Effect of Atmospheric Corrections on NDVI: Intercomparability of Landsat 8, Sentinel-2, and UAV Sensors

Moravec, David; Komárek, Jan; Medina, Serafín López-Cuervo; Molina, Íñigo

doi:10.3390/rs13183550

Cited by 35 publications

(12 citation statements)

References 40 publications

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“…In general, atmospheric correction is not performed for UAS‐based imagery, assuming that the atmospheric conditions remain stable during imaging. Nevertheless, some attention to this aspect has been investigated in recent years (Minařík et al., 2019; Moravec et al., 2021; Schläpfer et al., 2020), especially to enhance comparability with satellite imagery.…”

Section: Discussionmentioning

confidence: 99%

Unoccupied aerial systems imagery for phenotyping in cotton, maize, soybean, and wheat breeding

et al. 2023

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High‐throughput phenotyping (HTP) with unoccupied aerial systems (UAS), consisting of unoccupied aerial vehicles (UAV; or drones) and sensor(s), is an increasingly promising tool for plant breeders and researchers. Enthusiasm and opportunities from this technology for plant breeding are similar to the emergence of genomic tools ∼30 years ago, and genomic selection more recently. Unlike genomic tools, HTP provides a variety of strategies in implementation and utilization that generate big data on the dynamic nature of plant growth formed by temporal interactions between growth and environment. This review lays out strategies deployed across four major staple crop species: cotton (Gossypium hirsutum L.), maize (Zea mays L.), soybean (Glycine max L.), and wheat (Triticum aestivum L.). Each crop highlighted in this review demonstrates how UAS‐collected data are employed to automate and improve estimation or prediction of objective phenotypic traits. Each crop section includes four major topics: (a) phenotyping of routine traits, (b) phenotyping of previously infeasible traits, (c) sample cases of UAS application in breeding, and (d) implementation of phenotypic and phenomic prediction and selection. While phenotyping of routine agronomic and productivity traits brings advantages in time and resource optimization, the most potentially beneficial application of UAS data is in collecting traits that were previously difficult or impossible to quantify, improving selection efficiency of important phenotypes. In brief, UAS sensor technology can be used for measuring abiotic stress, biotic stress, crop growth and development, as well as productivity. These applications and the potential implementation of machine learning strategies allow for improved prediction, selection, and efficiency within breeding programs, making UAS HTP a potentially indispensable asset.

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Section: Discussionmentioning

confidence: 99%

Unoccupied aerial systems imagery for phenotyping in cotton, maize, soybean, and wheat breeding

et al. 2023

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“…In addition to being a well-established and widely used method for burn severity mapping, testing the paired scene approach allowed us to evaluate the impact of atmospheric and terrain corrections to Sentinel imagery. Atmospheric correction is demonstrated to increase NDVI with both Landsat and Sentinel due to an increase in reflectivity in the nearinfrared spectrum and decrease in the visible range of the electromagnetic spectrum [82], and also affects shortwave infrared light reflectance [83], which is used in NBR indices. Additionally, use of atmospherically corrected imagery is particularly important when comparing burn severity across fires in different regions or years, as we carried out in this study, due to differences in atmospheric conditions [84].…”

Section: The Paired Scene Approachmentioning

confidence: 99%

Comparing Sentinel-2 and Landsat 8 for Burn Severity Mapping in Western North America

et al. 2022

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Accurate assessment of burn severity is a critical need for an improved understanding of fire behavior and ecology and effective post-fire management. Although NASA Landsat satellites have a long history of use for remotely sensed mapping of burn severity, the recently launched (2015 and 2017) European Space Agency Sentinel-2 satellite constellation offers increased temporal and spatial resolution with global coverage, combined with free data access. Evaluations of burn severity derived from Landsat and Sentinel generally show comparable results, but these studies only assessed a small number of fires with limited field data. We used 912 ground calibration plots from 26 fires that burned between 2016 and 2019 in western North America to compare Sentinel- and Landsat-derived burn severity estimates with the field-based composite burn index. We mapped burn severity using two methods; the well-established paired scene approach, in which a single pre- and post-fire scene are selected for each fire, and also a mean image compositing approach that automatically integrates multiple scenes using the cloud-based remote sensing platform Google Earth Engine. We found that Sentinel generally performed as well or better than Landsat for four spectral indices of burn severity, particularly when using atmospherically corrected Sentinel imagery. Additionally, we tested the effects of mapping burn severity at Sentinel’s finer spatial resolution (10 m) on estimates of the spatial complexity of stand-replacing fire, resulting in a 5% average reduction per-fire in area mapped as high-severity patch interiors (24,273 ha total) compared to mapping at the resolution of Landsat (30 m). These findings suggest Sentinel may improve ecological discrimination of fine-scale fire effects, but also warrant caution when comparing estimates of burn severity spatial patterns derived at different resolutions. Overall, these results indicate that burn severity mapping will benefit substantially from the integration of Sentinel imagery through increased imagery availability, and that Sentinel’s higher spatial resolution improves opportunities for examining finer-scale fire effects across ecosystems.

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“…Satellites play a crucial role in monitoring various phenomena on Earth. Uncertainty quantification, along with quality assurance and quality control, is crucial for satellite data, as it can be affected by atmospheric effects [ 1 , 2 ], the Sun–target–satellite angle [ 3 , 4 ], and heterogeneous landscapes [ 5 ]. These factors can create differences between satellite images and actual phenomena, potentially leading to incorrect interpretations.…”

Section: Introductionmentioning

confidence: 99%

Ground-Based NDVI Network: Early Validation Practice with Sentinel-2 in South Korea

Lee,

Lim,

Lee

et al. 2024

Sensors

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As satellite launching increases worldwide, uncertainty quantification for satellite data becomes essential. Misunderstanding satellite data uncertainties can lead to misinterpretations of natural phenomena, emphasizing the importance of validation. In this study, we established a tower-based network equipped with multispectral sensors, SD-500 and SD-600, to validate the satellite-derived NDVI product. Multispectral sensors were installed at eight long-term ecological monitoring sites managed by NIFoS. High correlations were observed between both multispectral sensors and a hyperspectral sensor, with correlations of 0.76 and 0.92, respectively, indicating that the calibration between SD-500 and SD-600 was unnecessary. High correlations, 0.8 to 0.96, between the tower-based NDVI with Sentinel-2 NDVI, were observed at most sites, while lower correlations at Anmyeon-do, Jeju, and Wando highlighting challenges in evergreen forests, likely due to shadows in complex canopy structures. In future research, we aim to analyze the uncertainties of surface reflectance in evergreen forests and develop a biome-specific validation protocol starting from site selection. Especially, the integration of tower, drone, and satellite data is expected to provide insights into the effect of complex forest structures on different spatial scales. This study could offer insights for CAS500-4 and other satellite validations, thereby enhancing our understanding of diverse ecological conditions.

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Effect of Atmospheric Corrections on NDVI: Intercomparability of Landsat 8, Sentinel-2, and UAV Sensors

Cited by 35 publications

References 40 publications

Unoccupied aerial systems imagery for phenotyping in cotton, maize, soybean, and wheat breeding

Unoccupied aerial systems imagery for phenotyping in cotton, maize, soybean, and wheat breeding

Comparing Sentinel-2 and Landsat 8 for Burn Severity Mapping in Western North America

Ground-Based NDVI Network: Early Validation Practice with Sentinel-2 in South Korea

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