Geometrical light field parameters for improving remote sensing estimates of the backscattering coefficient for the marine hydrosol

1994 ◽  
Author(s):  
Robert H. Stavn ◽  
Alan D. Weidemann
Keyword(s):  
Remote Sensing ◽  
Light Field ◽  
Backscattering Coefficient

The Light Field And The Scope Of Light Science

2021 ◽  
pp. 4-10
Author(s):  
Vladimir P. Budak ◽  
Julian B. Aizenberg
Keyword(s):  
Remote Sensing ◽  
Plant Growth ◽  
Electricity Generation ◽  
Light Field ◽  
Theoretical Foundation ◽  
Theoretical Base ◽  
Technical Application ◽  
Single Section ◽  
Lighting Systems ◽  
Effective Development

For centuries, light has been produced to enable the human visual system to operate but, today, light is being used for an increasing number of non-visual, technical purposes. Examples include plant growth, remote sensing, electricity generation, and communication. This paper discusses the theoretical foundation of such technical applications of light and shows that it is completely identical to the fundamentals of lighting. The foundation is the theory of the light field, which is generated by the interaction of the radiation field with a quadratic (energy) receiver. Within its range of applicability, the theory of the light field is complete and closed. As a result, the light field provides a unified theoretical base for lighting systems and the technical application of light. This creates the basis for combining them into a single section of science and technology, which will ensure their effective development and application.

scholarly journals Radarsat backscattering from an agricultural scene

2001 ◽  
Vol 36 (5) ◽  
pp. 823-830 ◽  
Author(s):  
Antonio Roberto Formaggio ◽  
José Carlos Neves Epiphanio ◽  
Maurício dos Santos Simões
Keyword(s):  
Remote Sensing ◽  
Spatial Resolution ◽  
High Spatial Resolution ◽  
Agricultural Area ◽  
Backscattering Coefficient ◽  
Dispersion Diagram ◽  
Satellite Systems ◽  
Almost All ◽  
Different Cultures ◽  
Bare Soils

Orbital remote sensing in the microwave electromagnetic region has been presented as an important tool for agriculture monitoring. The satellite systems in operation have almost all-weather capability and high spatial resolution, which are features appropriated for agriculture. However, for full exploration of these data, an understanding of the relationships between the characteristics of each system and agricultural targets is necessary. This paper describes the behavior of backscattering coefficient (sigma°) derived from calibrated data of Radarsat images from an agricultural area. It is shown that in a dispersion diagram of sigma° there are three main regions in which most of the fields can be classified. The first one is characterized by low backscattering values, with pastures and bare soils; the second one has intermediate backscattering coefficients and comprises well grown crops mainly; and a third one, with high backscattering coefficients, in which there are fields with strong structures causing a kind of double bounce effect. The results of this research indicate that the use of Radarsat images is optimized when a multitemporal analysis is done making the best use of the agricultural calendar and of the dynamics of different cultures.

scholarly journals Expanding understanding of optical variability in Lake Superior with a four-year dataset

2017 ◽  
Author(s):  
Colleen B. Mouw ◽  
Audrey B. Ciochetto ◽  
Brice Grunert ◽  
Angela Yu
Keyword(s):  
Remote Sensing ◽  
Light Field ◽  
Lake Superior ◽  
Chlorophyll Concentration ◽  
Optical Observations ◽  
Colored Dissolved Organic Matter ◽  
Suspended Particulate ◽  
Variable Light ◽  
Biogeochemical Parameters ◽  
Development And Validation

Abstract. Lake Superior is one of the largest freshwater lakes on our planet, but few optical observations have been made to allow for development and validation of visible spectral satellite remote sensing products. The dataset described here focuses on coincidently observing inherent and apparent optical properties along with biogeochemical parameters. Specifically, we observe remote sensing reflectance, absorption, scattering, backscattering, attenuation, chlorophyll concentration, and suspended particulate matter over the ice-free months of 2013–2016. The dataset substantially increases the optical knowledge of the lake. In addition to visible spectral satellite algorithm development, the dataset is valuable for characterizing the variable light field, particle, phytoplankton, and colored dissolved organic matter distributions, and helpful in food web and carbon cycle investigations, among others. The compiled data can be freely accessed at: https://seabass.gsfc.nasa.gov/archive/URI/Mouw/LakeSuperior/.

scholarly journals An Improved Backscattering Theoretical Model for Snow Area

2021 ◽  
Vol 3 (2) ◽  
pp. 7-13
Author(s):  
Dina Naqiba Nur Ezzaty Abd Wahid ◽  
Syabeela Syahali ◽  
Muhamad Jalaluddin Jamri
Keyword(s):  
Remote Sensing ◽  
Close Agreement ◽  
Order Term ◽  
Backscattering Coefficient ◽  
Snow Layer ◽  
Remote Sensing Technology ◽  
The Earth ◽  
Sensing Technology ◽  
Data Field ◽  
Long Time

Remote sensing has been studied for a long time to monitor the earth terrain. Remote sensing technology has been used globally in many different fields and one of the most popular area of study that uses remote sensing technology is snow monitoring. In previous researches, remote sensing has been modelled on snow area to study the scattering mechanisms of various scattering processes. In this paper, surface volume second order term that was dropped in previous study is derived, included and studied to observe the improvement in the surface volume backscattering coefficient. This new model is applied on snow layer above ground and the snow layer is modelled as a volume of ice particles as the Mie scatterers that are closely packed and bounded by irregular boundaries. Various parameters are used to investigate the improvement of adding the new term. Results show improvement in cross-polarized return, for all the range of parameters studied. Comparison is made with the field measurement result from U.S. Army Cold Regions Research and Engineering Laboratory (CRREL) in 1990. Close agreement is shown between developed model and data field backscattering coefficient result.

Optical Effects of Large Particles

Ocean Optics ◽  
1994 ◽  
Author(s):  
Kendall L. Carder ◽  
David K. Costello
Keyword(s):  
Optical Properties ◽  
Absorption Coefficient ◽  
Process Model ◽  
Light Field ◽  
Ocean Optics ◽  
Backscattering Coefficient ◽  
Model Inversion ◽  
Inherent Optical Properties ◽  
And Inversion ◽  
The Relationship

Two important problems facing the ocean optics research community in the coming decade concern optical model closure and inversion (see Chapter 3). We obtain model closure if we can describe the measured light environment by combining elementary measurements of the optical properties of the medium with radiative transfer theory. If we can accurately deduce the concentration of various constituents from a combination of measures of the submarine light field and inverse model calculations, we term this process model inversion. The most elementary measurements of the optical properties of the sea are those that are independent of the geometry of the light field, the inherent optical properties (Preisendorfer, 1961). Optical properties that are dependent on the geometry of the light field are termed apparent optical properties (AOP). Models of the submarine light field typically relate apparent optical properties to inherent optical properties (see Chapter 2). Examples include the relationship between the AOP irradiance reflectance R and a combination of inherent optical properties (backscattering coefficient bb and absorption coefficient a), and the relationship between the AOP downwelling diffuse attenuation coefficient kd and a combination of the absorption coefficient, backscattering coefficient, and downwelling average cosine μd (e.g., Gordon et al., 1975; Morel and Prieur, 1977; Smith and Baker, 1981; Morel, 1988; Kirk, 1984a). Under some circumstances these relationships work well enough that the absorption coefficient can be derived indirectly. This is important since measurement of the absorption coefficient by direct means has been difficult. Derived values for the absorption coefficient by model inversion methods are not easily verified by independent measurements, however, because of the difficulty of measuring the absorption coefficient. Model closure and model inversion both become more tenuous when the following phenomena are present: 1. Transpectral or inelastic scattering such as fluorescence (e.g., Gordon, 1979; Carder and Steward, 1985; Mitchell and Kiefer, 1988a; Spitzer and Dirks, 1985; Hawes and Carder, 1990) or water Raman scattering (Marshall and Smith, 1990; Stavn, 1990; Stavn and Weidemann, 1988a,b; Peacock et al, 1990; Chapter 12 this volume). 2. Particles that are large relative to the measurement volume for inherent optical property meters such as beam transmissometers, light-scattering photometers, fluorometers, and absorption meters.

scholarly journals Enhanced 3D Point Cloud from a Light Field Image

2020 ◽  
Vol 12 (7) ◽  
pp. 1125 ◽  
Author(s):  
Helia Farhood ◽  
Stuart Perry ◽  
Eva Cheng ◽  
Juno Kim
Keyword(s):  
Remote Sensing ◽  
Urban Areas ◽  
Point Cloud ◽  
Light Field ◽  
Feature Matching ◽  
Depth Map ◽  
Point Clouds ◽  
Environmental Research ◽  
3D Point Cloud ◽  
3D Point Clouds

The importance of three-dimensional (3D) point cloud technologies in the field of agriculture environmental research has increased in recent years. Obtaining dense and accurate 3D reconstructions of plants and urban areas provide useful information for remote sensing. In this paper, we propose a novel strategy for the enhancement of 3D point clouds from a single 4D light field (LF) image. Using a light field camera in this way creates an easy way for obtaining 3D point clouds from one snapshot and enabling diversity in monitoring and modelling applications for remote sensing. Considering an LF image and associated depth map as an input, we first apply histogram equalization and histogram stretching to enhance the separation between depth planes. We then apply multi-modal edge detection by using feature matching and fuzzy logic from the central sub-aperture LF image and the depth map. These two steps of depth map enhancement are significant parts of our novelty for this work. After combing the two previous steps and transforming the point–plane correspondence, we can obtain the 3D point cloud. We tested our method with synthetic and real world image databases. To verify the accuracy of our method, we compared our results with two different state-of-the-art algorithms. The results showed that our method can reliably mitigate noise and had the highest level of detail compared to other existing methods.

scholarly journals Expanding understanding of optical variability in Lake Superior with a 4-year dataset

2017 ◽  
Vol 9 (2) ◽  
pp. 497-509 ◽  
Author(s):  
Colleen B. Mouw ◽  
Audrey B. Ciochetto ◽  
Brice Grunert ◽  
Angela Yu
Keyword(s):  
Remote Sensing ◽  
Light Field ◽  
Lake Superior ◽  
Chlorophyll Concentration ◽  
Optical Observations ◽  
Colored Dissolved Organic Matter ◽  
Suspended Particulate ◽  
Variable Light ◽  
Biogeochemical Parameters ◽  
Development And Validation

Abstract. Lake Superior is one of the largest freshwater lakes on our planet, but few optical observations have been made to allow for the development and validation of visible spectral satellite remote sensing products. The dataset described here focuses on coincidently observing inherent and apparent optical properties along with biogeochemical parameters. Specifically, we observe remote sensing reflectance, absorption, scattering, backscattering, attenuation, chlorophyll concentration, and suspended particulate matter over the ice-free months of 2013–2016. The dataset substantially increases the optical knowledge of the lake. In addition to visible spectral satellite algorithm development, the dataset is valuable for characterizing the variable light field, particle, phytoplankton, and colored dissolved organic matter distributions, and helpful in food web and carbon cycle investigations. The compiled data can be freely accessed at https://seabass.gsfc.nasa.gov/archive/URI/Mouw/LakeSuperior/.

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