Influence of atmospheric correction on the estimation of biophysical parameters of crop canopy using satellite remote sensing

Rahman, Hafızur

doi:10.1080/01431160151144332

Cited by 14 publications

(7 citation statements)

References 11 publications

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“…For many applications, especially multi-temporal analyses, raw relative pixel values or digital image numbers have to be corrected for atmospheric effects and converted to spectral reflectance at the surface before the images are processed [35]. Improper atmospheric correction can lead to significant errors in the retrieved reflectance and affect the accuracy of the estimates [36]. Several atmospheric correction models have been developed, including the Simplified Model for Atmospheric Correction (SMAC [37]), Second Simulation of the Satellite Signal in the Solar Spectrum (6S [38]), Moderate-Resolution Atmospheric Transmittance and Radiance Code (MODTRAN [39]), ATmospheric CORrection (ATCOR [40]), Dark Object Subtraction (DOS [41]), and COSine Transmission for atmospheric correction (COST [42]).…”

Section: Modis Datamentioning

confidence: 99%

An Adaptive Model to Monitor Chlorophyll-a in Inland Waters in Southern Quebec Using Downscaled MODIS Imagery

et al. 2014

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Abstract:The purpose of this study is to assess the performance of an adaptive model (AM) in estimating chlorophyll-a concentration (Chl-a) in optically complex inland waters. Chl-a modeling using remote sensing data is usually based on a single model that generally follows an exponential function. The estimates produced by such models are relatively accurate at high Chl-a concentrations, but accuracy drops at low concentrations. Our objective was to develop an approach combining spectral response classification and three semi-empirical algorithms. The AM discriminates between three blooming classes (waters poorly, moderately, and highly loaded in Chl-a), with discrimination thresholds set using the classification and regression tree (CART) technique. The calibration of three specific estimators for each class was achieved using a multivariate stepwise regression. Compared to published models (Floating Algae Index, Kahru model, and APProach by ELimination) using the same data set, the AM provided better Chl-a concentration estimates (R 2 of 0.96, relative RMSE of 23%, relative Bias of −2%, and a relative NASH criterion of 0.9). Moreover, the AM achieved an overall success rate of 67% in the estimation of blooming classes (corresponding to low, moderate, and high Chl-a concentration classes). This was done using an independent data set collected from 22 inland water bodies for the period 2007-2010 and for which the only information available was the blooming class.

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Section: Modis Datamentioning

confidence: 99%

An Adaptive Model to Monitor Chlorophyll-a in Inland Waters in Southern Quebec Using Downscaled MODIS Imagery

et al. 2014

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“…The evolving condition of a landscape with surface components consisting of primary surface classes e.g., soil, vegetation and water is principally dependent on the dynamic changes of surface variables that result in modification in their radiative response properties. In such deterministic processes of surface radiative responses, vegetation phenology, land physiography and condition, amount of incident solar radiation, dynamic soil moisture condition plays important role (S. Rahman, 2007, Rahman 2001). The role of overspreading water has a dual role to play in this regard.…”

Section: Y Ymentioning

confidence: 99%

Preliminary Application of Space-based Remote Sensing and Geospatial Technology for Investigation on the Geo-environmental Consequences of Cyclone Aila 2009 in the Bangladesh

Tazneen

Rahman²,

Rahman

et al. 2021

International Journal of Environment and Geoinformatics

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Climate change has become a great concern in the context of global change and increased frequency and magnitude of natural disasters throughout the world. Natural disasters like cyclones, storm surges, floods etc. often cause significant losses of life, largescale economic and social impacts, and considerable environmental damages. This paper dealt with the overall methodological development to investigate the consequences of climate change extremities particularly, the after effects of cyclone using Remote Sensing (RS) and Geographic Information System (GIS) technology. In this context, individual functional components have been investigated, tested and verified. Time series multi-sensor satellite data particularly of Landsat, World view (from Google origin) have been utilized to infer information on the consequences of Cyclone Aila 2009 hitting part of Southwestern Bangladesh as a pilot study. Information retrieval mechanism utilized is based on specially designed methodological framework using satellite-based RS technology along with GIS. Developed technical approach consists of a three-fold components -(i) Establishment of input foundation layer with high spatial details to support characterization, recognition and identification of important surface features using high resolution satellite data; (ii) Effective operational procedure to process, analyze, interpret and finally to archive the retrieved information; (iii) Functional computations were made using spatial modeler language (SML) programming environment under ERDAS Imagine image processing software. Satellite image processing, analysis operation together with image-based spectral characterization of surface features under different stressing conditions etc. have been exercised to derive useful surface information. Specially designed geospatial database has been established in GIS using ArcGIS operational platform. Varieties of geospatial data from diverse sources have been incorporated categorically as column-based attributes in GIS. Dual spatial data layers have been generated in GIS for two different dates representing the pre-cyclone and post-cyclone time sequences utilizing appropriate high spatial resolution satellite images. Finally, a geospatial image-based combinational technique has been utilized employing high temporal and moderate spatial resolution time series satellite data with low temporal and high spatial resolution satellite data. Such an operation results in an improved spatial and temporal resolution of output products enabling capture of dynamics of surface features in the spatiotemporal domain providing more precision and details in the output. This study has been supplemented with necessary Ground Position System (GPS)-guided ground truthing, selected field data collection and field-based group discussions outcomes.

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“…Meyer et al 1993, Richter 1996, Richter et al 2006, Schroeder et al 2007), but few of them quantify the effects of some of these corrections on the change-detection results (e.g. Rahman 2001, Paolini et al 2006.…”

Section: Introductionmentioning

confidence: 98%

Relationship between atmospheric corrections and training-site strategy with respect to accuracy of greenhouse detection process from very high resolution imagery

Carvajal

Agüera

Aguilar

et al. 2010

International Journal of Remote Sensing

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Frequently, satellite images that are acquired to extract a target surface are atmospherically corrected prior to the detection process. Thus, the unification of measure units is achieved, and atmospheric effects are removed from various imagery sources or taken at different dates. In this paper, four increasing levels of atmospheric corrections are applied (Top-Of-Atmosphere transformation: TOA; Apparent Reflectance Model: ARM; Flat Areas Model: FAM; Non-Flat Areas Model: NFAM). Then, the classification process is carried out using two strategies of training-site definitions (statistically purified and crude training sites) and two satellite imagery sources (QuickBird and Ikonos). Three-way Analysis of Variance (ANOVA) tests and Fisher's least-significant difference tests are included in quality classification assessment, based on four accuracy indexes. Two images from both remote sensors are orthorectified, and then it is checked that all selected atmospheric correction levels have significantly different influences on the statistics of both orthoimages. Taking into account the conditions established in this work, it is concluded that a lower atmospheric correction level would be preferred because it does not present significantly worse results than other levels considered. Training sites would not be statistically purified, and QuickBird or Ikonos would be chosen, depending on the aspect of the greenhouse detection accuracy preferred.

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Influence of atmospheric correction on the estimation of biophysical parameters of crop canopy using satellite remote sensing

Cited by 14 publications

References 11 publications

An Adaptive Model to Monitor Chlorophyll-a in Inland Waters in Southern Quebec Using Downscaled MODIS Imagery

An Adaptive Model to Monitor Chlorophyll-a in Inland Waters in Southern Quebec Using Downscaled MODIS Imagery

Preliminary Application of Space-based Remote Sensing and Geospatial Technology for Investigation on the Geo-environmental Consequences of Cyclone Aila 2009 in the Bangladesh

Relationship between atmospheric corrections and training-site strategy with respect to accuracy of greenhouse detection process from very high resolution imagery

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