Calibrating Geosynchronous and Polar Orbiting Satellites: Sharing Best Practices

Helder, Dennis L.; Doelling, David R.; Bhatt, Rajendra; Choi, Taeyoung; Barsi, Julia A.

doi:10.3390/rs12172786

Cited by 6 publications

(5 citation statements)

References 66 publications

(79 reference statements)

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“…According to Babu and Green et al [44,45], the uncertainty associated with AVIRIS measurements was around ~8.6% for all bands. According to Barsi and Helder et al [46,47], the uncertainty associated with the temporal and absolute temporal trend of L8 was within 2% for all bands. After obtaining the uncertainties associated with the input parameters of the ELM SR model, the ELM generates atmospheric coefficients (gain and bias) were calculated within their uncertainties using Monte-Carlo Simulation Method.…”

Section: Uncertainty Analysismentioning

confidence: 65%

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: Uncertainty Analysismentioning

confidence: 65%

Method of Validating Satellite Surface Reflectance Product Using Empirical Line Method

et al. 2023

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“…In order to achieve better correction results, it was necessary to extend the application of the correction model to the entire plateau and achieve correction of summertime GIIRS temperatures over the entire plateau. Due to the orbit height of polar-orbiting satellites being lower than that of geostationary satellites, the observation accuracy of polar-orbiting satellites is often higher [34,35]. Research on the accuracy of IASI temperature profile products showed that IASI temperature products have good reliability and accuracy [36][37][38].…”

Section: Correction Model Based On the Iasi Level2 Temperature Productmentioning

confidence: 99%

Error Analysis and Correction of FENGYUN-4A GIIRS Temperature Profile Products in Summer over the Qinghai–Tibet Plateau

Jiang,

Wang,

Niu

2024

Remote Sensing

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To understand the applicability of the temperature profile product of the FENGYUN-4A (FY-4A) geostationary interferometric infrared detector (GIIRS) in summer over the Qinghai–Tibet Plateau and to improve product quality, the error of GIIRS temperature products was analyzed based on radiosonde data. A long short-term memory network model (LSTM) was used to correct the GIIRS temperature profile product in summer over the plateau at a high altitude (above 500 hPa), and further evaluation of the corrected product was conducted. The results show that summertime GIIRS temperature retrievals over the Qinghai–Tibet Plateau had a positive bias above 150 hPa and a negative bias below 150 hPa, resulting in an overall negative bias. The root mean square error was between 2 and 2.9 K, and the root mean square error was relatively large at 100 hPa and above. The LSTM established in this study could effectively correct the GIIRS temperature over the plateau. The correlation and root mean square error of the corrected GIIRS temperature and the radiosonde observation temperature were significantly improved. Using a trained LSTM correction model to correct the hourly GIIRS temperature can improve the accuracy and usability of the product. After correction, the average bias of the GIIRS temperature compared to the ERA5 temperature product was reduced from −0.26 K to 0.06 K, and the root mean square error was reduced from 2.25 K to 1.25 K. The correction model can be applied to different seasons, and it can also correct the GIIRS temperature in larger areas based on other high-precision and high-resolution data, achieving good results, thus indicating that the correction model has universal applicability.

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“…Additionally, the uncertainty related to SBAF correction is computed based on Equation ( 27) below. Lastly, the temporal uncertainty of the orbital sensor (L8 OLI) SR is determined by utilizing the L8 OLI Level 1 temporal uncertainty [41].…”

Section: Uncertanties In Validation Of Landsat 8 With Respect To Arab...mentioning

confidence: 99%

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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Calibrating Geosynchronous and Polar Orbiting Satellites: Sharing Best Practices

Cited by 6 publications

References 66 publications

Method of Validating Satellite Surface Reflectance Product Using Empirical Line Method

Method of Validating Satellite Surface Reflectance Product Using Empirical Line Method

Error Analysis and Correction of FENGYUN-4A GIIRS Temperature Profile Products in Summer over the Qinghai–Tibet Plateau

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

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