Diffuse Attenuation Coefficient (Kd) from ICESat-2 ATLAS Spaceborne Lidar Using Random-Forest Regression

2021 ◽  
Vol 87 (11) ◽  
pp. 831-840
Author(s):  
Forrest Corcoran ◽  
Christopher E. Parrish
Keyword(s):  
Attenuation Coefficient ◽  
Infrared Imaging ◽  
Impulse Response Function ◽  
Geographic Location ◽  
Data Sets ◽  
Random Forest Regression ◽  
Diffuse Attenuation Coefficient ◽  
Multispectral Satellite Imagery ◽  
Kd Values ◽  
Using Data

This study investigates a new method for measuring water turbidity—specifically, the diffuse attenuation coefficient of downwelling irradiance Kd —using data from a spaceborne, green-wavelength lidar aboard the National Aeronautics and Space Administration's ICESat-2 satellite. The method enables us to fill nearshore data voids in existing Kd data sets and provides a more direct measurement approach than methods based on passive multispectral satellite imagery. Furthermore, in contrast to other lidar-based methods, it does not rely on extensive signal processing or the availability of the system impulse response function, and it is designed to be applied globally rather than at a specific geographic location. The model was tested using Kd measurements from the National Oceanic and Atmospheric Administration's Visible Infrared Imaging Radiometer Suite sensor at 94 coastal sites spanning the globe, with Kd values ranging from 0.05 to 3.6 m –1 . The results demonstrate the efficacy of the approach and serve as a benchmark for future machine-learning regression studies of turbidity using ICESat-2.

TROPOMI's potential for ocean applications

2020 ◽  
Author(s):  
Julia Oelker ◽  
Svetlana Losa ◽  
Mariana Altenburg Soppa ◽  
Andreas Richter ◽  
Astrid Bracher
Keyword(s):  
Attenuation Coefficient ◽  
Heat Budget ◽  
Solar Spectrum ◽  
Optical Absorption Spectroscopy ◽  
Fluorescence Signal ◽  
Data Sets ◽  
Phytoplankton Diversity ◽  
Diffuse Attenuation Coefficient ◽  
Underwater Light Field ◽  
Surface Ocean

<p>The backscattered light from within the ocean carries information about surface ocean optical constituents, e.g., phytoplankton and the amount of light in the ocean. Global quantified insight in these parameters is important for estimating primary productivity and heat budget, and for a better understanding and modeling of biogeochemical cycles. Atmospheric sensors such as SCIAMACHY and GOME-2 have proven to yield valuable information on phytoplankton diversity, sun-induced marine fluorescence, and the underwater light field. As commonly used for the retrieval of atmospheric trace gases, the oceanic parameters are inferred from differential optical absorption spectroscopy combined with radiative transfer modeling. Within the ESA Sentinel-5p+ Innovation themes, we explore TROPOMI's potential for deriving the diffuse attenuation coefficient, quantification of different phytoplankton groups and the fluorescence signal of phytoplankton. Here we present results on deriving the diffuse attenuation coefficient from the vibrational Raman scattering signal in backscattered radiances measured by TROPOMI. The diffuse attenuation coefficient describes how fast the incoming radiation in the ocean is diminished with depth. Retrieval results in three spectral regions covering the ultraviolet and blue region of the solar spectrum are presented and intercompared. In future, we will explore if information on sources of colored dissolved organic matter and ultraviolet-absorbing phytoplankton pigments can be inferred from these data sets.</p>

The potential of Sentinel-5P’s high spectral resolution for ocean applications

2021 ◽  
Author(s):  
Astrid Bracher ◽  
Julia Oelker ◽  
Svetlana Losa ◽  
Mariana Altenburg Soppa ◽  
Andreas Richter ◽  
...  
Keyword(s):  
Attenuation Coefficient ◽  
Spectral Range ◽  
Short Wave ◽  
High Spectral Resolution ◽  
Optical Absorption Spectroscopy ◽  
Data Sets ◽  
Phytoplankton Diversity ◽  
Diffuse Attenuation Coefficient ◽  
In Situ Data ◽  
Spatial Coverage

<p>Hyperspectral satellite data are a source of the top of the atmosphere radiance signal which can be used for novel algorithms aimed for observations of marine ecosystems and the light-lit ocean. Atmospheric sensors such as SCIAMACHY, GOME-2 and OMI have proven in the past to yield valuable information on phytoplankton diversity, sun-induced marine fluorescence, and the underwater light field, however at low coverage and spatial resolution. Within the ESA Sentinel-5p+ Innovation themes, we explore TROPOMI's potential for deriving the diffuse attenuation coefficient and the quantification of different phytoplankton groups. As commonly used for the retrieval of atmospheric trace gases, we apply the differential optical absorption spectroscopy combined with radiative transfer modeling (RTM) to infer these oceanic parameters. We present results on a measure describing the diminishing of incoming radiation in the ocean with depth, the diffuse attenuation coefficient KD. KD is derived by the retrieval of the vibrational Raman scattering signal in backscattered radiances measured by TROPOMI in the UV and spectral range which then is further converted to the associated KD using RTM. The final TROMPOMI KD data sets resolved for three spectral regions (UV-B+short wave UV-A, UV-A and short blue) agree well with in situ data sampled during an expedition with RV Polarstern in 2018 in the Atlantic Ocean.  Further, KD-blue compared to wavelength-converted KD(490nm) products (OLCI-A and the merged OC-CCI) from common, multispectral, ocean color sensors, show that differences between the three data sets are within uncertainties given for the OC-CCI product. Our study shows for the first time KD products for the UV spectral range retrieved from space based data. TROPOMI KD-blue results have higher quality and much higher spatial coverage and resolution than previous ones from SCIAMACHY, GOME-2 and OMI.  Additionally, first results on TROPOMI’s potential for retrieving three phytoplankton groups will be shown and compared to similar multispectral phytoplankton group data for the same time period and ocean region as shown for TROPOMI KD.</p>

Assessment of Regional Explosion Discriminants Using Data Sets of Unparalleled Spatial Sampling

2012 ◽  
Author(s):  
Kate C. Miller ◽  
Lindsay L. Worthington ◽  
Steven Harder ◽  
Scott Phillips ◽  
Hans Hartse ◽  
...  
Keyword(s):  
Spatial Sampling ◽  
Data Sets ◽  
Using Data

scholarly journals Using Machine Learning Methods to Identify Particle Types from Doppler Lidar Measurements in Iceland

2021 ◽  
Vol 13 (13) ◽  
pp. 2433
Author(s):  
Shu Yang ◽  
Fengchao Peng ◽  
Sibylle von Löwis ◽  
Guðrún Nína Petersen ◽  
David Christian Finger
Keyword(s):  
Machine Learning ◽  
Weather Conditions ◽  
Dust Storms ◽  
Machine Learning Algorithms ◽  
Lidar Data ◽  
Data Sets ◽  
Doppler Lidar ◽  
Lidar Measurements ◽  
Using Data ◽  
Filter Noise

Doppler lidars are used worldwide for wind monitoring and recently also for the detection of aerosols. Automatic algorithms that classify the lidar signals retrieved from lidar measurements are very useful for the users. In this study, we explore the value of machine learning to classify backscattered signals from Doppler lidars using data from Iceland. We combined supervised and unsupervised machine learning algorithms with conventional lidar data processing methods and trained two models to filter noise signals and classify Doppler lidar observations into different classes, including clouds, aerosols and rain. The results reveal a high accuracy for noise identification and aerosols and clouds classification. However, precipitation detection is underestimated. The method was tested on data sets from two instruments during different weather conditions, including three dust storms during the summer of 2019. Our results reveal that this method can provide an efficient, accurate and real-time classification of lidar measurements. Accordingly, we conclude that machine learning can open new opportunities for lidar data end-users, such as aviation safety operators, to monitor dust in the vicinity of airports.

scholarly journals A novel framework for designing a multi-DoF prosthetic wrist control using machine learning

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Chinmay P. Swami ◽  
Nicholas Lenhard ◽  
Jiyeon Kang
Keyword(s):  
Machine Learning ◽  
Random Forest ◽  
Upper Limb ◽  
Daily Living ◽  
Machine Learning Algorithms ◽  
Data Sets ◽  
Random Forest Regression ◽  
Prosthetic Devices ◽  
Upper Limb Function ◽  
The Neural Network

AbstractProsthetic arms can significantly increase the upper limb function of individuals with upper limb loss, however despite the development of various multi-DoF prosthetic arms the rate of prosthesis abandonment is still high. One of the major challenges is to design a multi-DoF controller that has high precision, robustness, and intuitiveness for daily use. The present study demonstrates a novel framework for developing a controller leveraging machine learning algorithms and movement synergies to implement natural control of a 2-DoF prosthetic wrist for activities of daily living (ADL). The data was collected during ADL tasks of ten individuals with a wrist brace emulating the absence of wrist function. Using this data, the neural network classifies the movement and then random forest regression computes the desired velocity of the prosthetic wrist. The models were trained/tested with ADLs where their robustness was tested using cross-validation and holdout data sets. The proposed framework demonstrated high accuracy (F-1 score of 99% for the classifier and Pearson’s correlation of 0.98 for the regression). Additionally, the interpretable nature of random forest regression was used to verify the targeted movement synergies. The present work provides a novel and effective framework to develop an intuitive control for multi-DoF prosthetic devices.

POSTPARTUM AMENORRHOEA IN RURAL EASTERN UTTAR PRADESH, INDIA

1998 ◽  
Vol 30 (2) ◽  
pp. 227-243
Author(s):  
K. N. S. YADAVA ◽  
S. K. JAIN
Keyword(s):  
Higher Education ◽  
Uttar Pradesh ◽  
Current Status Data ◽  
North India ◽  
Current Status ◽  
Data Sets ◽  
Survival Status ◽  
The Mean ◽  
The Difference ◽  
Using Data

This paper calculates the mean duration of the postpartum amenorrhoea (PPA) and examines its demographic, and socioeconomic correlates in rural north India, using data collected through 'retrospective' (last but one child) as well as 'current status' (last child) reporting of the duration of PPA.The mean duration of PPA was higher in the current status than in the retrospective data;n the difference being statistically significant. However, for the same mothers who gave PPA information in both the data sets, the difference in mean duration of PPA was not statistically significant. The correlates were identical in both the data sets. The current status data were more complete in terms of the coverage, and perhaps less distorted by reporting errors caused by recall lapse.A positive relationship of the mean duration of PPA was found with longer breast-feeding, higher parity and age of mother at the birth of the child, and the survival status of the child. An inverse relationship was found with higher education of a woman, higher education of her husband and higher socioeconomic status of her household, these variables possibly acting as proxies for women's better nutritional status.

scholarly journals Diurnal Variation of the Diffuse Attenuation Coefficient for Downwelling Irradiance at 490 nm in Coastal East China Sea

2021 ◽  
Vol 13 (9) ◽  
pp. 1676
Author(s):  
Yu Zhang ◽  
Zhantang Xu ◽  
Yuezhong Yang ◽  
Guifen Wang ◽  
Wen Zhou ◽  
...  
Keyword(s):  
Diurnal Variation ◽  
Attenuation Coefficient ◽  
East China Sea ◽  
East China ◽  
Coefficient Of Determination ◽  
Target Area ◽  
Diffuse Attenuation Coefficient ◽  
China Sea ◽  
Mean Square Errors ◽  
Downwelling Irradiance

The diurnal variation of the diffuse attenuation coefficient for downwelling irradiance at 490 nm (Kd(490)) has complex characteristics in the coastal regions. However, owing to the scarcity of in situ data, our knowledge on the diurnal variation is inadequate. In this study, an optical-buoy dataset was used to investigate the diurnal variation of Kd(490) in the coastal East China Sea, and to evaluate the Kd(490) L2 products of geostationary ocean color imager (GOCI), as well as the performance of six empirical algorithms for Kd(490) estimation in the Case-2 water. The results of validation show that there was high uncertainty in GOCI L2 Kd(490), with mean absolute percentage errors (MAPEs) of 69.57% and 68.86% and root mean square errors (RMSEs) of 0.70 and 0.71 m−1 compared to buoy-measured Kd12(490) and Kd13(490), respectively. Meanwhile, with the coefficient of determination (R2) of 0.71, as well as the lowest MAPE of 27.31% and RMSE of 0.29 m−1, the new dual ratio algorithm (NDRA) performed the best in estimating Kd(490) in the target area, among the six algorithms. Further, four main types of Kd(490) diurnal variation were found from buoy data, showing different variabilities compared to the area closer to the shore. One typical diurnal variation pattern showed that Kd(490) decreased at flood tide and increased at ebb tide, which was confirmed by GOCI images through the use of NDRA. Hydrometeorological factors influencing the diurnal variations of Kd(490) were also studied. In addition to verifying the predominant impact of tide, we found that the dominant effect of tide and wind on the water column is intensifying sediment resuspension, and the change of sediment transport produced by them are secondary to it.

Intercomparison of Cloud Products based on S5P/TROPOMI Level 1b Data Version 1 and the updated Version 2

2021 ◽  
Author(s):  
Miriam Latsch ◽  
Andreas Richter ◽  
John P. Burrows ◽  
Thomas Wagner ◽  
Holger Sihler ◽  
...  
Keyword(s):  
Infrared Imaging ◽  
Recognition Algorithm ◽  
Cloud Fraction ◽  
Trace Gas ◽  
Oxygen Absorption ◽  
Data Sets ◽  
Absorption Bands ◽  
Data Set ◽  
Trace Species ◽  
Retrieval Scheme

<p>The first European Sentinel satellite for monitoring the composition of the Earth’s atmosphere, the Sentinel 5 Precursor (S5p), carries the TROPOspheric Monitoring Instrument (TROPOMI) to map trace species of the global atmosphere at high spatial resolution. Retrievals of tropospheric trace gas columns from satellite measurements are strongly influenced by clouds. Thus, cloud retrieval algorithms were developed and implemented in the trace gas processing chain to consider this impact.</p><p>In this study, different cloud products available for NO<sub>2</sub> retrievals based on the TROPOMI level 1b data version 1 and an updated TROPOMI level 1b test data set of version 2 (Diagnostic Data Set 2B, DDS2B) are analyzed. The data sets include a) the TROPOMI level 2 OCRA/ROCINN (Optical Cloud Recognition Algorithm/Retrieval of Cloud Information using Neural Networks) cloud products CRB (cloud as reflecting boundaries) and CAL (clouds as layers), b) the FRESCO (Fast Retrieval Scheme for Clouds from Oxygen absorption bands) cloud product,  c) the cloud fraction from the NO<sub>2</sub> fitting window, d) the VIIRS (Visible Infrared Imaging Radiometer Suite) cloud product, and e) the MICRU (Mainz Iterative Cloud Retrieval Utilities) cloud fraction. The cloud products are compared with regard to cloud fraction, cloud height, cloud albedo/optical thickness, flagging and quality indicators in all 4 seasons. In particular, the differences of the cloud products under difficult situations such as snow or ice cover and sun glint are investigated.</p><p>We present results of a statistical analysis on a limited data set comparing cloud products from the current and the upcoming lv2 data versions and their approaches. The aim of this study is to better understand TROPOMI cloud products and their quantitative impacts on trace gas retrievals.</p>

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