Hyperspectral remote sensing of phytoplankton assemblages in the ocean: Effects of the vertical distribution

2010 ◽  
Author(s):  
E. Torrecilla ◽  
J. Piera ◽  
I. F. Aymerich ◽  
S. Pons ◽  
O. N. Ross ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Vertical Distribution ◽  
Hyperspectral Remote Sensing ◽  
Phytoplankton Assemblages ◽  
The Vertical Distribution

scholarly journals Scratching Beneath the Surface: A Model to Predict the Vertical Distribution of Prochlorococcus Using Remote Sensing

2018 ◽  
Vol 10 (6) ◽  
pp. 847 ◽  
Author(s):  
Priscila Lange ◽  
Robert Brewin ◽  
Giorgio Dall’Olmo ◽  
Glen Tarran ◽  
Shubha Sathyendranath ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Vertical Distribution ◽  
The Vertical Distribution

Is aerosol optical depth a good metric to map dust properties? Lessons learned from AER-D

2020 ◽  
Author(s):  
Franco Marenco ◽  
Claire Ryder ◽  
Victor Estelles ◽  
Debbie O'Sullivan
Keyword(s):  
Remote Sensing ◽  
Particle Size ◽  
Vertical Distribution ◽  
Aerosol Optical Depth ◽  
Optical Depth ◽  
Field Experiments ◽  
Saharan Air Layer ◽  
Validation Experiment ◽  
Operational Models ◽  
The Vertical Distribution

<p>The main observable quantity used on a global scale to map aerosols is aerosol optical depth (AOD), from ground-based and satellite remote sensing. It is at the same time an optical property and a vertically integrated quantity, and it is commonly used as the main metric towards which to pull aerosol models, through data assimilation, verification, and tuning. Here we introduce a few reflections on how to better constrain our knowledge of the Saharan Air Layer and its associated mineral dust, following results from the AER-D campaign.</p><p>AER-D was a small field experiment in the Eastern Atlantic during August 2015, based on the opportunity given by the simultaneous ICE-D experiment. The purpose of AER-D was to investigate the physical properties of the Saharan Air Layer, and to assess and validate remote sensing and modelling products. The FAAM atmospheric research aircraft was used as a flying laboratory, and it carried a full set of instruments aimed at both in-situ sampling and remote sensing.</p><p>A broad distribution of particle sizes was consistently observed, with a significant giant mode up to 80 µm, generally larger than what was observed in previous experiments: we ascribe this to the set of instruments used, able to capture the full spectrum. We will discuss the representation of the particle size in operational models, and we will show that despite predicting an extinction coefficient of the correct order of magnitude, the particle size is generally underestimated. We will also discuss the implication of the giant particles for the ground-based remote sensing of columnar size-distributions from the SKYNET and AERONET networks (Sunphotometer Airborne Validation Experiment, which was a component of AER-D).</p><p>We will present the vertical structure of the Saharan Air Layer, and in particular one episode when the structure was very different than the one generally accepted in the conceptual model. Moreover, the comparison with the operational models showed that they can predict a correct aerosol optical depth (AOD, a vertically integrated quantity) despite missing the vertical distribution.</p><p>These findings lead to a series of reflections on how to better constrain our knowledge of the Saharan Air Layer and its representation in operational models. Size-resolved properties and the vertical distribution are essential companions of the global AOD observations commonly used operationally. We will also discuss objectives and ideas for future field experiments.</p>

scholarly journals Evaluation of Weighting Average Functions as a Simplification of the Radiative Transfer Simulation in Vertically Inhomogeneous Eutrophic Waters

2019 ◽  
Vol 9 (8) ◽  
pp. 1635 ◽  
Author(s):  
Kun Xue ◽  
Ronghua Ma
Keyword(s):  
Remote Sensing ◽  
Optical Properties ◽  
Radiative Transfer ◽  
Vertical Distribution ◽  
Attenuation Coefficient ◽  
Spectral Variation ◽  
Diffuse Attenuation Coefficient ◽  
Remote Sensing Reflectance ◽  
Vertical Variations ◽  
The Vertical Distribution

Current water color remote sensing algorithms typically do not consider the vertical variations of phytoplankton. Ecolight with a radiative transfer program was used to simulate the underwater light field of vertical inhomogeneous waters based on the optical properties of a eutrophic lake (i.e., Lake Chaohu, China). Results showed that the vertical distribution of chlorophyll-a (Chla(z)) can considerably affect spectrum shape and magnitude of apparent optical properties (AOPs), including subsurface remote sensing reflectance in water (rrs(λ, z)) and the diffuse attenuation coefficient (Kx(λ, z)). The vertical variations of Chla(z) changed the spectrum shapes of rrs(λ, z) at the green and red wavelengths with a maximum value at approximately 590 nm, and changed the Kx(λ, z) from blue to red wavelength range with no obvious spectral variation. The difference between rrs(λ, z) at depth z m and its asymptotic value (Δrrs(λ, z)) could reach to ~78% in highly stratified waters. Diffuse attenuation coefficient of downwelling plane irradiance (Kd(λ, z)) had larger vertical variations, especially near water surface, in highly stratified waters. Three weighting average functions performed well in less stratified waters, and the weighting average function proposed by Zaneveld et al., (2005) performed best in highly stratified waters. The total contribution of the first three layers to rrs(λ, 0−) was approximately 90%, but the contribution of each layer in the water column to rrs(λ, 0−) varied with wavelength, vertical distribution of Chla(z) profiles, concentration of suspended particulate inorganic matter (SPIM), and colored dissolved organic matter (CDOM). A simple stratified remote sensing reflectance model considering the vertical distribution of phytoplankton was built based on the contribution of each layer to rrs(λ, 0−).

scholarly journals Using Multi-Angular Hyperspectral Data to Estimate the Vertical Distribution of Leaf Chlorophyll Content in Wheat

2021 ◽  
Vol 13 (8) ◽  
pp. 1501
Author(s):  
Bin Wu ◽  
Wenjiang Huang ◽  
Huichun Ye ◽  
Peilei Luo ◽  
Yu Ren ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Vertical Distribution ◽  
Chlorophyll Content ◽  
Spectral Response ◽  
Middle Layer ◽  
Bottom Layer ◽  
Estimation Accuracy ◽  
Leaf Chlorophyll Content ◽  
Leaf Chlorophyll ◽  
The Vertical Distribution

Heterogeneity exists in the vertical distribution of the biochemical components of crops. A leaf chlorophyll deficiency occurs in the bottom- and middle-layers of crops due to nitrogen stress and leaf senescence. Some studies used multi-angular remote sensing data for estimating the vertical distribution of the leaf chlorophyll content (LCC). However, these studies performed LCC inversion of different vertical layers using a fixed view zenith angle (VZA), but rarely considered the contribution of the components of the non-target layers to the spectral response. The main goal of this work was to determine the LCC of different vertical layers of the canopy of winter wheat (Triticum aestivum L.), using multi-angular remote sensing and spectral vegetation indices. Different combinations of VZAs were used for obtaining the LCC of different layers. The results revealed that the responses of the transformed chlorophyll in reflectance absorption index (TCARI) and modified chlorophyll absorption in reflectance index (MCARI)/optimized soil-adjusted vegetation index (OSAVI) to the upper-layer LCC were strongest at VZA 10°. For the middle-layer LCC, the response was strongest at 30°, but the response was significantly lower than that of the upper-layer. For the bottom-layer LCC, the responses were weak due to the obscuring effect of the upper- and middle-layer; thus, the LCC inversion of the bottom-layer data was not optimal for a single VZA. The optimal VZA or VZA combinations for LCC estimation were VZA 10° for the upper-layer LCC (TCARI with coefficient of determination (R2) = 0.69, root mean square error (RMSE) = 4.80 ug/cm2, MCARI/OSAVI with R2 = 0.73, RMSE = 4.17 ug/cm2), VZA 10° and 30° for the middle-layer LCC (TCARI with R2 = 0.17, RMSE = 4.81 ug/cm2, MCARI/OSAVI with R2 = 0.17, RMSE = 4.76 ug/cm2), and VZA 10°, 30°, and 50° for the bottom-layer LCC (TCARI with R2 = 0.40, RMSE = 6.29 ug/cm2, MCARI/OSAVI with R2 = 0.40, RMSE = 6.36 ug/cm2). The proposed observation strategy provided a significantly higher estimation accuracy of the target layer LCC than the single VZA approach, and demonstrated the ability of canopy multi-angular spectral reflectance to accurately estimate the wheat canopy chlorophyll content vertical distribution.

scholarly journals Monitoring the Vertical Distribution of Maize Canopy Chlorophyll Content Based on Multi-Angular Spectral Data

2021 ◽  
Vol 13 (5) ◽  
pp. 987
Author(s):  
Bin Wu ◽  
Huichun Ye ◽  
Wenjiang Huang ◽  
Hongye Wang ◽  
Peilei Luo ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Vertical Distribution ◽  
Chlorophyll Content ◽  
Spectral Data ◽  
Growth Stage ◽  
Growth Stages ◽  
Growth Monitoring ◽  
Spad Value ◽  
The Vertical Distribution ◽  
Late Growth

Remote sensing approaches have several advantages over traditional methods in determining information on physical and chemical parameters, including timely data acquisition, low costs, and wide coverage. Thus, remote sensing is widely used in crop growth monitoring. Unlike vertical observations, multi-angular remote sensing technology can obtain the vertical distribution information of the central and lower leaves of a crop. Furthermore, applications of remote sensing on the vertical distribution of maize canopy components is complicated, and related research is limited. In the current paper, we employed multi-angular spectral data, measured by a self-designed multi-angular observation instrument at view zenith angles (VZAs) of 0°, 10°, 20°, 30°, 40°, 50°, and 60°, to explore the monitoring strategy and monitoring precision of the vertical distribution of chlorophyll content in the maize canopy. This was then used to determine the optimal monitoring method for the chlorophyll content (soil and plant analyzer development (SPAD) value) of each layer. The correlation between SPAD value and chlorophyll sensitivity indices at different growth stages was used as the basis for screening indices and VZAs. The correlation between the selected EPI (eucalyptus pigment index) and REIP (red edge inflection point) indices and chlorophyll content indicated view zenith angles (VZAs) of 0°, 30°, and 40° as optimal for the early growth stage monitoring of chlorophyll content in the 1st, 2nd, and 3rd layers, respectively. These values were associated with RMSEs of 4.14, 1.71, and 1.11 for EPI, respectively; and 4.61, 2.31, and 1.00 for REIP, respectively. In addition, a VZA of 50° was selected to monitor the chlorophyll content of the 1st, 2nd, 3rd, and 4th layers at the late growth stage, with RMSE values of 2.97, 3.50, 2.80, and 4.80 for EPI, respectively; and 3.16, 5.02, 4.55, and 7.85 for REIP, respectively. The results demonstrated the ability of canopy multi-angular spectral reflectance to accurately estimate the maize canopy chlorophyll content vertical distribution, with the VZAs of different vertical layers varying between the early and late growth stages.

scholarly journals Changes and Predictions of Vertical Distributions of Global Light-Absorbing Aerosols Based on CALIPSO Observation

2020 ◽  
Vol 12 (18) ◽  
pp. 3014
Author(s):  
Zigeng Song ◽  
Xianqiang He ◽  
Yan Bai ◽  
Difeng Wang ◽  
Zengzhou Hao ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Vertical Distribution ◽  
Satellite Remote Sensing ◽  
Radiative Forcing ◽  
Prediction Models ◽  
Atmospheric Correction ◽  
Satellite Measurement ◽  
Vertical Distributions ◽  
Absorbing Aerosols ◽  
The Vertical Distribution

Knowledge of the vertical distribution of absorbing aerosols is crucial for radiative forcing assessment, and its quasi real-time prediction is one of the keys for the atmospheric correction of satellite remote sensing. In this study, we investigated the seasonal and interannual changes of the vertical distribution of global absorbing aerosols based on satellite measurement from the Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observations (CALIPSO) and proposed a neural network (NN) model to predict the vertical distribution of global absorbing aerosols. Gaussian fitting was proposed to derive the maximum fitted particle number concentration (MFNC), altitude corresponding to MFNC (MFA), and standard deviation (MFASD) for vertical distribution of dust and smoke aerosols. Results showed that higher MFA values of dust and smoke aerosols mainly occurred over deserts and tropical savannas, respectively. For dust aerosol, the MFA is mainly observed at 0.5 to 6 km above deserts, and low MFNC values occur in boreal spring and winter while high values in summer and autumn. The MFA of smoke is systematically lower than that of dust, ranging from 0.5 to 3.5 km over tropical rainforest and grassland. Moreover, we found that the MFA of global dust and smoke had decreased by 2.7 m yr−1 (statistical significance p = 0.02) and 1.7 m yr−1 (p = 0.02) over 2007–2016, respectively. The MFNC of global dust has increased by 0.63 cm−3 yr−1 (p = 0.05), whereas that of smoke has decreased by 0.12 cm−3 yr−1 (p = 0.05). In addition, the determination coefficient (R2) of the established prediction models for vertical distributions of absorbing aerosols were larger than 0.76 with root mean square error (RMSE) less than 1.42 cm−3, which should be helpful for the radiative forcing evaluation and atmospheric correction of satellite remote sensing.

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