The impact of size variations in the ground instantaneous field of view of pixels on MODIS BRDF modelling

McCamley, Geoffrey; Grant, I. F.; Jones, Simon; Bellman, Chris

doi:10.1016/j.jag.2015.01.005

Cited by 4 publications

(3 citation statements)

References 28 publications

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“…Unlike the MODIS MCD43A4 NBAR (Nadir Bidirectional Reflectance Distribution Function [BRDF]‐Adjusted Reflectance) product, which only considers reflectance variability due to sun‐sensor geometry relationships (McCamley et al. 2015), these corrections simultaneously address pixel blur (effective pixel size) and sun‐sensor geometry effects. The reflectance variability over time (i.e., varying sun‐sensor viewing geometry) due to both cross‐track and off‐nadir view angle variability is determined using per‐pixel and per‐band linear regression fits between the two view angles and raw reflectance computed across many cloud‐masked images.…”

Section: Methodsmentioning

confidence: 99%

“…One disadvantage of removing Aqua images from DSWE consideration is the inability to choose imagery from the satellite with the more ideal viewing geometry (i.e., closer to nadir) for a given day (Xin et al 2012). This constraint is of concern given how the MODIS sensor collects images of the ground surface; wide overpass swath and view angles severely inflate the ground-instantaneous fieldof-view (GIFOV) of highly off-nadir pixels (McCamley et al 2015). The MODIS swath width of 110°(up to 55°from nadir at the sensor, 65°at the surface due to Earth curvature, compared to 7.5 for Landsat) yields effective pixel dimensions up to several times those of the nominal resolution (Campagnolo and Montaño 2014).…”

Section: Dswemod Productionmentioning

confidence: 99%

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DSWEmod — The Production of High‐Frequency Surface Water Map Composites from Daily MODIS Images

Soulard

Waller

Walker

et al. 2022

J American Water Resour Assoc

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Optical satellite imagery is commonly used for monitoring surface water dynamics, but clouds and cloud shadows present challenges in assembling complete water time series. To test whether the daily revisit rate of Moderate Resolution Imaging Spectroradiometer (MODIS) satellite imagery can reduce cloud obstruction and improve high‐frequency surface water mapping, we compared map results derived from Landsat (30‐m) and MODIS (250‐m) data across the state of California for 2003–2019. We adapted the Dynamic Surface Water Extent (DSWE) model in Google Earth Engine to generate surface water map composites from MODIS imagery every 5, 10, 15, and 30 days, and compared products to monthly Landsat‐based DSWE maps. Results for DSWEmod (DSWE MODIS) in California suggest that more than 5% data loss (cloud obstruction, etc.) was present in only 2% of the 15‐day time series, as compared to 32% of the monthly Landsat DSWE time series. The five‐day DSWEmod composites averaged 8.4% obscuration in the winter months. Area estimates derived from cloud‐filtered MODIS and Landsat monthly products have the highest linear correlations compared to streamgage discharge records, suggesting that monthly scale analyses best explain the relationship between surface water area and general streamflow dynamics. Shorter‐interval DSWEmod products have lower correlations but utility for understanding the timing of surface water peaks and past flood events.

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Section: Methodsmentioning

confidence: 99%

Section: Dswemod Productionmentioning

confidence: 99%

DSWEmod — The Production of High‐Frequency Surface Water Map Composites from Daily MODIS Images

Soulard

Waller

Walker

et al. 2022

J American Water Resour Assoc

View full text Add to dashboard Cite

show abstract

“…References [46,50] propose C = 2 or C = 3 as suitable values. Higher C values increments the GIFOVmeas and the resolution of the images, ensuring a more suitable measurement process than reduced C values.…”

Section: Determination Of Underwater Fovmentioning

confidence: 99%

Autonomous Underwater Vehicles and Field of View in Underwater Operations

Ramírez

Sánchez

Papaelias

et al. 2021

JMSE

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Submarine inspections and surveys require underwater vehicles to operate in deep waters efficiently, safely and reliably. Autonomous Underwater Vehicles employing advanced navigation and control systems present several advantages. Robust control algorithms and novel improvements in positioning and navigation are needed to optimize underwater operations. This paper proposes a new general formulation of this problem together with a basic approach for the management of deep underwater operations. This approach considers the field of view and the operational requirements as a fundamental input in the development of the trajectory in the autonomous guidance system. The constraints and involved variables are also defined, providing more accurate modelling compared with traditional formulations of the positioning system. Different case studies are presented based on commercial underwater cameras/sonars, analysing the influence of the main variables in the measurement process to obtain optimal resolution results. The application of this approach in autonomous underwater operations ensures suitable data acquisition processes according to the payload installed onboard.

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Superpixel-Based Adaptive Kernel Selection for Angular Effect Normalization of Remote Sensing Images With Kernel Learning

Guo

Jia

Paull

2017

IEEE Trans. Geosci. Remote Sensing

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The impact of size variations in the ground instantaneous field of view of pixels on MODIS BRDF modelling

Cited by 4 publications

References 28 publications

DSWEmod — The Production of High‐Frequency Surface Water Map Composites from Daily MODIS Images

DSWEmod — The Production of High‐Frequency Surface Water Map Composites from Daily MODIS Images

Autonomous Underwater Vehicles and Field of View in Underwater Operations

Superpixel-Based Adaptive Kernel Selection for Angular Effect Normalization of Remote Sensing Images With Kernel Learning

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