Potential of ESA's Sentinel-2 for geological applications

Meer, Freek D. van der; Werff, H.M.A. van der; Ruitenbeek, F.J.A. van

doi:10.1016/j.rse.2014.03.022

Cited by 199 publications

(107 citation statements)

References 54 publications

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“…These satellites will be able to acquire images of the Earth covering 13 spectral bands with spatial resolutions of 10 m (four visible and NIR bands), 20 m (six red-edge/SWIR bands) and 60 m (three atmospheric correction bands). The revisit time will be only five days from the two-satellite constellation with open and free data access [54].…”

Section: Optimal Time Period For Collecting Satellite Imagesmentioning

confidence: 99%

Object-Based Greenhouse Horticultural Crop Identification from Multi-Temporal Satellite Imagery: A Case Study in Almeria, Spain

et al. 2015

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Greenhouse detection and mapping via remote sensing is a complex task, which has already been addressed in numerous studies. In this research, the innovative goal relies on the identification of greenhouse horticultural crops that were growing under plastic coverings on 30 September 2013. To this end, object-based image analysis (OBIA) and a decision tree classifier (DT) were applied to a set consisting of eight Landsat 8 OLI images collected from May to November 2013. Moreover, a single WorldView-2 satellite image acquired on 30 September 2013, was also used as a data source. In this approach, basic spectral information, textural features and several vegetation indices (VIs) derived from Landsat 8 and WorldView-2 multi-temporal satellite data were computed on previously segmented image objects in order to identify four of the most popular autumn crops cultivated under greenhouse in Almería, Spain (i.e., tomato, pepper, cucumber and aubergine). The best classification accuracy (81.3% overall accuracy) was achieved by using the full set of Landsat 8 time series. These results were considered good in the case of tomato and pepper crops, being significantly worse for cucumber and aubergine. These OPEN ACCESSRemote Sens. 2015, 7 7379 results were hardly improved by adding the information of the WorldView-2 image. The most important information for correct classification of different crops under greenhouses was related to the greenhouse management practices and not the spectral properties of the crops themselves.

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Section: Optimal Time Period For Collecting Satellite Imagesmentioning

confidence: 99%

Object-Based Greenhouse Horticultural Crop Identification from Multi-Temporal Satellite Imagery: A Case Study in Almeria, Spain

et al. 2015

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“…The recently launched Worldview-3 sensor has 8 SWIR bands, of which four were taken from the ASTER design [25]. Sentinel-2 lacks such narrow bands in the SWIR bands (Figure 1), and van der Meer et al [4] then also concluded that proxies obtained with Sentinel-2 were the same as what could be obtained with Landsat, albeit on a slightly higher spatial resolution. Iron is fourth most common mineral found in the earth crust [26].…”

Section: Introductionmentioning

confidence: 95%

“…Spectral position (λ, in nm) and bandwidth (∆λ, in nm), spatial resolution (R, in m), heritage and purpose of Sentinel-2 bands, according to the Copernicus derived user requirements. Modified after [2,3] and used with permission from [4]. The potential of Sentinel-2 has been explored for a variety of land surface parameter estimations: Leaf Area Index (LAI) [5][6][7]; chlorophyll and nitrogen [8]; biophysical parameters [9,10]; and red edge position [8,11].…”

Section: Introductionmentioning

confidence: 99%

“…The potential for geological applications was evaluated by van der Meer et al [4]. The Copernicus derived user requirements [3] predominantly aim at environmental monitoring and do not specify geological remote sensing other than clay minerals, as shown by the Sentinel-2 band purposes in Table 1.…”

Section: Introductionmentioning

confidence: 99%

“…Geologists increasingly study phenomena that are dynamic in space and time (e.g., active geothermal systems, oil and gas seeps, coastal environments), and they require time series of inter-calibrated and cross-calibrated data products rather than only archived data [4,15,16]. Geologic remote sensing is based on spectral analysis of minerals and rocks, initially conducted by Hunt and Salisbury [17][18][19].…”

Section: Introductionmentioning

confidence: 99%

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Sentinel-2 for Mapping Iron Absorption Feature Parameters

Werff

Meer

2015

Remote Sensing

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Iron is an indicator for soil fertility and the usability of an area for cultivating crops. Remote sensing is the only suitable tool for surveying large areas at a high temporal and spatial interval, yet a relative high spectral resolution is needed for mapping iron contents with reflectance data. Sentinel-2 has several bands that cover the 0.9 µm iron absorption feature, while space-borne sensors traditionally used for geologic remote sensing, like ASTER and Landsat, had only one band in this feature. In this paper, we introduce a curve-fitting technique for Sentinel-2 that approximates the iron absorption feature at a hyperspectral resolution. We test our technique on library spectra of different iron bearing minerals and we apply it to a Sentinel-2 image synthesized from an airborne hyperspectral dataset. Our method finds the wavelength position of maximum absorption and absolute absorption depth for minerals Beryl, Bronzite, Goethite, Jarosite and Hematite. Sentinel-2 offers information on the 0.9 µm absorption feature that until now was reserved for hyperspectral instruments. Being a satellite mission, this information comes at a lower spatial resolution than airborne hyperspectral data, but with a large spatial coverage and frequent revisit time.

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Estimating cover crop biomass nitrogen credits with Sentinel‐2 imagery and sites covariates

Xia

Guan

Copenhaver³

et al. 2021

Agronomy Journal

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Cover crops can positively impact productivity and the environment. While improved estimates of cover crop N benefits could help promote their adoption, little information is currently available at the broad scale. We conducted a multi-site study to determine whether use of satellite images and site factors could fill this gap. Six spectral indices were extracted from Sentinel-2 satellite imagery and used with modeling covariates to estimate cover crop properties and biomass N credits (biomass × N contents). The partial least squares regression (PLSR) models were calibrated and validated with samples from 42 cover crop fields located in the midwestern and northeastern United States collected in 2017-2018. Remote sensing (RS)-derived spectral indices strongly correlated (r > .7) with red clover (Trifolium pratense L.) but not with rye (Secale cereale L.) biomass. Growing degree days (GDDs), cover height, ground cover percentage, and temperature often had high importance (variable importance in projection >1) in PLSR models. Model predictive power was limited for estimates of biomass N credits when data from all validation sites and cover types were used (adjusted [adj] R 2 = .52). However, models for both biomass (adj R 2 = .81) and biomass N credits (adj R 2 = .89) were successful for red clover fields. This suggests N benefits could be more effectively modeled for specific cover crop types. We also found RS-based estimation of C/N ratios performed moderately well when applied to the complete dataset (adj R 2 = .54), suggesting a way to differentiate grass and legume cover crops that can potentially inform biogeochemical models. INTRODUCTIONInterest in cover crops has grown rapidly (Dunn et al.

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Potential of ESA's Sentinel-2 for geological applications

Cited by 199 publications

References 54 publications

Object-Based Greenhouse Horticultural Crop Identification from Multi-Temporal Satellite Imagery: A Case Study in Almeria, Spain

Object-Based Greenhouse Horticultural Crop Identification from Multi-Temporal Satellite Imagery: A Case Study in Almeria, Spain

Sentinel-2 for Mapping Iron Absorption Feature Parameters

Estimating cover crop biomass nitrogen credits with Sentinel‐2 imagery and sites covariates

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