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

Pinto, Cibele Teixeira; Jing, Xin; Leigh, Larry

doi:10.3390/rs12162597

Cited by 41 publications

(15 citation statements)

References 35 publications

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“…Results indicated a slightly improved Landsat-8 OLI SR in the longer wavelength bands and consistently decreased in the shorter wavelength bands. The result showed an OLI surface reflectance product improvement over all spectral bands familiar with the Landsat-7 ETM+ [8].…”

Section: Literature Reviewmentioning

confidence: 93%

Method of Validating Satellite Surface Reflectance Product Using Empirical Line Method

et al. 2023

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Atmospherically corrected surface reflectance (SR) products are used for reliable monitoring of land surfaces and are the standard products of Landsat sensors. Due to increased demand for SR products, a need exists to verify that the L2C2 (Level-2 Collection-2) SR products are precise and accurate. The Level-2 Collection 2 (L2C2) SR Product has processed satellite imagery data that correct for atmospheric effects such as absorption and scattering, providing a more accurate representation of Earth’s surface. The validation of SR products using ground truth measurement is essential. This study aims to develop and evaluate a validation methodology for satellite SR products. Thus, the Empirical Line Method (ELM) is used here for atmospheric validation of remotely sensed data. Validation is performed using the SR derived from ELM tied to ground truth measurement. Absolute surface reflectance models of Algodones Dunes and the Salton Sea located in North America Sonoran Desert are developed to extend the temporally limited ground truth measurements. This model can give ground truth reflectance in any time frame independent of time constraints. The result of the absolute surface reflectance model of Algodones Dunes indicates that the model predicts the response of Algodones Dunes with an average accuracy of 0.0041 and precision of 0.0063 and gives ground measurements across all multispectral between 350 and 2500 nm. For the Salton Sea, the model predicts the response of the Salton Sea with mean absolute error (MAE) of 0.0035 and gives ground measurements across all multispectral between 350 and 2500 nm. The ELM generates atmospheric coefficients (gain and bias), which are applied to an image to obtain SR. Validation results indicated that for L9-OLI-2, L8-OLI, and L5-TM-SR products, the RMSE range is 0.0019 to 0.0106, 0.0019 to 0.0148 and 0.0026 to 0.0045 reflectance unit, respectively, and accuracy is within 0.0038, 0.0022, and 0.0055 reflectance unit across all spectral bands of L9, L8, and L5, respectively. On average, the validation result showed a strong linear relation between the L2C2 SR products and ELM SR within 0.5 to 1 reflectance units. These results demonstrate the high accuracy and reliability of the L2C2 SR product, providing valuable information for a wide range of remote sensing applications, including land cover and land use mapping, vegetation monitoring, and climate change studies.

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Section: Literature Reviewmentioning

confidence: 93%

Method of Validating Satellite Surface Reflectance Product Using Empirical Line Method

et al. 2023

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Section: Arable Mark 2 Radiometric Calibration With Asd Fieldspecmentioning

confidence: 99%

“…Next, the Arable measurements are averaged over a 15 min interval to compare with the Simulated Arable from ASD through the cross-calibration method. Equation (2) will be used to calculate simulated Arable from ASD and Equation ( 3) is the ratio between simulated Arable SR from ASD and average Arable SR used as a cross calibration gain [4]. As requirements of cross calibration both the Arable sensor and the ASD FieldSpec should acquire the same ground target reflectance, with the same spectral bands, acquisition time, and illumination viewing geometry [31].…”

Section: Arable Mark 2 Radiometric Calibration With Asd Fieldspecmentioning

confidence: 99%

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Evaluation of Low-Cost Radiometer for Surface Reflectance Retrieval and Orbital Sensor’s Validation

Pathiranage,

Leigh,

Pinto

2023

Remote Sensing

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This paper evaluates the Arable Mark 2 sensor, an automated and low-cost radiometer, for its potential to retrieve surface reflectance data and validate orbital sensors such as the Landsat-8 (L8) Operational Land Imager (OLI) Level 2 product. While orbital sensors are widely used for monitoring solar radiation changes, managing natural resources, and understanding climatic trends, atmospheric effects can make it challenging to obtain accurate measurements. Equipped with multiple sensors, including long-wave and short-wave radiometers, the Arable Mark 2 sensor can measure upwelling and downwelling irradiance to calculate surface reflectance. To assess the accuracy and consistency of the Arable Mark 2 sensor, the study performed a cross-calibration using a ground truth measurement collected with the Analytical Spectral Device (ASD) as the reference point. Additionally, a spectral band adjustment factor (SBAF) was applied across the calibrated Arable surface reflectance to compare it against the orbital sensor. An automated library aided in calculating SBAF for the days with unavailable hyperspectral data. The study found that the Arable Mark 2 sensor can provide accurate surface reflectance data that can be used for orbital sensor validation. The Arable sensor was successfully calibrated against the ASD FieldSpec with an average difference of less than 1/10 reflectance unit (reflectance unit = 0.01) for the blue, green, yellow, and red bands. The red-edge and NIR-1 bands showed an average difference of less than 1/2 reflectance units, while the NIR-2 band had an average difference of less than 1/10 reflectance unit of calibration accuracy. The calibrated Arable surface reflectance data was then compared against orbital sensor surface reflectance data, and the results showed good agreement between the two datasets. The study concludes that the low-cost and automated nature of the Arable Mark 2 sensor makes it a promising tool for surface reflectance retrieval and orbital sensor validation.

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“…(1) The radiometric correction was performed to reduce, eliminate and correct errors in the digital numbers of the image. The radiometric correction (also known as) was performed by calculating the top-of-atmosphere (TOA) reflectance using Equations ( 1) and (2) [38]:…”

Section: Image Preprocessingmentioning

confidence: 99%

Recognition and Prediction of Land Dynamics and Its Associated Impacts in Al-Qurayyat City and along Al-Shamal Train Pathway in Saudi Arabia

et al. 2021

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Saudi Arabia has experienced substantial urban growth over the last few decades, transforming from rural to urban communities due to rapid economic growth. Saudi Arabia is ranked as one of the most urbanized countries, with more than 80% of its population existing in urban centers. Four Landsat imagery datasets acquired in 1989, 2002, 2013, and 2021 were used to estimate the dynamics of land cover and urban growth in Al-Qurayyat City and investigate the relationship between the construction of Al-Shamal train in 2011 and the land dynamics. The results emphasize a strong intercorrelation between the construction of the Al-Shamal train pathway and the land development and the rapid urbanization in Al-Qurayyat City. The results show that the urban and built-up area expanded from 1.96% to 7.25% between 1989 and 2021. Future prediction of land cover dynamics and urban growth in 2030 were estimated using the Markov chain and CA-Markov models. The findings of future prediction show that more than 60% of the total area of Al-Qurayyat City will transform into urban and built-up areas by 2030. The dramatic increase in urban and built-up areas and the subsequent reduction in other land cover types will impact the environmental sustainability of Al-Qurayyat City. The findings in this paper recommend smart growth, which guarantees environmentally friendly development for future land use/land cover planning in Al-Qurayyat City. This study will be beneficial to the urban planner and policymakers for proper sustainable development decisions by exploring the land cover changing pattern and the trends of urban expansion.

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

Cited by 41 publications

References 35 publications

Method of Validating Satellite Surface Reflectance Product Using Empirical Line Method

Method of Validating Satellite Surface Reflectance Product Using Empirical Line Method

Evaluation of Low-Cost Radiometer for Surface Reflectance Retrieval and Orbital Sensor’s Validation

Recognition and Prediction of Land Dynamics and Its Associated Impacts in Al-Qurayyat City and along Al-Shamal Train Pathway in Saudi Arabia

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