Characterization of Sentinel-2A and Landsat-8 top of atmosphere, surface, and nadir BRDF adjusted reflectance and NDVI differences

Zhang, Hankui K.; Roy, David P.; Yan, Lin; Li, Zhongbin; Huang, Haiyan; Vermote, Éric; Skakun, Sergii; Roger, Jean-Claude

doi:10.1016/j.rse.2018.04.031

Cited by 267 publications

(199 citation statements)

References 51 publications

Supporting

Mentioning

183

Contrasting

Unclassified

Order By: Relevance

“…The difference between sensors was again pronounced, with a much larger gap between aquatic methods for S2 (0.002 sr −1 ) than for L8 (0.0009 sr −1 ). Zhang et al (2018) reported S2 produced higher R rs values than L8 over land. These differences are likely related to environmental conditions or ground sampling resolution and not sensor specifications (Pahlevan et al, 2017a(Pahlevan et al, , 2017b.…”

Section: Aquatic Correctionsmentioning

confidence: 84%

Performance of Landsat-8 and Sentinel-2 surface reflectance products for river remote sensing retrievals of chlorophyll-a and turbidity

Kuhn

Valério

Ward

et al. 2019

Remote Sensing of Environment

Self Cite

221

127

View full text Add to dashboard Cite

Rivers and other freshwater systems play a crucial role in ecosystems, industry, transportation and agriculture. Despite the > 40 years of inland water observations made possible by optical remote sensing, a standardized reflectance product for inland waters is yet forthcoming. The aim of this work is to compare the standard USGS land surface reflectance product to two Landsat-8 and Sentinel-2 aquatic remote sensing reflectance products over the Amazon, Columbia and Mississippi rivers. Landsat-8 reflectance products from all three routines are then evaluated for their comparative performance in retrieving chlorophyll-a and turbidity in reference to shipborne, underway in situ validation measurements. The land surface product shows the best agreement (4% Mean Absolute Percent Difference) with field measurements of radiometry collected on the Amazon River and generates 36% higher reflectance values in the visible bands compared to aquatic methods (ACOLITE and SeaDAS) with larger differences between land and aquatic products observed in Sentinel-2 (0.01 sr −1 ) compared to Landsat-8 (0.001 sr −1 ). Choice of atmospheric correction routine can bias Landsat-8 retrievals of chlorophyll-a and turbidity by as much as 59% and 35% respectively. Using a more restrictive time window for matching in situ and satellite imagery can reduce differences by 5-31% depending on correction technique. This work highlights the challenges of satellite retrievals over rivers and underscores the need for future optical and biogeochemical research aimed at improving our understanding of the absorbing and scattering properties of river water and their relationships to remote sensing reflectance. sediment loading, warming and eutrophication (Whitehead et al., 2009;Malmqvist et al., 2008). In terrestrial, ocean, coastal and lake ecosystems, satellites have been increasingly marshalled for ecological monitoring (Smith, 2003;Valerio et al., 2017), yet rivers have received relatively little attention in the field of aquatic remote sensing, in part

show abstract

Section: Aquatic Correctionsmentioning

confidence: 84%

Performance of Landsat-8 and Sentinel-2 surface reflectance products for river remote sensing retrievals of chlorophyll-a and turbidity

Kuhn

Valério

Ward

et al. 2019

Remote Sensing of Environment

Self Cite

221

127

View full text Add to dashboard Cite

show abstract

“…The Landsat ARD provide an important step in this direction. However, ensuring data interoperability and seamless sensor combination or harmonization with non-Landsat data is non-trivial, and, for example, research on this for Landsat and the Landsatlike Sentinel 2 data is underway [7,[60][61][62].…”

Section: Discussionmentioning

confidence: 99%

Analysis Ready Data: Enabling Analysis of the Landsat Archive

Dwyer¹,

Roy²,

Sauer³

et al. 2018

Preprint

Self Cite

127

121

View full text Add to dashboard Cite

Abstract:Data that have been processed to allow analysis with a minimum of additional user effort are often referred to as Analysis Ready Data (ARD). The ability to perform large scale Landsat analysis relies on the ability to access observations that are geometrically and radiometrically consistent, and have had non-target features (clouds) and poor quality observations flagged so that they can be excluded. The United States Geological Survey (USGS) has processed all of the Landsat 4 and 5 Thematic Mapper (TM), Landsat 7 Enhanced Thematic Mapper Plus (ETM+), Landsat 8 Operational Land Imager (OLI) and Thermal Infrared Sensor (TIRS) archive over the conterminous United States (CONUS), Alaska, and Hawaii, into Landsat ARD. The ARD are available to significantly reduce the burden of pre-processing on users of Landsat data. Provision of pre-prepared ARD is intended to make it easier for users to produce Landsat-based maps of land cover and land-cover change and other derived geophysical and biophysical products. The ARD are provided as tiled, georegistered, top of atmosphere and atmospherically corrected products defined in a common equal area projection, accompanied by spatially explicit quality assessment information, and appropriate metadata to enable further processing while retaining traceability of data provenance.

show abstract

“…Different studies had evaluated the performances of Landsat-8 and Sentinel-2 data based on diversified applications viz. detection of greenhouse (Novelli et al 2016), estimation of forest canopy cover and Leaf Area Index (LAI) (Korhonen et al 2017), detection of C3 and C4 grass species (Shoko and Mutanga 2017), land use land cover mapping (Forkuor et al 2018), characterization of reflectance and Normalized Difference Vegetation Index (NDVI) (Zhang et al 2018) etc. and in most of the applications it was evident that Sentinel-2 was performing better because of its improved spatial and spectral capabilities.…”

Section: Introductionmentioning

confidence: 99%

Comparison of Landsat-8 and Sentinel-2 Data for Classification of Rabi Crops Over Karnataka, India

Paul

Kumar

2019

Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci.

View full text Add to dashboard Cite

Classification of crops is very important to study different growth stages and forecast yield. Remote sensing data plays a significant role in crop identification and condition assessment over a large spatial scale. Importance of Normalized Difference Indices (NDIs) along with surface reflectances of remotely sensed spectral bands have been evaluated for classification of eight types of Rabi crops utilizing the Landsat-8 and Sentinel-2 datasets and performances of both the satellites are compared. Landsat-8 and Sentinel-2A images are acquired for the location of crops and seven and nine spectral bands are utilized respectively for the classification. Experiments are carried out considering the different combinations of surface reflectances of spectral bands and optimal NDIs as features in support vector machine classifier. Optimal NDIs are selected from the set of 7 C 2 and 9 C 2 NDIs of Landsat-8 and Sentinel-2A datasets respectively using the partial informational correlation measure, a nonparametric feature selection approach. Few important vegetation indices (e.g. enhanced vegetation index) are also experimented in combination with the surface reflectances and NDIs to perform the crop classification. It has been observed that combination of surface reflectances and optimal NDIs can classify the crops more efficiently. The average overall accuracy of 80.96% and 88.16% are achieved using the Landsat-8 and Sentinel-2A datasets respectively. It has been observed that all the crop classes except Paddy and Cotton achieve producer accuracy and user accuracy of more than 75% and 85% respectively. This technique can be implemented for crop identification with adequate accessibility of crop information.

show abstract

Characterization of Sentinel-2A and Landsat-8 top of atmosphere, surface, and nadir BRDF adjusted reflectance and NDVI differences

Cited by 267 publications

References 51 publications

Performance of Landsat-8 and Sentinel-2 surface reflectance products for river remote sensing retrievals of chlorophyll-a and turbidity

Performance of Landsat-8 and Sentinel-2 surface reflectance products for river remote sensing retrievals of chlorophyll-a and turbidity

Analysis Ready Data: Enabling Analysis of the Landsat Archive

Comparison of Landsat-8 and Sentinel-2 Data for Classification of Rabi Crops Over Karnataka, India

Contact Info

Product

Resources

About