Evaluation of BRDF Information Retrieved from Time-Series Multiangle Data of the Himawari-8 AHI

Recently, much attention has been given to using geostationary Earth orbit (GEO) meteorological satellite data for retrieving land surface parameters due to their high observation frequencies. However, their bidirectional reflectance distribution function (BRDF) information content with a single viewing angle has not been sufficiently investigated, which lays a foundation for subsequent quantitative estimation. In this study, we aim to comprehensively evaluate BRDF information from time-series observations from the Advanced Himawari Imager (AHI) onboard the GEO satellite Himawari-8. First, ~6.2 km monthly multiangle surface reflectances from POLDER onboard a low-Earth-orbiting (LEO) satellite with good angle distributions over various land types during 2008 were used as reference data, and corresponding 0.05° high-quality MODIS (i.e., onboard LEO satellites) and AHI datasets during four months in 2020 were obtained using cloud and aerosol property products. Then, indicators of angle distribution, BRDF change, and albedos were retrieved by the kernel-driven Ross-Li BRDF model from the three datasets, which were used for comparisons over different time spans. Generally, the quality of sun-viewing geometries varies dramatically for accumulated AHI observations according to the weight-of-determination, and wide-ranging anisotropic flat indices are obtained. The root-mean-square-errors of white sky albedos between AHI and MODIS half-month data are 0.018 and 0.033 in the red and near-infrared bands, respectively, achieving smaller values of 0.004 and 0.007 between the half-month and daily AHI data, respectively, due to small variances in sun-viewing geometries. The generally wide AHI BRDF variances and good consistency in albedo with MODIS show their potential for retrieving anisotropy information and albedo, while angle accumulation quality of AHI time-series observations must be considered.

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Land surface parameters retrieval using time series remotely sensed observations

2007 IEEE International Geoscience and Remote Sensing Symposium ◽

10.1109/igarss.2007.4423829 ◽

2007 ◽

Author(s):

Dongwei Wang ◽

Jindi Wang ◽

Zhiqiang Xiao ◽

Ge Zhang

Keyword(s):

Time Series ◽

Land Surface ◽

Remotely Sensed ◽

Surface Parameters ◽

Land Surface Parameters

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Inconsistency among Landsat Sensors in Land Surface Mapping: A Comprehensive Investigation Based on Simulation

Remote Sensing ◽

10.3390/rs13071383 ◽

2021 ◽

Vol 13 (7) ◽

pp. 1383

Author(s):

Feng Chen ◽

Chenxing Wang ◽

Yuansheng Zhang ◽

Zhenshi Yi ◽

Qiancong Fan ◽

...

Keyword(s):

Time Series ◽

Land Surface ◽

Near Infrared ◽

Specific Class ◽

Spectral Indices ◽

Surface Mapping ◽

Multispectral Data ◽

Multi Temporal ◽

Time Series Analyses ◽

Shortwave Infrared

Comprehensive investigations on the between-sensor comparability among Landsat sensors have been relatively limited compared with the increasing use of multi-temporal Landsat records in time series analyses. More seriously, the sensor-related difference has not always been considered in applications. Accordingly, comparisons were conducted among all Landsat sensors available currently, including Multispectral Scanner (MSS), Thematic Mappers (TM), Enhanced Thematic Mappers (ETM+), and Operational Land Imager (OLI)) in land cover mapping, based on a collection of synthesized, multispectral data. Compared to TM, OLI showed obvious between-sensor differences in channel reflectance, especially over the near infrared (NIR) and shortwave infrared (SWIR) channels, and presented positive bias in vegetation spectral indices. OLI did not always outperform TM and ETM+ in classification, which related to the methods used. Furthermore, the channels over SWIR of TM and its successors contributed largely to enhancement of inter-class separability and to improvement of classification. Currently, the inclusion of MSS data is confronted with significant challenges regarding the consistency of surface mapping. Considering the inconsistency among the Landsat sensors, it is applicable to generate a consistent time series of spectral indices through proper transformation models. Meanwhile, it suggests the generation of specific class(es) based on interest instead of including all classes simultaneously.

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A Method for Consistent Estimation of Multiple Land Surface Parameters From MODIS Top-of-Atmosphere Time Series Data

IEEE Transactions on Geoscience and Remote Sensing ◽

10.1109/tgrs.2017.2702609 ◽

2017 ◽

Vol 55 (9) ◽

pp. 5158-5173 ◽

Cited By ~ 10

Author(s):

Hanyu Shi ◽

Zhiqiang Xiao ◽

Shunlin Liang ◽

Han Ma

Keyword(s):

Time Series ◽

Land Surface ◽

Time Series Data ◽

Series Data ◽

Consistent Estimation ◽

Surface Parameters ◽

Land Surface Parameters

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A New Pseudoinvariant Near-Infrared Threshold Method for Relative Radiometric Correction of Aerial Imagery

Remote Sensing ◽

10.3390/rs11161931 ◽

2019 ◽

Vol 11 (16) ◽

pp. 1931 ◽

Cited By ~ 1

Author(s):

Hua Zhang ◽

Paul V. Zimba ◽

Emmanuel U. Nzewi

Keyword(s):

Time Series ◽

High Resolution ◽

Land Surface ◽

Vegetation Index ◽

Near Infrared ◽

Aerial Imagery ◽

Surface Reflectance ◽

Radiometric Correction ◽

Surface Patterns ◽

Relative Radiometric Correction

The utilization of high-resolution aerial imagery such as the National Agriculture Imagery Program (NAIP) data is often hampered by a lack of methods for retrieving surface reflectance from digital numbers. This study developed a new relative radiometric correction method to retrieve 1 m surface reflectance from NAIP imagery. The advantage of this method lies in the adaptive identification of pseudoinvariant (PIV) pixels from a time series of Landsat images that can fully characterize the temporally spectral variations of land surface. The identified PIV pixels allow for an effective conversion of digital numbers to surface reflectance, as demonstrated through the validation at 150 sites across the contiguous United States. The results show substantial improvement in the agreement of NAIP-derived normalized difference vegetation index (NDVI) values with Landsat-derived NDVI reference. Across the sites, root mean square error and mean absolute error were reduced from 0.37 ± 0.14 to 0.08 ± 0.07 and from 0.91 ± 0.64 to 0.18 ± 0.52, respectively. Over 70% PIV pixels on average were derived from vegetated areas, while water and developed areas together contributed 27% of the PIV pixels. As the NAIP program is continuing to generate new images across the country, the advantages of its high spatial resolution, national coverage, long time series, and regular revisits will make it an increasingly crucial data source for a variety of research and management applications. The proposed method could benefit many agricultural, hydrological, and urban studies that rely on NAIP imagery to quantify land surface patterns and dynamics. It could also be applied to improve the preprocessing of high-resolution aerial imagery in other countries.

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ECOCLIMAP-II: a climatologic global data base of ecosystems and land surface parameters at 1 km based on the analysis of time series of VEGETATION data

2007 IEEE International Geoscience and Remote Sensing Symposium ◽

10.1109/igarss.2007.4422971 ◽

2007 ◽

Cited By ~ 4

Author(s):

Stephanie Faroux ◽

Valery Masson ◽

Jean-Louis Roujean

Keyword(s):

Time Series ◽

Data Base ◽

Land Surface ◽

Surface Parameters ◽

Land Surface Parameters ◽

Global Data ◽

Analysis Of Time Series

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Improving Web QoE Monitoring for Encrypted Network Traffic through Time Series Modeling

ACM SIGMETRICS Performance Evaluation Review ◽

10.1145/3466826.3466840 ◽

2021 ◽

Vol 48 (4) ◽

pp. 37-40

Author(s):

Nikolas Wehner ◽

Michael Seufert ◽

Joshua Schuler ◽

Sarah Wassermann ◽

Pedro Casas ◽

...

Keyword(s):

Neural Networks ◽

Time Series ◽

Recurrent Neural Networks ◽

Quality Of Experience ◽

Unique Feature ◽

Time Series Modeling ◽

Web Browsing ◽

Large Dataset ◽

Efficient Approximation

This paper addresses the problem of Quality of Experience (QoE) monitoring for web browsing. In particular, the inference of common Web QoE metrics such as Speed Index (SI) is investigated. Based on a large dataset collected with open web-measurement platforms on different device-types, a unique feature set is designed and used to estimate the RUMSI - an efficient approximation to SI, with machinelearning based regression and classification approaches. Results indicate that it is possible to estimate the RUMSI accurately, and that in particular, recurrent neural networks are highly suitable for the task, as they capture the network dynamics more precisely.

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Histamine Control in Raw and Processed Tuna: A Rapid Tool Based on NIR Spectroscopy

Foods ◽

10.3390/foods10040885 ◽

2021 ◽

Vol 10 (4) ◽

pp. 885

Author(s):

Sergio Ghidini ◽

Luca Maria Chiesa ◽

Sara Panseri ◽

Maria Olga Varrà ◽

Adriana Ianieri ◽

...

Keyword(s):

Root Mean Square ◽

Near Infrared ◽

Nir Spectroscopy ◽

Partial Least Square ◽

Least Square ◽

Partial Least Square Regression ◽

Coefficient Of Determination ◽

Mean Square ◽

Tuna Fish ◽

Mean Square Errors

The present study was designed to investigate whether near infrared (NIR) spectroscopy with minimal sample processing could be a suitable technique to rapidly measure histamine levels in raw and processed tuna fish. Calibration models based on orthogonal partial least square regression (OPLSR) were built to predict histamine in the range 10–1000 mg kg−1 using the 1000–2500 nm NIR spectra of artificially-contaminated fish. The two models were then validated using a new set of naturally contaminated samples in which histamine content was determined by conventional high-performance liquid chromatography (HPLC) analysis. As for calibration results, coefficient of determination (r2) > 0.98, root mean square of estimation (RMSEE) ≤ 5 mg kg−1 and root mean square of cross-validation (RMSECV) ≤ 6 mg kg−1 were achieved. Both models were optimal also in the validation stage, showing r2 values > 0.97, root mean square errors of prediction (RMSEP) ≤ 10 mg kg−1 and relative range error (RER) ≥ 25, with better results showed by the model for processed fish. The promising results achieved suggest NIR spectroscopy as an implemental analytical solution in fish industries and markets to effectively determine histamine amounts.

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Textile Recognition and Sorting for Recycling at an Automated Line Using Near Infrared Spectroscopy

Recycling ◽

10.3390/recycling6010011 ◽

2021 ◽

Vol 6 (1) ◽

pp. 11

Author(s):

Kirsti Cura ◽

Niko Rintala ◽

Taina Kamppuri ◽

Eetta Saarimäki ◽

Pirjo Heikkilä

Keyword(s):

Infrared Spectroscopy ◽

Near Infrared Spectroscopy ◽

Near Infrared ◽

Nir Spectroscopy ◽

Automated Recognition ◽

Textile Materials ◽

Input Material ◽

Adequate Quality ◽

Automated Line

In order to add value to recycled textile material and to guarantee that the input material for recycling processes is of adequate quality, it is essential to be able to accurately recognise and sort items according to their material content. Therefore, there is a need for an economically viable and effective way to recognise and sort textile materials. Automated recognition and sorting lines provide a method for ensuring better quality of the fractions being recycled and thus enhance the availability of such fractions for recycling. The aim of this study was to deepen the understanding of NIR spectroscopy technology in the recognition of textile materials by studying the effects of structural fabric properties on the recognition. The identified properties of fabrics that led non-matching recognition were coating and finishing that lead different recognition of the material depending on the side facing the NIR analyser. In addition, very thin fabrics allowed NIRS to penetrate through the fabric and resulted in the non-matching recognition. Additionally, ageing was found to cause such chemical changes, especially in the spectra of cotton, that hampered the recognition.

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Assessment of the EUMETSAT Multi Decadal Land Surface Albedo Data Record from Meteosat Observations

Remote Sensing ◽

10.3390/rs13101992 ◽

2021 ◽

Vol 13 (10) ◽

pp. 1992

Author(s):

Alessio Lattanzio ◽

Michael Grant ◽

Marie Doutriaux-Boucher ◽

Rob Roebeling ◽

Jörg Schulz

Keyword(s):

Land Surface ◽

Surface Albedo ◽

Time Range ◽

Essential Variable ◽

Spatial Coverage ◽

Data Record ◽

Moderate Resolution Imaging Spectroradiometer ◽

Cloud Mask ◽

Cloud Screening

Surface albedo, defined as the ratio of the surface-reflected irradiance to the incident irradiance, is one of the parameters driving the Earth energy budget and it is for this reason an essential variable in climate studies. Instruments on geostationary satellites provide suitable observations allowing long-term monitoring of surface albedo from space. In 2012, EUMETSAT published Release 1 of the Meteosat Surface Albedo (MSA) data record. The main limitation effecting the quality of this release was non-removed clouds by the incorporated cloud screening procedure that caused too high albedo values, in particular for regions with permanent cloud coverage. For the generation of Release 2, the MSA algorithm has been replaced with the Geostationary Surface Albedo (GSA) one, able to process imagery from any geostationary imager. The GSA algorithm exploits a new, improved, cloud mask allowing better cloud screening, and thus fixing the major limitation of Release 1. Furthermore, the data record has an extended temporal and spatial coverage compared to the previous release. Both Black-Sky Albedo (BSA) and White-Sky Albedo (WSA) are estimated, together with their associated uncertainties. A direct comparison between Release 1 and Release 2 clearly shows that the quality of the retrieval improved significantly with the new cloud mask. For Release 2 the decadal trend is less than 1% over stable desert sites. The validation against Moderate Resolution Imaging Spectroradiometer (MODIS) and the Southern African Regional Science Initiative (SAFARI) surface albedo shows a good agreement for bright desert sites and a slightly worse agreement for urban and rain forest locations. In conclusion, compared with MSA Release 1, GSA Release 2 provides the users with a significantly more longer time range, reliable and robust surface albedo data record.

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