A hybrid algorithm for estimating the chlorophyll-a
concentration across different trophic states in Asian inland waters
Optical water type discrimination and tuning remote sensing
band-ratio algorithms: application to retrieval of chlorophyll and kd
(490) in the Irish and Celtic seas
Influence of a red band-based water classification approach on
chlorophyll algorithms for optically complex estuaries
A soft-classification-based chlorophyll-a estimation method using
MERIS data in the highly turbid and eutrophic Taihu Lake
A system to measure the data quality of spectral remote-sensing
reflectance of aquatic environments
An improved optical classification scheme for the Ocean Colour
Essential Climate Variable and its applications
A
system to measure the data quality of spectral remote-sensing
reflectance of aquatic environments
Water Classification
The reference Rrs spectra were first normalized by their respective
root of sum of squares (RSS), $$
nR_{rs}(\lambda)=\frac{R_{rs}}{[\sum^N_{i=1}R_{rs}(\lambda_i)^2]^{1/2}}
$$ where the index N represents the total number of wavelengths,
varying from 1 to 9 and λi corresponds
to the wavelengths of 412, 443, 488, 510, 531, 547, 555, 667, and 678
nm. The nRrs spectra vary over the range between 0 and 1, while it
retains the ‘‘shapes’’ pertaining to the original Rrs spectra, i.e., the
band ratios of nRrs(λi) remain the
same as Rrs(λi).
The number of data clusters k was evaluated using the gap method
[Tibshirani et al., 2001]. The gap value is defined as: GAPn(k) = En*[log(Wk)] − log(Wk)
where n is the sample size, k is the number of clusters being evaluated,
and Wk is the pooled within-cluster dispersion measurement, with $$
W_k=\sum_{r=1}^k\frac{1}{2n_r}D_r
$$ where nr is the number of data points in cluster r, and Dr is the sum of
the pair-wise distances for all points in cluster r.
The expected value En*[log(Wk)]
is determined by Monte Carlo sampling from a reference distri- bution,
and $log(Wk) is computed from the sample data. According to the gap
method, the optimum cluster number of the nRrs data is determined as 23.
Interestingly, this number is nearly the same as that of Forel- Ule
water type classes developed 100 years ago [Arnone et al., 2004].
The unsupervised method, K-means clustering technique, was further
used to group the nRrs spectra.
But what i want to do is just”Case1, Case2, and Case2 into slightly
turbid, moderet turbid and highly turbid”
Anyway, I’m gonna to read it over as I also need it to do the
verification/validation.
Quantitatively measurement
match up Rrs*(λ′)
with nRrs(λ)
with regard to the wavelengths. If Rrs*
has more spectral bands than that of nRrs(λ),
we will only choose the same wavelengths with nRrs(λ)
for further analysis. If Rrs*
has fewer wavelengths than the nRrs(λ)
spectra (i.e., N(λ′) < 9), a
subset of nRrs(λ′)
and associated upper boundary spectra nRrsU(λ′)
and lower boundary spectra nRrsL(λ′)
will be extrated first for λ′.
the normalization of Rrs*
spectra following equation (1). For the case of N(λ′) < 9,
the new nRrs(λ′)
specta will be rescaled through the normalization procedure of eq(1) so
that the RSS of nRrs(λ′)
is equal to 1 (?). Further, the new upper and lower
boundary spectra nRrsU(λ′)
and nRrsL(λ′)
will also be rescaled by the newly rescaled nRrs(λ′)
spectra as below $$
nR_{rs}^{U}(\lambda)=\frac{nR_{rs}^{U}(\lambda)}{[\sum_{i=1}^NnR_{rs}(\lambda_i)^2]^{1/2}}
$$
assign a water type to the target spectrum by comparing it with
the reference nRrs spectra. The spectra similarity between the target
spectrum nRrs*
and refference spectra nRrs are estimated using a spectral angle
mapper(SAM)[Kruse et al., 1993], $$
cos\
\alpha=\frac{\sum_{i=1}^{N}[nR_{rs}^**nR_{rs}]}{\sqrt{\sum_{i=1}^{N}[nR_{rs}^{*}(\lambda_i)]^2\sum_{i=1}^{N}[nR_{rs}(\lambda_i)]^2}}
$$ Where α is the angle
formed between the refference spectrum nRrs and the normalized target
spectrum nRrs*.
As a spectral classifier, SAM is able to determine the spectral
similarity by treating them as vertors in a space with dimensionality
equal to the number of bands, N. The water type of the target spectrum
nRrs*
is identified as one with the largest cosine values (equivalent to the
smllest angles).
the computation of QA scores by comparing the target spectrum
nRrs with
the upper and lower boundaries (nRrsU
and nRrsL)
of the corresponding water type. The number of wavelengths where nRrs*
falling within the boundaries is counted, and used to derive the total
score (Ctot)
for the nRrs
spectrum, $$
C_{tot}=\frac{C(\lambda_1)+C(\lambda_2)+\ldots+C(\lambda_N)}{N}
$$
Where Ci is the
wavelength-specific score with N the total number of wavelengths for
both Rrs*
and Rrsref.
At wavelength λi , for
example, if Rrs*(λi)
is found beyond either the upper or lower boundary of nRrs, a score of 0
will be assigned to this wavelength; otherwise score=1. As suggested by
equation (7), the total score Ctot
will vary within the range of [0, 1]. A higher score indicates higher
data quality.
To account for the measurement uncertainty and possible
data-processing errors and likely insufficient data coverage, the
original upper boundary and lower boundary are slightly modified by
± 0.5,nRrsU = nRrsU × (1+0.005) andnRrsL = nRrsL × (1−0.005),
respectively. Note that this added range of 0.5% is one order of
magnitude smaller than the projected accuracy for radiance measurement
[Hooker et al., 1992].
image-00030115182324282
Result
Because all field measurements are discrete, it is likely that the
database used here does not cover every water types and/or there are
situations where the range of Rrs variability goes beyond the domains
defined here. Such a limitation can be updated or revised when more
high-quality in situ measurements are avail- able. A MATLABVR script is
made available (http://oceanoptics.umb.edu/score_metric/) to facilitate
the evaluation and refinement of the score metrics. Nevertheless, this
QA scheme provides an easily applicable system to quantitatively
evaluate the quality of individual Rrs spectra.
function[maxCos, cos, clusterID, totScore] = QAscores_matrix(test_Rrs, test_lambda) % Quality assurance system for Rrs spectra (version 1) % % Author: Jianwei Wei, University of Massachusetts Boston % Email: Jianwei.Wei@umb.edu % Nov-01-2016 % % ------------------------------------------------------------------------------ % KNOWN VARIABLES : ref_nRrs -- Normalized Rrs spectra per-determined from water clustering (23x9 matrix) % ref_lambda -- Wavelengths for ref_nRrs (1x9 matrix) % upB -- Upper boundary (23x9 matrix) % lowB -- Lower boundary (23x9 matrix) % % INPUTS: test_Rrs -- Rrs spectra for testing (units: sr^-1); % a row vector % test_lambda-- Wavelengths for test_Rrs % % OUTPUTS: maxCos -- maximum cosine values % cos -- cosine values for every ref_nRrs spectra % clusterID -- idenfification of water types (from 1-23) % totScore -- total score assigned to test_Rrs % ------------------------------------------------------------------------------ % % NOTE: % 1) Nine wavelengths (412, 443, 488, 510, 531, 547, 555, 667, 678nm) are assumed in the model % 2) If your Rrs data were measured at other wavelength, e.g. 440nm, you may want to change 440 to 443 before the model run; % or modify the code below to find a cloest wavelength from the nine bands. % 3) The latest version may be found online at HTTP://oceanoptics.umb.edu/score_metric % % Reference: % Wei, Jianwei; Lee, Zhongping; Shang, Shaoling (2016). A system % to measure the data quality of spectral remote sensing % reflectance of aquatic environments. Journal of Geophysical Research, % 121, doi:10.1002/2016JC012126 % % ------------------------------------------------------------------------------ % Apr. 5 2017, Keping Du % 1) Vectorize code % 2) totScore takes account of NaN bands % 3) add input data check % Apr. 11, 2017 % 4) compatible with previous matlab version (tested on v2014a) % % INPUTS: % test_Rrs -- matrix (inRow*inCol), each row represents one Rrs spectrum % OUTPUTS: % maxCos,clusterID,totScore -- row vector (1*inRow) % cos -- matrix (refRow[23]*inRow) % % Note: % 1) nanmean, nansum need statistics toolbox % 2) on less memory and multi-core system, it may further speedup using % parfor % % ------------------------------------------------------------------------------ %% check input data [row_lam, len] = size(test_lambda); if( row_lam ~= 1 ) test_lambda = test_lambda'; [row_lam, len] = size(test_lambda); end
[row, col] = size(test_Rrs); if( len~=col && len~=row) error('Rrs and lambda size mismatch, please check the input data!'); elseif( len == row ) test_Rrs = test_Rrs'; end
I believe this is actually not the true evaluation that do not need
any ‘in-situ’ data.
It should met a lot of problem when applied it to other regions and
sensors.
I need to modify the former code if I want to use it in the
evaluation of fusion result.
A
hybrid algorithm for estimating the chlorophyll-a concentration
across different trophic states in Asian inland waters
image-00030115201801830
MCI ≤ 0.001
Slightly turbid
0.001 < MCI ≤ 0.0016
Moderate turbid
MCI > 0.0016
Highly turbid
Optical
water type discrimination and tuning remote sensing band-ratio
algorithms: Application to retrieval of chlorophyll and Kd(490)
in the Irish and Celtic Seas
image-00030115202628690
This is a very very emprical classification.
Influence
of a red band-based water classification approach on chlorophyll
algorithms for optically complex estuarie
image-00030115202825460
I can try this. Along with the Chia/TSM ratio
A
soft-classification-based chlorophyll-a estimation method using
MERIS data in the highly turbid and eutrophic Taihu Lake
SGLI didn’t have red edge wavelength, which introduced a lot of
problem in this work.
Phytoplankton
pigments cause high reflectance in red edge wavelengths (e.g.
709 nm), and low reflectance in red wavelengths (e.g. 681 nm)
because of the phytoplankton pigment absorption peak around 681 nm.
An
improved optical classification scheme for the Ocean Colour Essential
Climate Variable and its applications
An absorption-related optical classification approach was developed
by considering the contributions of different particle sources,
aph(443)/ ad(443) (Table 3), to divide the study sites into three water
classes, i.e., detritus-dominated waters (aph(443)/ad(443) < 0.2,
Wd), pigment- dominated waters (aph(443)/ad(443) ≥ 1.0, Wp) and
intermediate waters (0.2 ≤ aph(443)/ad(443) < 1.0, Wm),
自己可以尝试一下。
Remotely
estimating total suspended solids concentration in clear to extremely
turbid waters using a novel semi-analytical method
