be generated. FORCE provides functionality for compiling best-available-pixel composites and spectral temporal metrics, which both utilize all available observations within a defined temporal window using selection and statistical aggregation techniques, respectively. These products are immediately fit for common Earth observation analysis workflows, such as machine learning-based image classification, and are thus referred to as highly analysis ready data (hARD). FORCE provides data fusion functionality to improve the spatial resolution of (i) coarse continuous fields like land surface phenology and (ii) Landsat ARD using Sentinel-2 ARD as prediction targets. Quality controlled time series preparation and analysis functionality with a number of aggregation and interpolation techniques, land surface phenology retrieval, and change and trend analyses are provided. Outputs of this module can be directly ingested into a geographic information system (GIS) to fuel research questions without any further processing, i.e., hARD+. FORCE is open source software under the terms of the GNU General Public License v. >= 3, and can be downloaded from http://force.feut.de.

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Improved Estimates of Arctic Land Surface Phenology Using Sentinel-2 Time Series

Remote Sensing ◽

10.3390/rs12223738 ◽

2020 ◽

Vol 12 (22) ◽

pp. 3738

Author(s):

Adrià Descals ◽

Aleixandre Verger ◽

Gaofei Yin ◽

Josep Peñuelas

Keyword(s):

Time Series ◽

High Resolution ◽

Land Surface ◽

Vegetation Index ◽

Mean Squared Error ◽

Google Earth ◽

The Arctic ◽

Threshold Method ◽

Land Surface Phenology ◽

Sentinel 2

The high spatial resolution and revisit time of Sentinel-2A/B tandem satellites allow a potentially improved retrieval of land surface phenology (LSP). The biome and regional characteristics, however, greatly constrain the design of the LSP algorithms. In the Arctic, such biome-specific characteristics include prolonged periods of snow cover, persistent cloud cover, and shortness of the growing season. Here, we evaluate the feasibility of Sentinel-2 for deriving high-resolution LSP maps of the Arctic. We extracted the timing of the start and end of season (SoS and EoS, respectively) for the years 2019 and 2020 with a simple implementation of the threshold method in Google Earth Engine (GEE). We found a high level of similarity between Sentinel-2 and PhenoCam metrics; the best results were observed with Sentinel-2 enhanced vegetation index (EVI) (root mean squared error (RMSE) and mean error (ME) of 3.0 d and −0.3 d for the SoS, and 6.5 d and −3.8 d for the EoS, respectively), although other vegetation indices presented similar performances. The phenological maps of Sentinel-2 EVI compared well with the same maps extracted from the Moderate Resolution Imaging Spectroradiometer (MODIS) in homogeneous landscapes (RMSE and ME of 9.2 d and 2.9 d for the SoS, and 6.4 and −0.9 d for the EoS, respectively). Unreliable LSP estimates were filtered and a quality flag indicator was activated when the Sentinel-2 time series presented a long period (>40 d) of missing data; discontinuities were lower in spring and early summer (9.2%) than in late summer and autumn (39.4%). The Sentinel-2 high-resolution LSP maps and the GEE phenological extraction method will support vegetation monitoring and contribute to improving the representation of Artic vegetation phenology in land surface models.

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