scholarly journals Estimación de la concentración de clorofila-a en la supercie de la bahía de Sechura usando datos imágenes de Landsat 8

2021 ◽  
Vol 24 (2) ◽  
pp. 11-16
Author(s):  
Gilberto Ramírez ◽  
Joel Rojas ◽  
Jhon Guerrero
Keyword(s):  
Landsat 8 ◽  
Surface Reflectance

El propósito de este estudio es implementar los algoritmos OC2 y OC3 para estimar la Concentración de Clorofila-a (CCA) superficial a partir de datos imágenes del sensor OLI a bordo del satélite Landsat 8. Se validó el modelo de corrección atmosférica LaSRC (Landsat 8 Surface Reflectance Code) con mediciones in situ de la reflectancia de la superficie del agua registrada con un espectroradiómetro en la superficie del área del cultivo de concha de abanico de la bahía de Sechura. La validación da como resultado un coeficiente de correlación lineal de R = 95.1 % y un error cuadrático medio RMSE = 0.0095. También se hizo una comparación de la CCA derivadas de los algoritmos OC2 y OC3, obteniéndose como resultado un RMSE=0.145 mg/m3 y un coeciente de correlación de R=99 %. Por último, se hizo un contraste de los histogramas de la distribución espacial de la CCA estimadas de los algoritmos OC2 y OC3 sobre una región del área de estudio. Los resultados indican una mayor capacidad de discernir del algoritmo OC3 con respecto al algoritmo OC2.

scholarly journals Evaluation Analysis of Landsat Level-1 and Level-2 Data Products Using In Situ Measurements

2020 ◽  
Vol 12 (16) ◽  
pp. 2597
Author(s):  
Cibele Teixeira Pinto ◽  
Xin Jing ◽  
Larry Leigh
Keyword(s):  
In Situ Measurements ◽  
Landsat 8 ◽  
Surface Reflectance ◽  
Spectral Bands ◽  
Data Products ◽  
Landsat 7 ◽  
Level 1 ◽  
Level 2 ◽  
Reflectance Data

Landsat Level-1 products are delivered as quantized and calibrated scaled Digital Numbers (DN). The Level-1 DN data can be rescaled to Top-of-Atmosphere (TOA) reflectance applying radiometric rescaling coefficients. Currently, the Level-1 product is the standard data product of the Landsat sensors. The more recent Level-2 data products contain surface reflectance values, i.e., reflectance as it would be measured at ground level in the absence of atmospheric effects; in the near future, these products are anticipated to become the standard products of the Landsat sensors. The purpose of this paper is to present a radiometric performance evaluation of Level-1 and Level-2 data products for the Landsat-7 Enhanced Thematic Mapper Plus (ETM+) and Landsat-8 Operational Land Imager (OLI) sensors. TOA reflectance and derived surface reflectance values from both data products were evaluated and compared to in situ measurements from eight test sites located in Turkey, Brazil, Chile, the United States, France, and Namibia. The results indicate an agreement between the ETM+ and OLI Level-1 TOA reflectance data and the in situ measurements of 3.9% to 6.5% and 3.9% to 6.0%, respectively, across all spectral bands. Agreement between the in situ measurements and both Level-2 surface reflectance data products were consistently decreased in the shorter wavelength bands, and slightly better in the longer wavelength bands. The mean percent absolute error for Level-2 surface reflectance data ranged from 3.3% to 10% for both Landsat sensors. The significant decay in agreement with the data collected in situ in the short wavelength spectral bands with Level-2 data suggests issues with retrieval of aerosol concentration at some sites. In contrast, the results indicate a reasonably accurate estimate of water vapor in the mid-infrared spectrum. Lastly, despite the less reliable performance of Level-2 data product in the visible spectral region (compared with Level-1 data) in both sensors, the Landsat-8 OLI Level-2 showed an improvement of surface reflectance product over all spectral bands in common with the Landsat-7 ETM+ Level-2 data.

scholarly journals DEVELOPMENT OF SCHEME FOR THE ATMOSPHERIC CORRECTION OF LANDSAT-8 OLI DATA

2018 ◽  
Vol XLII-5 ◽  
pp. 671-674
Author(s):  
V. N. Pathak ◽  
M. R. Pandya ◽  
D. B. Shah ◽  
H. J. Trivedi
Keyword(s):  
Atmospheric Correction ◽  
Solar Spectrum ◽  
Coefficient Of Determination ◽  
Radiative Transfer Model ◽  
Transfer Model ◽  
Landsat 8 ◽  
Surface Reflectance ◽  
Standard Data ◽  
Satellite Signal

<p><strong>Abstract.</strong> In the present study, a physics based method called Scheme for Atmospheric Correction of Landsat-8 (SACLS8) is developed for the Operational Land Imager (OLI) sensor of Landsat-8. The Second Simulation of the Satellite Signal in the Solar Spectrum Vector (6SV) radiative transfer model is used in the simulations to obtain the surface reflectance. The surface reflectance derived using the SACL8 scheme is validated with the <i>in-situ</i> measurements of surface reflectance carried out at the homogeneous desert site located in the Little Rann of Kutch, Gujarat, India. The results are also compared with Landsat-8 surface reflectance standard data product over the same site. The good agreement of results with high coefficient of determination (R<sup>2</sup><span class="thinspace"></span>><span class="thinspace"></span>0.94) and low root mean square error (of the order of 0.03) with <i>in-situ</i> measurement values as well as those obtained from the Landsat-8 surface reflectance data establishes a good performance of the SACLS8 scheme for the atmospheric correction of Landsat-8 dataset.</p>

scholarly journals Validation of Space-Based Albedo Products from Upscaled Tower-Based Measurements Over Heterogeneous and Homogeneous Landscapes

2020 ◽  
Vol 12 (5) ◽  
pp. 833
Author(s):  
Rui Song ◽  
Jan-Peter Muller ◽  
Said Kharbouche ◽  
Feng Yin ◽  
William Woodgate ◽  
...  
Keyword(s):  
High Resolution ◽  
Surface Albedo ◽  
Spatial Scales ◽  
Atmospheric Correction ◽  
Landsat 8 ◽  
Surface Reflectance ◽  
Hemispherical Reflectance ◽  
Land Surfaces ◽  
Sentinel 2

Surface albedo is a fundamental radiative parameter as it controls the Earth’s energy budget and directly affects the Earth’s climate. Satellite observations have long been used to capture the temporal and spatial variations of surface albedo because of their continuous global coverage. However, space-based albedo products are often affected by errors in the atmospheric correction, multi-angular bi-directional reflectance distribution function (BRDF) modelling, as well as spectral conversions. To validate space-based albedo products, an in situ tower albedometer is often used to provide continuous “ground truth” measurements of surface albedo over an extended area. Since space-based albedo and tower-measured albedo are produced at different spatial scales, they can be directly compared only for specific homogeneous land surfaces. However, most land surfaces are inherently heterogeneous with surface properties that vary over a wide range of spatial scales. In this work, tower-measured albedo products, including both directional hemispherical reflectance (DHR) and bi-hemispherical reflectance (BHR), are upscaled to coarse satellite spatial resolutions using a new method. This strategy uses high-resolution satellite derived surface albedos to fill the gaps between the albedometer’s field-of-view (FoV) and coarse satellite scales. The high-resolution surface albedo is generated from a combination of surface reflectance retrieved from high-resolution Earth Observation (HR-EO) data and moderate resolution imaging spectroradiometer (MODIS) BRDF climatology over a larger area. We implemented a recently developed atmospheric correction method, the Sensor Invariant Atmospheric Correction (SIAC), to retrieve surface reflectance from HR-EO (e.g., Sentinel-2 and Landsat-8) top-of-atmosphere (TOA) reflectance measurements. This SIAC processing provides an estimated uncertainty for the retrieved surface spectral reflectance at the HR-EO pixel level and shows excellent agreement with the standard Landsat 8 Surface Reflectance Code (LaSRC) in retrieving Landsat-8 surface reflectance. Atmospheric correction of Sentinel-2 data is vastly improved by SIAC when compared against the use of in situ AErosol RObotic NETwork (AERONET) data. Based on this, we can trace the uncertainty of tower-measured albedo during its propagation through high-resolution EO measurements up to coarse satellite scales. These upscaled albedo products can then be compared with space-based albedo products over heterogeneous land surfaces. In this study, both tower-measured albedo and upscaled albedo products are examined at Ground Based Observation for Validation (GbOV) stations (https://land.copernicus.eu/global/gbov/), and used to compare with satellite observations, including Copernicus Global Land Service (CGLS) based on ProbaV and VEGETATION 2 data, MODIS and multi-angle imaging spectroradiometer (MISR).

scholarly journals DETEKSI SEBARAN MUATAN PADATAN TERSUSPENSI DENGAN MODEL EMPIRIS DAN MODEL SEMI-ANALITIK DI PERAIRAN BEKASI

2020 ◽  
Vol 12 (2) ◽  
pp. 341-351
Author(s):  
Pingkan Mayestika Afgatiani ◽  
Maryani Hartuti ◽  
Syarif Budhiman
Keyword(s):  
Time Series ◽  
Mean Absolute Error ◽  
Absolute Error ◽  
Google Earth ◽  
Landsat 8 ◽  
Surface Reflectance ◽  
Mean Square ◽  
Itu Model ◽  
Google Earth Engine

Salah satu parameter dalam kualitas air adalah muatan padatan tersuspensi (MPT). Muatan padatan tersuspensi terdiri dari lumpur, pasir dan jasad renik yang disebabkan pengikisan tanah yang terbawa ke badan air. Penelitian ini bertujuan untuk mendeteksi sedimen tersuspensi di perairan Bekasi. Landsat 8 digunakan untuk analisis padatan tersuspensi dengan platform Google Earth Engine dengan membandingkan antara model empiris dan semi-analitik. Alur studi ini meliputi deliniasi wilayah non air menggunakan data citra surface reflectance, analisis MPT, dan visualisasi. Selanjutnya dilakukan validasi dengan data in situ, pemilihan model dan implementasi time series. Hasil deteksi MPT tertampil dengan tampilan warna yang berbeda sesuai dengan konsentrasinya. Hasil uji validasi dengan data in situ menunjukkan nilai Normalized Mean Absolute Error (NMAE) model semi-analitik lebih mendekati syarat minimum yaitu sebesar 66,8%, berbeda jauh dengan model empiris sebesar 43768%. Nilai Root Mean Square Error (RMSE) pun terlihat bahwa model semi-analitik menghasilkan nilai yang jauh lebih kecil sebesar 51,4 dan model empiris sebesar 58577,2. Hal ini menunjukkan bahwa model semi-analitik memiliki nilai yang lebih baik dalam mendeteksi sebaran MPT. Analisis time series menunjukkan bahwa persebaran MPT tahun 2015 – 2019 di perairan pesisir memiliki sebaran MPT yang sangat tinggi, karena banyaknya tambak dan muara sungai. Oleh karena itu, model semi-analitik lebih direkomendasikan untuk mengestimasi konsentrasi MPT dibandingkan dengan model empiris.

scholarly journals Evaluation of Surface Reflectance Products Based on Optimized 6S Model Using Synchronous In Situ Measurements

2021 ◽  
Vol 14 (1) ◽  
pp. 83
Author(s):  
Xiaocheng Zhou ◽  
Xueping Liu ◽  
Xiaoqin Wang ◽  
Guojin He ◽  
Youshui Zhang ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Atmospheric Correction ◽  
Solar Spectrum ◽  
In Situ Measurements ◽  
Landsat 8 ◽  
Surface Reflectance ◽  
Landsat 8 Oli ◽  
Auxiliary Data ◽  
Sentinel 2

Surface reflectance (SR) estimation is the most essential preprocessing step for multi-sensor remote sensing inversion of geophysical parameters. Therefore, accurate and stable atmospheric correction is particularly important, which is the premise and basis of the quantitative application of remote sensing. It can also be used to directly compare different images and sensors. The Landsat-8 Operational Land Imager (OLI) and Sentinel-2 Multi-Spectral Instrument (MSI) surface reflectance products are publicly available and demonstrate high accuracy. However, there is not enough validation using synchronous spectral measurements over China’s land surface. In this study, we utilized Moderate Resolution Imaging Spectroradiometer (MODIS) atmospheric products reconstructed by Categorical Boosting (CatBoost) and 30 m ASTER Global Digital Elevation Model (ASTER GDEM) data to adjust the relevant parameters to optimize the Second Simulation of Satellite Signal in the Solar Spectrum (6S) model. The accuracy of surface reflectance products obtained from the optimized 6S model was compared with that of the original 6S model and the most commonly used Fast Line-of-sight Atmospheric Analysis of Spectral Hypercubes (FLAASH) model. Surface reflectance products were validated and evaluated with synchronous in situ measurements from 16 sites located in five provinces of China: Fujian, Gansu, Jiangxi, Hunan, and Guangdong. Through the indirect and direct validation across two sensors and three methods, it provides evidence that the synchronous measurements have the higher and more reliable validation accuracy. The results of the validation indicated that, for Landsat-8 OLI and Sentinel-2 MSI SR products, the overall root mean square error (RMSE) calculated results of optimized 6S, original 6S and FLAASH across all spectral bands were 0.0295, 0.0378, 0.0345, and 0.0313, 0.0450, 0.0380, respectively. R2 values reached 0.9513, 0.9254, 0.9316 and 0.9377, 0.8822, 0.9122 respectively. Compared with the original 6S model and FLAASH model, the mean percent absolute error (MPAE) of the optimized 6S model was reduced by 32.20% and 15.86% for Landsat-8 OLI, respectively. On the other, for the Sentinel-2 MSI SR product, the MPAE value was reduced by 33.56% and 33.32%. For the two kinds of data, the accuracy of each band was improved to varying extents by the optimized 6S model with the auxiliary data. These findings support the hypothesis that reliable auxiliary data are helpful in reducing the influence of the atmosphere on images and restoring reality as much as is feasible.

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

2019 ◽  
Vol 11 (11) ◽  
pp. 1344 ◽  
Author(s):  
Muhammad Bilal ◽  
Majid Nazeer ◽  
Janet E. Nichol ◽  
Max P. Bleiweiss ◽  
Zhongfeng Qiu ◽  
...  
Keyword(s):  
Vegetation Index ◽  
Estimation Method ◽  
Global Scale ◽  
Sensor Data ◽  
Landsat 8 ◽  
Surface Reflectance ◽  
Atmospheric Conditions ◽  
Land Surfaces ◽  
Sentinel 2A

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.

PEMETAAN SEBARAN PADANG LAMUN MENGGUNAKAN CITRA SATELIT LANDSAT 8 DI KEPULAUAN TANIMBAR KABUPATEN MALUKU TENGGARA BARAT PROVINSI MALUKU

2019 ◽  
Vol 3 ◽  
pp. 871
Author(s):  
Desita Anggraeni ◽  
M. Nurkholis Fauzi ◽  
Christian Novia Ngesti H.
Keyword(s):  
Landsat 8 ◽  
Surface Reflectance ◽  
Halophila Ovalis ◽  
Halodule Uninervis ◽  
Gap Data

Padang lamun merupakan habitat penting pesisir yang memiliki peran kunci dalam ekosistem pesisir. Kawasan ini merupakan area asuhan bagi ikan-ikan kecil, udang, persembunyian biota dari predatornya, pendaur zat hara, serta penyerap nutrien dari limpasan air laut yang dapat membantu menstabilkan sedimen dan kejernihan air. Kepulauan Tanimbar merupakan salah satu lokasi di Provinsi Maluku dengan potensi sebaran lamun yang cukup luas, namun informasi mengenai sebaran lamun di kawasan ini tidak terdata dengan baik. Teknologi penginderaan jauh merupakan salah satu alternatif untuk mengisi gap data di area yang luas dan sulit dijangkau, termasuk untuk memetakan sebaran lamun di Kepulauan Tanimbar. Penelitian ini bertujuan untuk menyediakan data dasar sebaran dan luas habitat lamun di pesisir Kepulauan Tanimbar. Metode yang digunakan adalah analisis citra penginderaan jauh Landsat 8, menerapkan penajaman citra untuk perairan dangkal menggunakan algoritma Lyzenga. Citra Landsat yang digunakan Landsat Surface Reflectance liputan path/row 106/65 dan 106/66 tahun perekaman 2017. Pengambilan data lapangan dilakukan pada tanggal 1-10 November 2017. Metode pengambilan data lamun dilakukan menggunakan metode seagrass watch . Hasil pengolahan citra menunjukkan lamun terdistribusi merata di seluruh pesisir Kepulauan Tanimbar dengan luas total 5.615,63 hektar dengan tutupan terpadat di sekitar Pulau Seira. Hasil survei lapangan menunjukkan tutupan lamun terpadat dijumpai di Formusan dengan tutupan lamun rata-rata 95%. Kondisi lamun paling baik berada di daerah Sabal, didukung kondisi air yang sangat jernih dengan substrat utama pasir. Berdasarkan hasil pengamatan lapangan, jenis lamun yang ditemukan antara lain: E n h alu s a c o r oid e s , T h ala s sia h e m p ric hii, C y m o d o c e a s e r r ula t a , C y m o d o c e a rotundata, Syringodi um isoetifolium, Halodule uninervis, Halophila ovalis, dan Halophila minor .

scholarly journals Development of the Statistical Model for Monitoring Salinization in the Mekong Delta of Vietnam Using Remote Sensing Data and In-Situ Measurements

Proceedings ◽  
2018 ◽  
Vol 2 (10) ◽  
pp. 565
Author(s):  
Nguyen Nguyen Vu ◽  
Le Van Trung ◽  
Tran Thi Van
Keyword(s):  
Statistical Model ◽  
Mekong Delta ◽  
Remote Sensing Data ◽  
Principal Component ◽  
Measured Data ◽  
In Situ Measurements ◽  
Landsat 8 ◽  
Spectral Bands ◽  
Component Band

This article presents the methodology for developing a statistical model for monitoring salinity intrusion in the Mekong Delta based on the integration of satellite imagery and in-situ measurements. We used Landsat-8 Operational Land Imager and Thermal Infrared Sensor (Landsat- 8 OLI and TIRS) satellite data to establish the relationship between the planetary reflectance and the ground measured data in the dry season during 2014. The three spectral bands (blue, green, red) and the principal component band were used to obtain the most suitable models. The selected model showed a good correlation with the exponential function of the principal component band and the ground measured data (R2 > 0.8). Simulation of the salinity distribution along the river shows the intrusion of a 4 g/L salt boundary from the estuary to the inner field of more than 50 km. The developed model will be an active contribution, providing managers with adaptation and response solutions suitable for intrusion in the estuary as well as the inner field of the Mekong Delta.

scholarly journals Application of an improved surface energy balance model to two large valley glaciers in the St. Elias Mountains, Yukon

2020 ◽  
pp. 1-16
Author(s):  
Tim Hill ◽  
Christine F. Dow ◽  
Eleanor A. Bash ◽  
Luke Copland
Keyword(s):  
Energy Balance ◽  
Surface Energy ◽  
Surface Energy Balance ◽  
Shortwave Radiation ◽  
Balance Model ◽  
Landsat 8 ◽  
Glacier Surface ◽  
Ice Dynamics ◽  
Surface Melt

Abstract Glacier surficial melt rates are commonly modelled using surface energy balance (SEB) models, with outputs applied to extend point-based mass-balance measurements to regional scales, assess water resource availability, examine supraglacial hydrology and to investigate the relationship between surface melt and ice dynamics. We present an improved SEB model that addresses the primary limitations of existing models by: (1) deriving high-resolution (30 m) surface albedo from Landsat 8 imagery, (2) calculating shadows cast onto the glacier surface by high-relief topography to model incident shortwave radiation, (3) developing an algorithm to map debris sufficiently thick to insulate the glacier surface and (4) presenting a formulation of the SEB model coupled to a subsurface heat conduction model. We drive the model with 6 years of in situ meteorological data from Kaskawulsh Glacier and Nàłùdäy (Lowell) Glacier in the St. Elias Mountains, Yukon, Canada, and validate outputs against in situ measurements. Modelled seasonal melt agrees with observations within 9% across a range of elevations on both glaciers in years with high-quality in situ observations. We recommend applying the model to investigate the impacts of surface melt for individual glaciers when sufficient input data are available.

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