Empirical cross sensor comparison of Sentinel-2A and 2B MSI, Landsat-8 OLI, and Landsat-7 ETM+ top of atmosphere spectral characteristics over the conterminous United States

Chastain, Robert A.; Housman, Ian W.; Goldstein, Joshua; Finco, Mark; Tenneson, Karis

doi:10.1016/j.rse.2018.11.012

Cited by 133 publications

(126 citation statements)

References 55 publications

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“…Although efforts have been made in the radiometric and geometric calibration of the Landsat and S2 missions so that their bands could be compatible [20], small spectral differences still exist [11,20,33]. We adjusted the six Landsat bands (blue, green, red, nir, swir1, and swir2) using cross-sensor transformation coefficients (Table 1) derived from Chastain et al, (2019) [33]. Chastain et al, (2019) [33] used absolute difference metrics and major axis linear regression analysis over 10,000 image pairs across the conterminous United States to obtain these transformation coefficients.…”

Section: Band Adjustmentmentioning

confidence: 99%

“…We adjusted the six Landsat bands (blue, green, red, nir, swir1, and swir2) using cross-sensor transformation coefficients (Table 1) derived from Chastain et al, (2019) [33]. Chastain et al, (2019) [33] used absolute difference metrics and major axis linear regression analysis over 10,000 image pairs across the conterminous United States to obtain these transformation coefficients.…”

Section: Band Adjustmentmentioning

confidence: 99%

“…These models were implemented in GEE by Poortinga et al, (2019) [32]. We adjust the Landsat Top of Atmosphere (TOA) bands (blue, green, red, nir, swir1, swir2) according to S2 level 1C (TOA) values using cross-sensor transformation coefficients derived from Chastain et al, (2019) [33] (Table 1). In Section 3.1 we describe several tests to assess and evaluate the performance of each followed step.…”

Section: Introductionmentioning

confidence: 99%

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Harmonization of Landsat and Sentinel 2 for Crop Monitoring in Drought Prone Areas: Case Studies of Ninh Thuan (Vietnam) and Bekaa (Lebanon)

Nguyen

Baez-Villanueva

Bui³

et al. 2020

Remote Sensing

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Proper satellite-based crop monitoring applications at the farm-level often require near-daily imagery at medium to high spatial resolution. The combination of data from different ongoing satellite missions Sentinel 2 (ESA) and Landsat 7/8 (NASA) provides this unprecedented opportunity at a global scale; however, this is rarely implemented because these procedures are data demanding and computationally intensive. This study developed a robust stream processing for the harmonization of Landsat 7, Landsat 8 and Sentinel 2 in the Google Earth Engine cloud platform, connecting the benefit of coherent data structure, built-in functions and computational power in the Google Cloud. The harmonized surface reflectance images were generated for two agricultural schemes in Bekaa (Lebanon) and Ninh Thuan (Vietnam) during 2018–2019. We evaluated the performance of several pre-processing steps needed for the harmonization including the image co-registration, Bidirectional Reflectance Distribution Functions correction, topographic correction, and band adjustment. We found that the misregistration between Landsat 8 and Sentinel 2 images varied from 10 m in Ninh Thuan (Vietnam) to 32 m in Bekaa (Lebanon), and posed a great impact on the quality of the final harmonized data set if not treated. Analysis of a pair of overlapped L8-S2 images over the Bekaa region showed that, after the harmonization, all band-to-band spatial correlations were greatly improved. Finally, we demonstrated an application of the dense harmonized data set for crop mapping and monitoring. An harmonic (Fourier) analysis was applied to fit the detected unimodal, bimodal and trimodal shapes in the temporal NDVI patterns during one crop year in Ninh Thuan province. The derived phase and amplitude values of the crop cycles were combined with max-NDVI as an R-G-B false composite image. The final image was able to highlight croplands in bright colors (high phase and amplitude), while the non-crop areas were shown with grey/dark (low phase and amplitude). The harmonized data sets (with 30 m spatial resolution) along with the Google Earth Engine scripts used are provided for public use.

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Section: Band Adjustmentmentioning

confidence: 99%

Section: Band Adjustmentmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Harmonization of Landsat and Sentinel 2 for Crop Monitoring in Drought Prone Areas: Case Studies of Ninh Thuan (Vietnam) and Bekaa (Lebanon)

Nguyen

Baez-Villanueva

Bui³

et al. 2020

Remote Sensing

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“…Additional image pre-processing steps were applied to account for sensor, solar, atmospheric, and topographic distortions (Young et al, 2017) and create a reliable and consistent time series. We applied shadow and cloud removal (Housman et al, 2018;Chastain et al, 2019), a bidirectional reflectance distribution function (BRDF) (Lucht et al, 2000;Roy et al, 2008Roy et al, , 2016bRoy et al, , 2017Gao et al, 2014) and topographic correction (Smith et al, 1980;Justice et al, 1981;Teillet et al, 1982;Soenen et al, 2005).…”

Section: Annual Satellite Image Compositesmentioning

confidence: 99%

Linking Earth Observations for Assessing the Food Security Situation in Vietnam: A Landscape Approach

Poortinga¹,

Nguyen

Tenneson³

et al. 2019

Front. Environ. Sci.

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Land cover change and its impact on food security is a topic that has major implications for development in population-dense Southeast Asia. The main drivers of forest loss include the expansion of agriculture and plantation estates, growth of urban centers, extraction of natural resources, and water infrastructure development. The design and implementation of appropriate land use policies requires accurate and timely information on land cover dynamics to account for potential political, economical, and agricultural consequences. Therefore, SERVIR-Mekong led the collaborative development of a Regional Land Cover Monitoring System (RLCMS) with key regional stakeholders across the greater Mekong region. Through this effort, a modular system was used to create yearly land cover maps for the period 1988-2017. In this study, we compared this 30-year land cover time-series with Vietnam national forest resources and agricultural productivity statistics. We used remote sensing-derived land cover products to quantify landscape changes and linked those with food availability, one of food security dimension, from a landscape approach perspective. We found that agricultural production has soared while the coverage of agricultural areas has remained relatively stable. Land cover change dynamics coincide with important legislation regarding environmental management and sustainable development strategies in Vietnam. Our findings indicate that Vietnam has made major steps toward improving its' food security. We demonstrate that RLCMS is a valuable tool for evaluating the relationship between policies and their impacts on food security, ecosystem services and natural capital.

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“…Wide coverage, rapid updating, and spatiotemporal synchronization are some of the most important features of remotely sensed images [1]. In addition, the policy on providing free access to high-resolution satellite data archives (mainly Landsat and Copernicus) coupled with computational capabilities of platforms, such as Google Earth Engine (GEE) [2], open up new frontiers in change detection approaches [3,4].…”

Section: Introductionmentioning

confidence: 99%

Towards a Tool for Early Detection and Estimation of Forest Cuttings by Remotely Sensed Data

Puletti

Bascietto²

2019

Land

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Knowing the extent and frequency of forest cuttings over large areas is crucial for forest inventories and monitoring. Remote sensing has amply proved its ability to detect land cover changes, particularly in forested areas. Among various strategies, those focusing on mapping using classification approaches of remotely sensed time series are the most frequently used. The main limit of such approaches stems from the difficulty in perfectly and unambiguously classifying each pixel, especially over wide areas. The same procedure is of course simpler if performed over a single pixel. An automated method for identifying forest cuttings over a predefined network of sampling points (IUTI) using multitemporal Sentinel 2 imagery is described. The method employs normalized difference vegetation index (NDVI) growth trajectories to identify the presence of disturbances caused by forest cuttings using a large set of points (i.e., 1580 "forest" points). We applied the method using a total of 51 S2 images extracted from the Google Earth Engine over two years (2016 and 2017) in an area of about 70 km 2 in Tuscany, central Italy. Author Contributions: Conceptualization and Methodology N.P.; Formal Analysis N.P. and M.B.; Writing-Original Draft Preparation, Review and Editing, N.P. and M.B. Funding: This research received no external funding. Conflicts of Interest:The authors declare no conflicts of interest.

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Empirical cross sensor comparison of Sentinel-2A and 2B MSI, Landsat-8 OLI, and Landsat-7 ETM+ top of atmosphere spectral characteristics over the conterminous United States

Cited by 133 publications

References 55 publications

Harmonization of Landsat and Sentinel 2 for Crop Monitoring in Drought Prone Areas: Case Studies of Ninh Thuan (Vietnam) and Bekaa (Lebanon)

Harmonization of Landsat and Sentinel 2 for Crop Monitoring in Drought Prone Areas: Case Studies of Ninh Thuan (Vietnam) and Bekaa (Lebanon)

Linking Earth Observations for Assessing the Food Security Situation in Vietnam: A Landscape Approach

Towards a Tool for Early Detection and Estimation of Forest Cuttings by Remotely Sensed Data

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