scholarly journals Radiometric Correction of Landsat-8 and Sentinel-2A Scenes Using Drone Imagery in Synergy with Field Spectroradiometry

2018 ◽  
Vol 10 (11) ◽  
pp. 1687 ◽  
Author(s):  
Joan-Cristian Padró ◽  
Francisco-Javier Muñoz ◽  
Luis Ávila ◽  
Lluís Pesquer ◽  
Xavier Pons
Keyword(s):  
Near Infrared ◽  
Field Measurements ◽  
Systematic Sampling ◽  
Landsat 8 ◽  
Radiometric Correction ◽  
Multispectral Data ◽  
Central Hypothesis ◽  
Sentinel 2A ◽  
Sentinel 2

The main objective of this research is to apply unmanned aerial system (UAS) data in synergy with field spectroradiometry for the accurate radiometric correction of Landsat-8 (L8) and Sentinel-2 (S2) imagery. The central hypothesis is that imagery acquired with multispectral UAS sensors that are well calibrated with highly accurate field measurements can fill in the scale gap between satellite imagery and conventional in situ measurements; this can be possible by sampling a larger area, including difficult-to-access land covers, in less time while simultaneously providing good radiometric quality. With this aim and by using near-coincident L8 and S2 imagery, we applied an upscaling workflow, whereby: (a) UAS-acquired multispectral data was empirically fitted to the reflectance of field measurements, with an extensive set of radiometric references distributed across the spectral domain; (b) drone data was resampled to satellite grids for comparison with the radiometrically corrected L8 and S2 official products (6S-LaSRC and Sen2Cor-SNAP, respectively) and the CorRad-MiraMon algorithm using pseudo-invariant areas, such as reflectance references (PIA-MiraMon), to examine their overall accuracy; (c) then, a subset of UAS data was used as reflectance references, in combination with the CorRad-MiraMon algorithm (UAS-MiraMon), to radiometrically correct the matching bands of UAS, L8, and S2; and (d) radiometrically corrected L8 and S2 scenes obtained with UAS-MiraMon were intercompared (intersensor coherence). In the first upscaling step, the results showed a good correlation between the field spectroradiometric measurements and the drone data in all evaluated bands (R2 > 0.946). In the second upscaling step, drone data indicated good agreement (estimated from root mean square error, RMSE) with the satellite official products in visible (VIS) bands (RMSEVIS < 2.484%), but yielded poor results in the near-infrared (NIR) band (RMSENIR > 6.688% was not very good due to spectral sensor response differences). In the third step, UAS-MiraMon indicated better agreement (RMSEVIS < 2.018%) than the other satellite radiometric correction methods in visible bands (6S-LaSRC (RMSE < 2.680%), Sen2Cor-SNAP (RMSE < 2.192%), and PIA-MiraMon (RMSE < 3.130%), but did not achieve sufficient results in the NIR band (RMSENIR < 7.530%); this also occurred with all other methods. In the intercomparison step, the UAS-MiraMon method achieved an excellent intersensor (L8-S2) coherence (RMSEVIS < 1%). The UAS-sampled area involved 51 L8 (30 m) pixels, 143 S2 (20 m) pixels, and 517 S2 (10 m) pixels. The drone time needed to cover this area was only 10 min, including areas that were difficult to access. The systematic sampling of the study area was achieved with a pixel size of 6 cm, and the raster nature of the sampling allowed for an easy but rigorous resampling of UAS data to the different satellite grids. These advances improve human capacities for conventional field spectroradiometry samplings. However, our study also shows that field spectroradiometry is the backbone that supports the full upscaling workflow. In conclusion, the synergy between field spectroradiometry, UAS sensors, and Landsat-like satellite data can be a useful tool for accurate radiometric corrections used in local environmental studies or the monitoring of protected areas around the world.

scholarly journals Assessing the Potential of LAPAN-A3 Data for Landuse/landcover Mapping

2018 ◽  
Vol 50 (2) ◽  
pp. 184 ◽  
Author(s):  
Zylshal Zylshal ◽  
Rachmad Wirawan ◽  
Dony Kushardono
Keyword(s):  
Near Infrared ◽  
Agricultural Land ◽  
Reference Data ◽  
Accuracy Assessment ◽  
Bare Soil ◽  
Landsat 8 ◽  
Landsat 8 Oli ◽  
Radiometric Correction ◽  
Multispectral Data ◽  
Blurring Effect

LAPAN-A3 / LAPAN-IPB is the third generation of micro-satellite developed by Indonesian National Institute of Aeronautics and Space (LAPAN). The satellite carries a multispectral push-broom sensor that can record the earth's surface at the visible and near-infrared spectrum. Being launched in June 2016, there has no been many publications related to the use of LAPAN-A3 multispectral data for landuse/landcover (LULC) mapping. This paper aims to provide information regarding the use of LAPAN-A3 data for the LULC extraction maximum likelihood algorithm as well as neural network and then evaluate the results. The LAPAN-A3 image was geometrically corrected by using Landsat-8 OLI image as reference data. Three test areas with a size of 1200x945 pixels are then selected for pixel-based classification with the two aforementioned algorithms. For comparison, both LAPAN-A3 and Landsat-8 data were classified for 3 test areas. Accuracy assessment was performed on both datasets using manually interpreted SPOT-6 Pansharpened image as reference data. Preliminary results showed that LAPAN-A3 were able to extract  10 different LULC classes, comprises of built-up area, forest, rivers, fishponds, shrubs, wetland forests, rice fields, sea, agricultural land, and bare soil. The overall accuracy of LAPAN-A3 data is generally lower than Landsat-8, which ranges from 49.76% to 71.74%. These results illustrate the potential of LAPAN-A3 data to derive LULC information. The lack of necessary parameters to perform radiometric correction and blurring effect are several issues that need to be solved to improve the accuracy LULC. 

scholarly journals A Multi-Channel Algorithm for Mapping Volcanic Thermal Anomalies by Means of Sentinel-2 MSI and Landsat-8 OLI Data

2019 ◽  
Vol 11 (23) ◽  
pp. 2876 ◽  
Author(s):  
Francesco Marchese ◽  
Nicola Genzano ◽  
Marco Neri ◽  
Alfredo Falconieri ◽  
Giuseppe Mazzeo ◽  
...  
Keyword(s):  
Near Infrared ◽  
Short Wave ◽  
Global Scale ◽  
Landsat 8 ◽  
Thermal Anomalies ◽  
Volcanic Plumes ◽  
Processing Times ◽  
Multi Temporal ◽  
Sentinel 2A ◽  
Sentinel 2

The Multispectral Instrument (MSI) and the Operational Land Imager (OLI), respectively onboard Sentinel-2A/2B and Landsat 8 satellites, thanks to their features especially in terms of spatial/spectral resolution, represents two important instruments for investigating thermal volcanic activity from space. In this study, we used data from those sensors to test an original multichannel algorithm, which aims at mapping volcanic thermal anomalies at a global scale. The algorithm, named Normalized Hotspot Indices (NHI), combines two normalized indices, analyzing near infrared (NIR) and short wave infrared (SWIR) radiances, to identify hotspot pixels in daylight conditions. Results, achieved studying a number of active volcanoes located in different geographic areas and characterized by a different eruptive behavior, demonstrated the NHI capacity in mapping both subtle and more intense volcanic thermal anomalies despite some limitations (e.g., missed detections because of clouds/volcanic plumes). In addition, the study shows that the performance of NHI might be further increased using some additional spectral/spatial tests, in view of a possible usage of this algorithm within a known multi-temporal scheme of satellite data analysis. The low processing times and the straight forth exportability to data from other sensors make NHI, which is sensitive even to other high temperature sources, suited for mapping hot volcanic targets integrating information provided by current and well-established satellite-based volcanoes monitoring systems.

Evaluating the Persistence of Post-Wildfire Ash: A Multi-Platform Spatiotemporal Analysis

Fire ◽  
2021 ◽  
Vol 4 (4) ◽  
pp. 68
Author(s):  
Sarah A. Lewis ◽  
Peter R. Robichaud ◽  
Andrew T. Hudak ◽  
Eva K. Strand ◽  
Jan U. H. Eitel ◽  
...  
Keyword(s):  
Vegetation Index ◽  
Normalized Difference Vegetation Index ◽  
Field Measurements ◽  
Cost Effective ◽  
Spatiotemporal Analysis ◽  
Short Wave ◽  
Hydrologic Model ◽  
Landsat 8 ◽  
Data Volume ◽  
Sentinel 2

As wildland fires amplify in size in many regions in the western USA, land and water managers are increasingly concerned about the deleterious effects on drinking water supplies. Consequences of severe wildfires include disturbed soils and areas of thick ash cover, which raises the concern of the risk of water contamination via ash. The persistence of ash cover and depth were monitored for up to 90 days post-fire at nearly 100 plots distributed between two wildfires in Idaho and Washington, USA. Our goal was to determine the most ‘cost’ effective, operational method of mapping post-wildfire ash cover in terms of financial, data volume, time, and processing costs. Field measurements were coupled with multi-platform satellite and aerial imagery collected during the same time span. The image types spanned the spatial resolution of 30 m to sub-meter (Landsat-8, Sentinel-2, WorldView-2, and a drone), while the spectral resolution spanned visible through SWIR (short-wave infrared) bands, and they were all collected at various time scales. We that found several common vegetation and post-fire spectral indices were correlated with ash cover (r = 0.6–0.85); however, the blue normalized difference vegetation index (BNDVI) with monthly Sentinel-2 imagery was especially well-suited for monitoring the change in ash cover during its ephemeral period. A map of the ash cover can be used to estimate the ash load, which can then be used as an input into a hydrologic model predicting ash transport and fate, helping to ultimately improve our ability to predict impacts on downstream water resources.

scholarly journals Formation of sea ice ponds from ice-shelf runoff, adjacent to the McMurdo Ice Shelf, Antarctica

2020 ◽  
Vol 61 (82) ◽  
pp. 73-77 ◽  
Author(s):  
Grant J. Macdonald ◽  
Predrag Popović ◽  
David P. Mayer
Keyword(s):  
Sea Ice ◽  
Added Mass ◽  
Surface Melting ◽  
Landsat 8 ◽  
Ice Shelf ◽  
Surface Hydrology ◽  
Ice Shelves ◽  
Arctic Ponds ◽  
Sentinel 2

AbstractPonds that form on sea ice can cause it to thin or break-up, which can promote calving from an adjacent ice shelf. Studies of sea ice ponds have predominantly focused on Arctic ponds formed by in situ melting/ponding. Our study documents another mechanism for the formation of sea ice ponds. Using Landsat 8 and Sentinel-2 images from the 2015–16 to 2018–19 austral summers, we analyze the evolution of sea ice ponds that form adjacent to the McMurdo Ice Shelf, Antarctica. We find that each summer, meltwater flows from the ice shelf onto the sea ice and forms large (up to 9 km2) ponds. These ponds decrease the sea ice's albedo, thinning it. We suggest the added mass of runoff causes the ice to flex, potentially promoting sea-ice instability by the ice-shelf front. As surface melting on ice shelves increases, we suggest that ice-shelf surface hydrology will have a greater effect on sea-ice stability.

scholarly journals Retrieving Total and Inorganic Suspended Sediments in Amazon Floodplain Lakes: A Multisensor Approach

2019 ◽  
Vol 11 (15) ◽  
pp. 1744 ◽  
Author(s):  
Daniel Maciel ◽  
Evlyn Novo ◽  
Lino Sander de Carvalho ◽  
Cláudio Barbosa ◽  
Rogério Flores Júnior ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Atmospheric Correction ◽  
Suspended Sediments ◽  
Floodplain Lakes ◽  
Empirical Modeling ◽  
Landsat 8 ◽  
Amazon Floodplain ◽  
The Impact ◽  
Sentinel 2

Remote sensing imagery are fundamental to increasing the knowledge about sediment dynamics in the middle-lower Amazon floodplains. Moreover, they can help to understand both how climate change and how land use and land cover changes impact the sediment exchange between the Amazon River and floodplain lakes in this important and complex ecosystem. This study investigates the suitability of Landsat-8 and Sentinel-2 spectral characteristics in retrieving total (TSS) and inorganic (TSI) suspended sediments on a set of Amazon floodplain lakes in the middle-lower Amazon basin using in situ Remote Sensing Reflectance (Rrs) measurements to simulate Landsat 8/OLI (Operational Land Imager) and Sentinel 2/MSI (Multispectral Instrument) bands and to calibrate/validate several TSS and TSI empirical algorithms. The calibration was based on the Monte Carlo Simulation carried out for the following datasets: (1) All-Dataset, consisting of all the data acquired during four field campaigns at five lakes spread over the lower Amazon floodplain (n = 94); (2) Campaign-Dataset including samples acquired in a specific hydrograph phase (season) in all lakes. As sample size varied from one season to the other, n varied from 18 to 31; (3) Lake-Dataset including samples acquired in all seasons at a given lake with n also varying from 17 to 67 for each lake. The calibrated models were, then, applied to OLI and MSI scenes acquired in August 2017. The performance of three atmospheric correction algorithms was also assessed for both OLI (6S, ACOLITE, and L8SR) and MSI (6S, ACOLITE, and Sen2Cor) images. The impact of glint correction on atmosphere-corrected image performance was assessed against in situ glint-corrected Rrs measurements. After glint correction, the L8SR and 6S atmospheric correction performed better with the OLI and MSI sensors, respectively (Mean Absolute Percentage Error (MAPE) = 16.68% and 14.38%) considering the entire set of bands. However, for a given single band, different methods have different performances. The validated TSI and TSS satellite estimates showed that both in situ TSI and TSS algorithms provided reliable estimates, having the best results for the green OLI band (561 nm) and MSI red-edge band (705 nm) (MAPE < 21%). Moreover, the findings indicate that the OLI and MSI models provided similar errors, which support the use of both sensors as a virtual constellation for the TSS and TSI estimate over an Amazon floodplain. These results demonstrate the applicability of the calibration/validation techniques developed for the empirical modeling of suspended sediments in lower Amazon floodplain lakes using medium-resolution sensors.

scholarly journals An UAV and Satellite Multispectral Data Approach to Monitor Water Quality in Small Reservoirs

2020 ◽  
Vol 12 (9) ◽  
pp. 1514 ◽  
Author(s):  
Carmen Cillero Castro ◽  
Jose Antonio Domínguez Gómez ◽  
Jordi Delgado Martín ◽  
Boris Alejandro Hinojo Sánchez ◽  
Jose Luis Cereijo Arango ◽  
...  
Keyword(s):  
Near Infrared ◽  
Spectral Band ◽  
Water Reservoir ◽  
Cyanobacterial Blooms ◽  
Landsat 8 ◽  
Chl A ◽  
Multispectral Data ◽  
Small Reservoirs ◽  
In Situ Data ◽  
Drinking Water Reservoir

A multi-sensor and multi-scale monitoring tool for the spatially explicit and periodic monitoring of eutrophication in a small drinking water reservoir is presented. The tool was built with freely available satellite and in situ data combined with Unmanned Aerial Vehicle (UAV)-based technology. The goal is to evaluate the performance of a multi-platform approach for the trophic state monitoring with images obtained with MultiSpectral Sensors on board satellites Sentinel 2 (S2A and S2B), Landsat 8 (L8) and UAV. We assessed the performance of three different sensors (MultiSpectral Instrument (MSI), Operational Land Imager (OLI) and Rededge Micasense) for retrieving the pigment chlorophyll-a (chl-a), as a quantitative descriptor of phytoplankton biomass and trophic level. The study was conducted in a waterbody affected by cyanobacterial blooms, one of the most important eutrophication-derived risks for human health. Different empirical models and band indices were evaluated. Spectral band combinations using red and near-infrared (NIR) bands were the most suitable for retrieving chl-a concentration (especially 2 band algorithm (2BDA), the Surface Algal Bloom Index (SABI) and 3 band algorithm (3BDA)) even though blue and green bands were useful to classify UAV images into two chl-a ranges. The results show a moderately good agreement among the three sensors at different spatial resolutions (10 m., 30 m. and 8 cm.), indicating a high potential for the development of a multi-platform and multi-sensor approach for the eutrophication monitoring of small reservoirs.

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

2019 ◽  
Vol 11 (19) ◽  
pp. 2304 ◽  
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.

scholarly journals The impact of sensors for satellite derived bathymetry within the Canadian Arctic

GEOMATICA ◽  
2020 ◽  
Vol 74 (2) ◽  
pp. 46-64
Author(s):  
Ryan Ahola ◽  
René Chénier ◽  
Mesha Sagram ◽  
Bradley Horner
Keyword(s):  
Optical Sensors ◽  
Landsat 8 ◽  
Sea Floor ◽  
Arctic Regions ◽  
Canadian Context ◽  
Multiple Band ◽  
Survey Accuracy ◽  
The Impact ◽  
Sentinel 2

Canada’s coastline presents challenges for charting. Within Arctic regions, in situ surveying presents risks to surveyors, is time consuming and costly. To better meet its mandate, the Canadian Hydrographic Service (CHS) has been investigating the potential of remote sensing to complement traditional charting techniques. Much of this work has focused on evaluating the effectiveness of empirical satellite derived bathymetry (SDB) techniques within the Canadian context. With greater knowledge of applying SDB techniques within Canadian waters, CHS is now interested in understanding how characteristics of optical sensors can impact SDB results. For example, how does the availability of different optical bands improve or hinder SDB estimates? What is the impact of spatial resolution on SDB accuracy? Do commercial satellites offer advantages over freely available data? Through application of a multiple band modelling technique to WorldView-2, Pléiades, PlanetScope, SPOT, Sentinel-2, and Landsat-8 imagery obtained over Cambridge Bay, Nunavut, this paper provides insight into these questions via comparisons with in situ survey data. Result highlights in the context of these questions include the following: Similarities between sensors: Overall linear error at 90% (LE90) results for each sensor ranged from 0.88 to 1.91 m relative to in situ depths, indicating consistency in the accuracy of SDB estimates from the examined satellites. Most estimates achieved Category of Zone of Confidence level C accuracy, the suggested minimum survey accuracy level for incorporating SDB information into navigational charts. SDB coverage: Between sensors, differences in the area of the sea floor that could be measured by SDB were apparent, as were differences in the ability of each sensor to properly represent spatial bathymetry characteristics. Sensor importance: Though relationships between SDB accuracy and sensor resolution were found, significant advantages or disadvantages for particular sensors were not identified, suggesting that other factors may play a more important role for SDB image selection (e.g., sea floor visibility, sediments, waves). Findings from this work will help inform SBD planning activities for hydrographic offices and SDB researchers alike.

scholarly journals Evaluation of Atmospheric Correction Algorithms over Spanish Inland Waters for Sentinel-2 Multi Spectral Imagery Data

2019 ◽  
Vol 11 (12) ◽  
pp. 1469 ◽  
Author(s):  
Marcela Pereira-Sandoval ◽  
Ana Ruescas ◽  
Patricia Urrego ◽  
Antonio Ruiz-Verdú ◽  
Jesús Delegido ◽  
...  
Keyword(s):  
Near Infrared ◽  
Atmospheric Correction ◽  
Inland Waters ◽  
Atmospheric Effects ◽  
Image Correction ◽  
Lakes And Reservoirs ◽  
Dark Pixel ◽  
Organic Particles ◽  
Sentinel 2

The atmospheric contribution constitutes about 90 percent of the signal measured by satellite sensors over oceanic and inland waters. Over open ocean waters, the atmospheric contribution is relatively easy to correct as it can be assumed that water-leaving radiance in the near-infrared (NIR) is equal to zero and it can be performed by applying a relatively simple dark-pixel-correction-based type of algorithm. Over inland and coastal waters, this assumption cannot be made since the water-leaving radiance in the NIR is greater than zero due to the presence of water components like sediments and dissolved organic particles. The aim of this study is to determine the most appropriate atmospheric correction processor to be applied on Sentinel-2 MultiSpectral Imagery over several types of inland waters. Retrievals obtained from different atmospheric correction processors (i.e., Atmospheric correction for OLI ‘lite’ (ACOLITE), Case 2 Regional Coast Colour (here called C2RCC), Case 2 Regional Coast Colour for Complex waters (here called C2RCCCX), Image correction for atmospheric effects (iCOR), Polynomial-based algorithm applied to MERIS (Polymer) and Sen2Cor or Sentinel 2 Correction) are compared against in situ reflectance measured in lakes and reservoirs in the Valencia region (Spain). Polymer and C2RCC are the processors that give back the best statistics, with coefficients of determination higher than 0.83 and mean average errors less than 0.01. An evaluation of the performance based on water types and single bands–classification based on ranges of in situ chlorophyll-a concentration and Secchi disk depth values- showed that performance of these set of processors is better for relatively complex waters. ACOLITE, iCOR and Sen2Cor had a better performance when applied to meso- and hyper-eutrophic waters, compare with oligotrophic. However, other considerations should also be taken into account, like the elevation of the lakes above sea level, their distance from the sea and their morphology.

scholarly journals Soil Organic Carbon Mapping Using Multispectral Remote Sensing Data: Prediction Ability of Data with Different Spatial and Spectral Resolutions

2019 ◽  
Vol 11 (24) ◽  
pp. 2947 ◽  
Author(s):  
Daniel Žížala ◽  
Robert Minařík ◽  
Tereza Zádorová
Keyword(s):  
Prediction Accuracy ◽  
Remote Sensing Data ◽  
Cost Effective ◽  
Hyperspectral Data ◽  
Landsat 8 ◽  
Prediction Ability ◽  
Multispectral Data ◽  
Multispectral Remote Sensing ◽  
Data Source ◽  
Sentinel 2

The image spectral data, particularly hyperspectral data, has been proven as an efficient data source for mapping of the spatial variability of soil organic carbon (SOC). Multispectral satellite data are readily available and cost-effective sources of spectral data compared to costly and technically demanding processing of hyperspectral data. Moreover, their continuous acquisition allows to develop a composite from time-series, increasing the spatial coverage of SOC maps. In this study, an evaluation of the prediction ability of models assessing SOC using real multispectral remote sensing data from different platforms was performed. The study was conducted on a study plot (1.45 km2) in the Chernozem region of South Moravia (Czechia). The adopted methods included field sampling and predictive modeling using satellite multispectral Sentinel-2, Landsat-8, and PlanetScope data, and multispectral UAS Parrot Sequoia data. Furthermore, the performance of a soil reflectance composite image from Sentinel-2 data was analyzed. Aerial hyperspectral CASI 1500 and SASI 600 data was used as a reference. Random forest, support vector machine, and the cubist regression technique were applied in the predictive modeling. The prediction accuracy of models using multispectral data, including Sentinel-2 composite, was lower (RPD range from 1.16 to 1.65; RPIQ range from 1.53 to 2.17) compared to the reference model using hyperspectral data (RPD = 2.26; RPIQ = 3.34). The obtained results show very similar prediction accuracy for all spaceborne sensors (Sentinel-2, Landsat-8, and PlanetScope). However, the spatial correlation between the reference mapping results obtained from the hyperspectral data and other maps using multispectral data was moderately strong. UAS sensors and freely available satellite multispectral data can represent an alternative cost-effective data source for remote SOC mapping on the local scale.

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