Evaluation of Radiometric and Atmospheric Correction Algorithms for Aboveground Forest Biomass Estimation Using Landsat 5 TM Data

López-Serrano, Pablito Marcelo; Corral-Rivas, José Javier; Varela, Ramón Alberto Díaz; González, Juan Gabriel Álvarez; López-Sánchez, Carlos A.

doi:10.3390/rs8050369

Cited by 69 publications

(36 citation statements)

References 47 publications

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“…Nazer et al [45] compared five atmospheric correction algorithms, 6S, FLAASH, ATCOR, DOS, and ELM, over sand, turf, grass, and water surfaces. López-Serrano et al [46] evaluated the performance of the COST, ATCOR2, FLAASH, 6S, and TOA algorithms for the estimation of forest above-ground biomass. Martins et al [47] compared the 6S, ACOLITE, and Sen2Cor methods applied to the new platform Sentinel 2-MSI.…”

Section: Introductionmentioning

confidence: 99%

Atmospheric and Radiometric Correction Algorithms for the Multitemporal Assessment of Grasslands Productivity

et al. 2018

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A key step in the processing of satellite imagery is the radiometric correction of images to account for reflectance that water vapor, atmospheric dust, and other atmospheric elements add to the images, causing imprecisions in variables of interest estimated at the earth's surface level. That issue is important when performing spatiotemporal analyses to determine ecosystems' productivity. In this study, three correction methods were applied to satellite images for the period 2010-2014. These methods were Atmospheric Correction for Flat Terrain 2 (ATCOR2), Fast Line-of-Sight Atmospheric Analysis of Spectral Hypercubes (FLAASH), and Dark Object Substract 1 (DOS1). The images included 12 sub-scenes from the Landsat Thematic Mapper (TM) and the Operational Land Imager (OLI) sensors. The images corresponded to three Permanent Monitoring Sites (PMS) of grasslands, 'Teseachi', 'Eden', and 'El Sitio', located in the state of Chihuahua, Mexico. After the corrections were applied to the images, they were evaluated in terms of their precision for biomass estimation. For that, biomass production was measured during the study period at the three PMS to calibrate production models developed with simple and multiple linear regression (SLR and MLR) techniques. When the estimations were made with MLR, DOS1 obtained an R 2 of 0.97 (p < 0.05) for 2012 and values greater than 0.70 (p < 0.05) during 2013-2014. The rest of the algorithms did not show significant results and DOS1, which is the simplest algorithm, resulted in the best biomass estimator. Thus, in the multitemporal analysis of grassland based on spectral information, it is not necessary to apply complex correction procedures. The maps of biomass production, elaborated from images corrected with DOS1, can be used as a reference point for the assessment of the grassland condition, as well as to determine the grazing capacity and thus the potential animal production in such ecosystems.

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Section: Introductionmentioning

confidence: 99%

Atmospheric and Radiometric Correction Algorithms for the Multitemporal Assessment of Grasslands Productivity

et al. 2018

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“…Remote sensing data were affected by their own spectral resolution, resulting in differences in the extraction accuracy of the band spectrum, vegetation index, texture information compared with high-resolution images [37,38,42,81]. In addition, due to the large span of date and time acquisition of the eight scenes in the same period, although atmospheric correction was performed, radiation differences could not be completely eliminated, resulting in errors in bamboo forest information extraction [82]. This is what we might improve in the future study.…”

Section: Discussionmentioning

confidence: 99%

Spatiotemporal Estimation of Bamboo Forest Aboveground Carbon Storage Based on Landsat Data in Zhejiang, China

Han

et al. 2018

Remote Sensing

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China is one of the countries with the most abundant bamboo forest resources in the world, and Zhejiang province is among the top-3 Chinese provinces with richest bamboo forests. For rational bamboo forests management, it is of great significance to study the spatiotemporal dynamic changes of Aboveground Carbon (AGC) stocks of bamboo forest in Zhejiang. In this study, remote sensing variables, such as spectral, vegetation indices and texture features of bamboo forest in Zhejiang, were extracted from 32 Landsat TM and OLI images got from four different years (2000, 2004, 2008 and 2014). These variables were subsequently selected with stepwise regression method to build an estimation model of AGC of the bamboo forests. The results showed that (1) the accuracy of bamboo forest remote sensing information extracted from the four different years was high with a classification accuracy of >76.26% and an accuracy of users of >91.62%. The classification area of bamboo forest was highly consistent with the area from forest resource inventory, and the area accuracy was over 96.50%; (2) the estimation model performed well in predicting the AGC in Zhejiang for different years. The correlation coefficient for estimated and measured AGC was between 63% and 72% with low root mean square error; (3) the derived AGC of the bamboo forests in Zhejiang province increased gradually from 2000 to 2014, with the AGC density of 6.75 Mg·ha −1 , 10.95 Mg·ha −1 , 15.25 Mg·ha −1 and 19.07 Mg·ha −1 respectively, and the average annual growth of 0.88 Mg·ha −1 . The spatiotemporal evolution of bamboo forest AGC in Zhejiang province had a close relationship with the gradual expansion of bamboo forest in the province and the differentiation of management levels in different regions.

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“…Previous studies have evaluated the various atmospheric correction methods over different types of land covers, atmospheric conditions, and geographical locations [12,[24][25][26][27]. For example , Nazeer et al (2014) [12] validated the SR from both image-based (DOS and ELM) and physical methods (ATCOR, FLAASH, and 6S) over sand, artificial turf, grass, and water surfaces using in situ measured SR, and found the 6S to be robust and more accurate for SR estimation compared to the other methods.…”

Section: Introductionmentioning

confidence: 98%

“…Nguyen et al (2015) [24] tested the adequacy of the DOS, FLAASH, and 6S for above-ground biomass (AGB) estimations of the Gongju and Sejong regions of South Korea and found that 6S outperforms the other methods. López-Serrano et al (2016) [25] compared the ATCOR, COST (cosine of the Sun zenith angle), FLAASH, and 6S for estimating AGB in the temperate forest area of northeast Durango, Mexico, and concluded that the 6S method is more efficient and reliable than other methods. These validation exercises suggested that (i) the physical methods performed much better than the image-based methods, and (ii) the 6S is the most reliable physical method.…”

Section: Introductionmentioning

confidence: 99%

A Simplified and Robust Surface Reflectance Estimation Method (SREM) for Use over Diverse Land Surfaces Using Multi-Sensor Data

et al. 2019

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Surface reflectance (SR) estimation is the most critical preprocessing step for deriving geophysical parameters in multi-sensor remote sensing. Most state-of-the-art SR estimation methods, such as the vector version of the Second Simulation of the Satellite Signal in the Solar Spectrum (6SV) radiative transfer (RT) model, depend on accurate information on aerosol and atmospheric gases. In this study, a Simplified and Robust Surface Reflectance Estimation Method (SREM) based on the equations from 6SV RT model, without integrating information of aerosol particles and atmospheric gasses, is proposed and tested using Landsat 5 Thematic Mapper (TM), Landsat 7 Enhanced Thematic Mapper plus (ETM+), and Landsat 8 Operational Land Imager (OLI) data from 2000 to 2018. For evaluation purposes, (i) the SREM SR retrievals are validated against in situ SR measurements collected by Analytical Spectral Devices (ASD) from the South Dakota State University (SDSU) site, USA; (ii) cross-comparison between the SREM and Landsat spectral SR products, i.e., Landsat Ecosystem Disturbance Adaptive Processing System (LEDAPS) and Landsat 8 Surface Reflectance Code (LaSRC), are conducted over 11 urban (2013–2018), 13 vegetated (2013–2018), and 11 desert/arid (2000 to 2018) sites located over different climatic zones at a global scale; (iii) the performance of the SREM spectral SR retrievals for low to high aerosol loadings is evaluated; (iv) spatio-temporal cross-comparison is conducted for six Landsat paths/rows located in Asia, Africa, Europe, and the United States of America from 2013 to 2018 to consider a large variety of land surfaces and atmospheric conditions; (v) cross-comparison is also performed for the Normalized Difference Vegetation Index (NDVI), the Enhanced Vegetation Index (EVI), and the Soil Adjusted Vegetation Index (SAVI) calculated from both the SREM and Landsat SR data; (vi) the SREM is also applied to the Sentinel-2A and Moderate Resolution Imaging Spectrometer (MODIS) data to explore its applicability; and (vii) errors in the SR retrievals are reported using the mean bias error (MBE), root mean squared deviation (RMSD), and mean systematic error (MSE). Results depict significant and strong positive Pearson’s correlation (r), small MBE, RMSD, and MSE for each spectral band against in situ ASD data and Landsat (LEDAPS and LaSRC) SR products. Consistency in SREM performance against Sentinel-2A (r = 0.994, MBE = −0.009, and RMSD = 0.014) and MODIS (r = 0.925, MBE = 0.007, and RMSD = 0.014) data suggests that SREM can be applied to other multispectral satellites data. Overall, the findings demonstrate the potential and promise of SREM for use over diverse surfaces and under varying atmospheric conditions using multi-sensor data on a global scale.

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Evaluation of Radiometric and Atmospheric Correction Algorithms for Aboveground Forest Biomass Estimation Using Landsat 5 TM Data

Cited by 69 publications

References 47 publications

Atmospheric and Radiometric Correction Algorithms for the Multitemporal Assessment of Grasslands Productivity

Atmospheric and Radiometric Correction Algorithms for the Multitemporal Assessment of Grasslands Productivity

Spatiotemporal Estimation of Bamboo Forest Aboveground Carbon Storage Based on Landsat Data in Zhejiang, China

A Simplified and Robust Surface Reflectance Estimation Method (SREM) for Use over Diverse Land Surfaces Using Multi-Sensor Data

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