Performance Assessment of Landsat-9 Atmospheric Correction Methods in Global Aquatic Systems DOI Creative Commons

Aoxiang Sun,

Shuangyan He, Yanzhen Gu

и другие.

Remote Sensing, Год журнала: 2024, Номер 16(23), С. 4517 - 4517

Опубликована: Дек. 2, 2024

The latest satellite in the Landsat series, Landsat-9, was successfully launched on 27 September 2021, equipped with Operational Land Imager-2 (OLI-2) sensor, continuing legacy of OLI/Landsat-8. To evaluate uncertainties water surface reflectance derived from OLI-2, this study conducts a comprehensive performance assessment six atmospheric correction (AC) methods—DSF, C2RCC, iCOR, L2gen (NIR-SWIR1), (NIR-SWIR2), and Polymer—using in-situ measurements 14 global sites, including 13 AERONET-OC stations 1 MOBY station, collected between 2021 2023. Error analysis shows that (NIR-SWIR1) (RMSE ≤ 0.0017 sr−1, SA = 6.33°) (NIR-SWIR2) 0.0019 6.38°) provide best results across four visible bands, demonstrating stable different optical types (OWTs) ranging clear to turbid water. Following these are C2RCC 0.0030 5.74°) Polymer 0.0027 7.76°), DSF 0.0058 11.33°) iCOR 0.0051 12.96°) showing poorest results. By comparing uncertainty consistency Landsat-9 Sentinel-2A/B (MSI) S-NPP/NOAA20 (VIIRS), show OLI-2 has similar MSI VIIRS blue, blue-green, green RMSE differences within 0.0002 sr−1. In red band, lower than those but higher VIIRS, an difference about 0.0004 Overall, data processed using reliable high making it suitable for integrating multi-satellite observations enhance coastal color monitoring.

Язык: Английский

River Ice Mapping from Landsat-8 OLI Top of Atmosphere Reflectance Data by Addressing Atmospheric Influences with Random Forest: A Case Study on the Han River in South Korea DOI Creative Commons
Hyangsun Han, Tae-Wook Kim, S. D. Kim

и другие.

Remote Sensing, Год журнала: 2024, Номер 16(17), С. 3187 - 3187

Опубликована: Авг. 29, 2024

Accurate river ice mapping is crucial for predicting and managing floods caused by jams the safe operation of hydropower water resource facilities. Although satellite multispectral images are widely used mapping, atmospheric contamination limits their effectiveness. This study developed models Han River in South Korea using atmospherically uncorrected Landsat-8 Operational Land Imager (OLI) reflectance data, addressing influences with a Random Forest (RF) classification approach. The RF-based were implementing various combinations input variables, incorporating top-of-atmosphere (TOA) reflectance, normalized difference indices snow, water, bare ice, factors such as aerosol optical depth, vapor content, ozone concentration from Moderate Resolution Imaging Spectroradiometer observations, well surface elevation GLO-30 digital model. RF model all variables achieved excellent performance snow-covered snow-free an overall accuracy kappa coefficient exceeding 98.4% 0.98 test samples, higher than 83.7% 0.75 when compared against reference maps generated manually interpreting under conditions. corrected was also developed, but it showed very low conditions heavily contaminated vapor. Aerosol depth content identified most important variables. demonstrates that despite contamination, can be effectively monitoring applying machine learning auxiliary data to mitigate effects.

Язык: Английский

Процитировано

2
Validating Digital Earth Australia NBART for the Landsat 9 Underfly of Landsat 8 DOI Creative Commons
G.F. Byrne, Mark Broomhall, Andrew Walsh

и другие.

Remote Sensing, Год журнала: 2024, Номер 16(7), С. 1233 - 1233

Опубликована: Март 31, 2024

In recent years, Geoscience Australia has undertaken a successful continental-scale validation program, targeting Landsat and Sentinel analysis-ready data surface reflectance products. The field model used for this program was successfully built upon earlier studies, the measurement uncertainties associated with these protocols have been quantified published. As consequence, Australian earth observation community well-prepared to respond United States Geological Survey (USGS) call collaborators 2021 8 (L8) 9 (L9) underfly. Despite number of challenges, seven datasets were captured across five sites. there only single 100% overlap transit Australia, country amidst strong La Niña climate cycle, it decided deploy teams two available overpasses 15% side lap. sites encompassed rangelands, chenopod shrublands, large inland lake. Apart from instrument problems at one site, good weather enabled capture high-quality allowing meaningful comparisons between radiometric performance L8 L9, as well USGS processing models. Duplicate (cross-calibration) spectral sampling different provides evidence protocol reliability, while off-nadir view L9 over water site better compare atmospheric correction

Язык: Английский

Процитировано

1
Cropping intensity mapping in Sentinel-2 and Landsat-8/9 remote sensing data using temporal transfer of a stacked ensemble machine learning model within google earth engine DOI Creative Commons

Marziyeh Majnoun Hosseini,

Mohammad Javad Valadan Zoej, Alireza Taheri Dehkordi

и другие.

Geocarto International, Год журнала: 2024, Номер 39(1)

Опубликована: Янв. 1, 2024

This article aimed to map Cropping Intensity Patterns (CIPs) in the southwest region of Iran using Google Earth Engine and monthly composites Sentinel-2 Landsat-8/9 data. To detect CIPs with high inter- intra-class variability crops, a heterogeneous Stack ensemble machine learning model was developed. The incorporated Minimum Distance (MD) approach as meta-classifier, combining multiple base models, including Support Vector Machines (SVM), Random Forest (RF), Classification Regression Trees (CART), Gradient Boosted (GBT). In 2021, trained evaluated Ground Truth (GT) samples from same year, achieving an Overall Accuracy (OA) 94.24%. performance surpassed models by about 4% OA also reflected detection accuracies, User's (UA), Producer's (PA), F1-score, target classes. Subsequently, stack temporally transferred generate CIP maps for other years. achieved OAs 91.82% 90.97% based on GT 2020 2022, respectively. Finally, time series (2019-2023) were utilized Cellular Automata-Markov forecast 2024.

Язык: Английский

Процитировано

1
Continuity of Top-of-Atmosphere, Surface, and Nadir BRDF-Adjusted Reflectance and NDVI between Landsat-8 and Landsat-9 OLI over China Landscape DOI Creative Commons
Yuanheng Sun, Binyu Wang,

Senlin Teng

и другие.

Remote Sensing, Год журнала: 2023, Номер 15(20), С. 4948 - 4948

Опубликована: Окт. 13, 2023

The successful launch of Landsat-9 marks a significant achievement in preserving the data legacy and ensuring continuity Landsat’s calibrated Earth observations. This study comprehensively assesses reflectance Normalized Difference Vegetation Index (NDVI) between Landsat-8 Operational Land Imagers (OLIs) over diverse Chinese landscapes. It reveals that sensor discrepancies minimally impact NDVI consistency. Although Landsat-9’s top-of-atmosphere (TOA) is slightly lower than Landsat-8, small root-mean-square errors (RMSEs) ranging from 0.0102 to 0.0248 for VNIR SWIR bands (and larger RMSE at 0.0422) fall within acceptable ranges observation applications. Applying atmospheric corrections markedly enhances uniformity brings regression slopes closer unity. Further, Bidirectional Reflectance Distribution Function (BRDF) adjustments improve comparability, measurement reliability, maintains robust consistency across various types, time series, land cover classes. These findings affirm success achieving Landsat program, allowing interchangeable use OLI purposes. Future research may explore specific correlations different vegetation types seasons while integrating complementary platforms, such as Sentinel-2, enhance understanding factors.

Язык: Английский

Процитировано

3
Intercomparison of Landsat Operational Land Imager and Terra Advanced Spaceborne Thermal Emission and Reflection Radiometer Radiometric Calibrations Using Radiometric Calibration Network Data DOI Creative Commons
Mehran Yarahmadi,

Kurtis J. Thome,

Brian N. Wenny

и другие.

Remote Sensing, Год журнала: 2024, Номер 16(2), С. 400 - 400

Опубликована: Янв. 19, 2024

This paper presents a comprehensive intercomparison study investigating the radiometric performance of and concurrence among Advanced Spaceborne Thermal Emission Reflection Radiometer (ASTER), Landsat 8 Operational Land Imager (L8 OLI), 9 OLI (L9 OLI) instruments. leverages data sourced from Radiometric Calibration Network (RadCalNet) focuses on spectral bands relevant for vegetation analysis land cover classification, encompassing thorough assessment quality, uncertainties, underlying influencing factors. study’s outcomes underscore efficacy RadCalNet in evaluating precision reliability remote sensing data, offering valuable insights into strengths limitations ASTER, L8 OLI, L9 OLI. These serve as foundation informed decision making environmental monitoring resource management, highlighting pivotal role gauging sensors. Results sites, namely Railroad Valley Playa Gobabeb, show their possible suitability sensors with spatial resolutions down to 15 m. The results indicate that measurements both ASTER closely align RadCalNet, observed agreement falls comfortably within total range potential errors associated test site information.

Язык: Английский

Процитировано

0
Spatial and Temporal Variations of Total Suspended Matter Concentration during the Dry Season in Dongting Lake in the Past 35 Years DOI Creative Commons

Yifan Shao,

Qian Shen,

Yue Yao

и другие.

Remote Sensing, Год журнала: 2024, Номер 16(18), С. 3509 - 3509

Опубликована: Сен. 21, 2024

Dongting Lake is the second largest freshwater lake in China, located middle reaches of Yangtze River. Since 21st century, it has faced intensified human activities, particularly Three Gorges Dam impoundment and sand mining. The water quality significantly changed due to activities climate change. Currently, quantitative studies on spatial–temporal variations total suspended matter (TSM) during Lake’s dry season impacts its concentration are lacking. This study utilizes Landsat-5 TM Landsat-8 OLI data estimate changes TSM from 1986 2021, analyzing their driving mechanisms. By evaluating atmospheric calibration accuracy model precision metrics, we select a based ratio red green band, achieving an R2 0.84, RMSE 18.94 mg/L, MRE 27.32%. Applying this images, map distribution spatial pattern inter-annual variation, further investigate natural factors concentration. Our results show following: (1) From ranges 0 200 mg/L Lake, with area-wide average value between 41.61 75.44 mg/L. (2) 2021 correlated level. Before 2006, correlates positively, but no significant correlation exists 2006 onward. (3) onward, mean notably decreased compared that before likely Dam, while our analysis indicates positive mining intensity period. highlights influence season, providing valuable insights for related research similar lakes.

Язык: Английский

Процитировано

0
Performance Assessment of Landsat-9 Atmospheric Correction Methods in Global Aquatic Systems DOI Creative Commons

Aoxiang Sun,

Shuangyan He, Yanzhen Gu

и другие.

Remote Sensing, Год журнала: 2024, Номер 16(23), С. 4517 - 4517

Опубликована: Дек. 2, 2024

The latest satellite in the Landsat series, Landsat-9, was successfully launched on 27 September 2021, equipped with Operational Land Imager-2 (OLI-2) sensor, continuing legacy of OLI/Landsat-8. To evaluate uncertainties water surface reflectance derived from OLI-2, this study conducts a comprehensive performance assessment six atmospheric correction (AC) methods—DSF, C2RCC, iCOR, L2gen (NIR-SWIR1), (NIR-SWIR2), and Polymer—using in-situ measurements 14 global sites, including 13 AERONET-OC stations 1 MOBY station, collected between 2021 2023. Error analysis shows that (NIR-SWIR1) (RMSE ≤ 0.0017 sr−1, SA = 6.33°) (NIR-SWIR2) 0.0019 6.38°) provide best results across four visible bands, demonstrating stable different optical types (OWTs) ranging clear to turbid water. Following these are C2RCC 0.0030 5.74°) Polymer 0.0027 7.76°), DSF 0.0058 11.33°) iCOR 0.0051 12.96°) showing poorest results. By comparing uncertainty consistency Landsat-9 Sentinel-2A/B (MSI) S-NPP/NOAA20 (VIIRS), show OLI-2 has similar MSI VIIRS blue, blue-green, green RMSE differences within 0.0002 sr−1. In red band, lower than those but higher VIIRS, an difference about 0.0004 Overall, data processed using reliable high making it suitable for integrating multi-satellite observations enhance coastal color monitoring.

Язык: Английский

Процитировано

0