Sharpening the Sentinel-2 10 and 20 m Bands to Planetscope-0 3 m Resolution

Li, Zhongbin; Zhang, Hankui K.; Roy, David P.; Yan, Lin; Huang, Haiyan

doi:10.3390/rs12152406

Cited by 16 publications

(5 citation statements)

References 56 publications

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“…Another interesting deviation to the latitudinal trend is the earlier average ice-out at the Yukon Flats relative to Daring Lake (Figure 5c). Despite being 1.4 degrees south of the Flats, the Daring Lake sites are situated in the tundra with a lower mean annual temperature [66] than the boreal Yukon Flats sites [67]. The warmer annual temperature and consequent earlier ice-off at the Yukon Flats is consistent because ice-off is highly predicted by air temperature [68].…”

Section: Phenology Metricsmentioning

confidence: 76%

Arctic-Boreal Lake Phenology Shows a Relationship between Earlier Lake Ice-Out and Later Green-Up

et al. 2021

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Satellite remote sensing has transformed our understanding of Earth processes. One component of the Earth system where large uncertainties remain are Arctic and boreal freshwater lakes. With only short periods of open water due to annual ice cover, lake productivity in these regions is extremely sensitive to warming induced changes in ice cover. At the same time, productivity dynamics in these lakes vary enormously, even over short distances, making it difficult to understand these potential changes. A major impediment to an improved understanding of lake dynamics has been sparsely distributed field measurements, in large part due to the complexity and expense of conducting scientific research in remote northern latitudes. This project overcomes that hurdle by using a new set of ‘eyes in the sky’, the Planet Labs CubeSat fleet, to observe 35 lakes across 3 different arctic-boreal ecoregions in western North America. We extract time series of lake reflectance to identify ice-out and green-up across three years (2017–2019). We find that lakes with later ice-out have significantly faster green-ups. Our results also show ice-out varies latitudinally by 38 days from south to north, but only varies across years by ~9 days. In contrast, green-up varied between years by 22 days in addition to showing significant spatial variability. We compare PlanetScope to Sentinel-2 data and independently validate our ice-out estimates, finding an ice-out mean absolute difference (MAD) ~9 days. This study demonstrates the potential of using CubeSat imagery to monitor the timing and magnitude of ice-off and green-up at high spatiotemporal resolution.

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Section: Phenology Metricsmentioning

confidence: 76%

Arctic-Boreal Lake Phenology Shows a Relationship between Earlier Lake Ice-Out and Later Green-Up

et al. 2021

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“…4 (June 13-July 1), and even at the very beginning of plant growth (June 13). While these are most likely the imprint of underlying soil characteristics, irrigation issues, or inconsistencies during planting 69 , the spatially dense CubeSat retrievals provide detail that would likely be unresolved using coarser scale satellite platforms such as Landsat 11 and perhaps even Sentinel-2 70 . The full resolution (i.e., 3.125 m) of the CubeSat LAI maps permits an improved delineation of field sections showing optimal or suboptimal growth performance.…”

Section: Resultsmentioning

confidence: 99%

CubeSat constellations provide enhanced crop phenology and digital agricultural insights using daily leaf area index retrievals

Johansen

Ziliani

Houborg

et al. 2022

Sci Rep

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Satellite remote sensing has great potential to deliver on the promise of a data-driven agricultural revolution, with emerging space-based platforms providing spatiotemporal insights into precision-level attributes such as crop water use, vegetation health and condition and crop response to management practices. Using a harmonized collection of high-resolution Planet CubeSat, Sentinel-2, Landsat-8 and additional coarser resolution imagery from MODIS and VIIRS, we exploit a multi-satellite data fusion and machine learning approach to deliver a radiometrically calibrated and gap-filled time-series of daily leaf area index (LAI) at an unprecedented spatial resolution of 3 m. The insights available from such high-resolution CubeSat-based LAI data are demonstrated through tracking the growth cycle of a maize crop and identifying observable within-field spatial and temporal variations across key phenological stages. Daily LAI retrievals peaked at the tasseling stage, demonstrating their value for fertilizer and irrigation scheduling. An evaluation of satellite-based retrievals against field-measured LAI data collected from both rain-fed and irrigated fields shows high correlation and captures the spatiotemporal development of intra- and inter-field variations. Novel agricultural insights related to individual vegetative and reproductive growth stages were obtained, showcasing the capacity for new high-resolution CubeSat platforms to deliver actionable intelligence for precision agricultural and related applications.

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“…Quickbird was launched in October 2001 and provided imagery at 0.60 m resolution, exploiting pansharpened MS imagery and 2.4 m using MS bands [90]. With the highest spatial resolution available, pansharpening routines are widely used to optimize the spectral and spatial resolution of MS bands [91]. The availability of the first images provided by these two satellites allowed researchers to address issues that previously could not be studied from space or on the ground, such as individual mapping canopies, as shown in research focused on detecting and counting olive trees [92][93][94][95].…”

Section: Satellitesmentioning

confidence: 99%

Twenty Years of Remote Sensing Applications Targeting Landscape Analysis and Environmental Issues in Olive Growing: A Review

Messina

Modica

2022

Remote Sensing

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The olive (Olea europaea L.) is an iconic tree linked to the birth of some of the most ancient civilizations and one of the most important cultivated tree species in the Mediterranean basin. Over the last few decades, given the high socio-economic importance of the olive sector, there has been much research involving remote sensing (RS) applications in olive growing, especially in precision agriculture. This review article is part of a review that aims to cover the past, from the 2000s onwards, and the most recent applications of remote sensing (RS) in olive growing to be able to include research and all topics related to the use of RS on olive trees. As far as the use of RS platforms such as satellites, aircraft, and unmanned aerial vehicles (UAVs) in olive growing is concerned, a review of the literature showed the presence of several works devoted to it. A brief introduction on the history of the olive tree and its distribution and cultivation around the world, together with a summary of the leading RS platforms (a good portion of which are satellites) used in olive research, anticipates the discussion of four topics about olive growing that have as their common thread positive (and non-positive) impacts on the environment: preservation of olive landscape and soil erosion, identification of olive groves, olive oil mill wastewater (OOMW) and relative environmental risks, irrigation water management and the use of RS platforms for water stress monitoring. The preservation of olive groves as an element of Mediterranean identity and strategic economic resource in agriculture depends on sustainable environmental management alongside technological advances brought by precision agriculture.

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Sharpening the Sentinel-2 10 and 20 m Bands to Planetscope-0 3 m Resolution

Cited by 16 publications

References 56 publications

Arctic-Boreal Lake Phenology Shows a Relationship between Earlier Lake Ice-Out and Later Green-Up

Arctic-Boreal Lake Phenology Shows a Relationship between Earlier Lake Ice-Out and Later Green-Up

CubeSat constellations provide enhanced crop phenology and digital agricultural insights using daily leaf area index retrievals

Twenty Years of Remote Sensing Applications Targeting Landscape Analysis and Environmental Issues in Olive Growing: A Review

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