CPBAC: A quick atmospheric correction method using the topographic information

Yu, Kai; Liu, Suhong; Zhao, Yongchao

doi:10.1016/j.rse.2016.08.010

Cited by 15 publications

(9 citation statements)

References 23 publications

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“…Satellite remote sensed data are easily influenced by water vapor, aerosol, bidirectional reflection, and data transmission, which will result in serious fluctuations of time series data and influence the desired effect in data analysis [50,51]. Therefore, this study applied the Fast line-of-Sight Atmospheric Analysis of Spectral Hypercubes (FLAASH) [52][53][54] to eliminate such atmospheric interference in each image. A digital elevation model (DEM) [55,56] was used to make terrain corrections, as terrain factors may affect the brightness values of original imagery.…”

Section: Processing Landsat Times Series Productsmentioning

confidence: 99%

Estimating Forest Aboveground Carbon Storage in Hang-Jia-Hu Using Landsat TM/OLI Data and Random Forest Model

Zhang

Zhou

et al. 2019

Forests

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Dynamic monitoring of carbon storage in forests resources is important for tracking ecosystem functionalities and climate change impacts. In this study, we used multi-year Landsat data combined with a Random Forest (RF) algorithm to estimate the forest aboveground carbon (AGC) in a forest area in China (Hang-Jia-Hu) and analyzed its spatiotemporal changes during the past two decades. Maximum likelihood classification was applied to make land-use maps. Remote sensing variables, such as the spectral band, vegetation indices, and derived texture features, were extracted from 20 Landsat TM and OLI images over five different years (2000, 2004, 2010, 2015, and 2018). These variables were subsequently selected according to their importance and subsequently used in the RF algorithm to build an estimation model of forest AGC. The results showed the following: (1) Verification of classification results showed maximum likelihood can extract land information effectively. Our land cover classification yielded overall accuracies between 86.86% and 89.47%. (2) Additionally, our RF models showed good performance in predicting forest AGC, with R2 from 0.65 to 0.73 in the training and testing phase and a RMSE range between 3.18 and 6.66 Mg/ha. RMSEr in the testing phase ranged from 20.27 to 22.27 with a low model error. (3) The estimation results indicated that forest AGC in the past two decades increased with density at 10.14 Mg/ha, 21.63 Mg/ha, 26.39 Mg/ha, 29.25 Mg/ha, and 44.59 Mg/ha in 2000, 2004, 2010, 2015, and 2018. The total forest AGC storage had a growth rate of 285%. (4) Our study showed that, although forest area decreased in the study area during the time period under study, the total forest AGC increased due to an increment in forest AGC density. However, such an effect is overridden in the vicinity of cities by intense urbanization and the loss of forest covers. Our study demonstrated that the combined use of remote sensing data and machine learning techniques can improve our ability to track the forest changes in support of regional natural resource management practices.

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Section: Processing Landsat Times Series Productsmentioning

confidence: 99%

Estimating Forest Aboveground Carbon Storage in Hang-Jia-Hu Using Landsat TM/OLI Data and Random Forest Model

Zhang

Zhou

et al. 2019

Forests

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“…All the methods can be classified basically into two categories, namely scene-based empirical method and the radiative transfer modeling approach [19,23]. The scene-based methods mainly include the flat field correction method [24], the dark object subtraction [25,26], the empirical line method [27], the internal average reflectance method [28], the cloud shadow method [29], the log residuals calibration [30], and the quick atmospheric correction [31].…”

Section: Introductionmentioning

confidence: 99%

“…The second category comprises atmospheric correction based on the theory of radiative transfer, which establishes the strict radiative transfer model about the radiation transmission process in the range of the visible to short-wave infrared band from the sun to the surface and then to the sensor, and it describes the condition of the radiation source, the atmosphere status, the relationship between the reflection characteristics of the ground objects, and the radiances recorded by the sensor. The atmospheric correction is then conducted on the image by using the measured, or standardized, atmospheric parameters [19,23]. Some typical methods of this category are 6S (Second Simulation of the Satellite Signal in the Solar Spectrum), FLAASH (Fast Line-of-sight Atmospheric Analysis Spectral Hypercubes), ATCOR (ATmospheric CORection), HATCH (High-accuracy Atmospheric Correction for Hyperspectral Data), and BRDF (Bidirectional Reflectance Distribution Function) [23].…”

Section: Introductionmentioning

confidence: 99%

“…The atmospheric correction is then conducted on the image by using the measured, or standardized, atmospheric parameters [19,23]. Some typical methods of this category are 6S (Second Simulation of the Satellite Signal in the Solar Spectrum), FLAASH (Fast Line-of-sight Atmospheric Analysis Spectral Hypercubes), ATCOR (ATmospheric CORection), HATCH (High-accuracy Atmospheric Correction for Hyperspectral Data), and BRDF (Bidirectional Reflectance Distribution Function) [23]. The accuracy of the radiative transfer modelling approach is very high in the case of known atmospheric data, but the application of this atmospheric correction algorithm is often limited by the real-time measurement of atmospheric parameters at the corresponding time of satellite transit [26,33].…”

Section: Introductionmentioning

confidence: 99%

“…The procedures of the above methods were complex and need too much time due to the human intervention in setting many configuration parameters, which greatly limit the applicability for large numbers of inland waters [23,32]. Gong et al proposed that the spatial variation of the gas content and aerosols optical thickness is obvious for a given atmospheric model or aerosols type in a large-scale area [33].…”

Section: Introductionmentioning

confidence: 99%

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Retrieving the Lake Trophic Level Index with Landsat-8 Image by Atmospheric Parameter and RBF: A Case Study of Lakes in Wuhan, China

Zhou

Xiao

et al. 2019

Remote Sensing

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The importance of atmospheric correction is pronounced for retrieving physical parameters in aquatic systems. To improve the retrieval accuracy of trophic level index (TLI), we built eight models with 43 samples in Wuhan and proposed an improved method by taking atmospheric water vapor (AWV) information and Landsat-8 (L8) remote sensing image into the input layer of radical basis function (RBF) neural network. All image information taken in RBF have been radiometrically calibrated. Except model(a), image data used in the other seven models were not atmospherically corrected. The eight models have different inputs and the same output (TLI). The models are as follows: (1) model(a), the inputs are seven single bands; (2) model(c), besides seven single bands (b1, b2, b3, b4, b5, b6, b7), we added the AWV parameter k1 to the inputs; (3) model(c1), the inputs are AWV difference coefficient k2 and the seven bands; (4) model(c2), the input layers include seven single bands, k1 and k2; (5) model(b), seven band ratios (b3/b5, b1/b2, b3/b7, b2/b5, b2/b7, b3/b6, and b3/b4) were used as input parameters; (6) model(b1), the inputs are k1 and seven band ratios; (7) model(b2), the inputs are k2 and seven band ratios; (8) model(b3), the inputs are k1, k2, and seven band ratios. We estimated models with root mean squared error (RMSE), model(a) > model(b3) > model(b1) > model(c2) > model(c) > model(b) > model(c1) > model(b2). RMSE of the eight models are 12.762, 11.274, 10.577, 8.904, 8.361, 6.396, 5.389, and 5.104, respectively. Model b2 and c1 are two best models in these experiments, which confirms both the seven single bands and band ratios with k2 are superior to other models. Results also corroborate that most lakes in Wuhan urban area are in mesotrophic and light eutrophic states.

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Habitat selection of wintering cranes in typical wetlands in the middle and lower reaches of the Yangtze River over the past 20 years, China

Gao

Liang

Zhu

et al. 2023

Environ Sci Pollut Res

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CPBAC: A quick atmospheric correction method using the topographic information

Cited by 15 publications

References 23 publications

Estimating Forest Aboveground Carbon Storage in Hang-Jia-Hu Using Landsat TM/OLI Data and Random Forest Model

Estimating Forest Aboveground Carbon Storage in Hang-Jia-Hu Using Landsat TM/OLI Data and Random Forest Model

Retrieving the Lake Trophic Level Index with Landsat-8 Image by Atmospheric Parameter and RBF: A Case Study of Lakes in Wuhan, China

Habitat selection of wintering cranes in typical wetlands in the middle and lower reaches of the Yangtze River over the past 20 years, China

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