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Remote Sensing
Volume 12
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10.3390/rs12010087
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Article

Time Delay Evaluation on the Water-Leaving Irradiance Retrieved from Empirical Models and Satellite Imagery

by
Peter Otto
1,
Ramiro Vallejo-Rodríguez
2,
Saskia Keesstra
1,3,
Elizabeth León-Becerril
2,
José de Anda
2,
Leonel Hernández-Mena
2,
Jorge del Real-Olvera
2 and
José de Jesús Díaz-Torres
2,*
1
Soil, Water and Land Use Team, Wageningen Environmental Research, Wageningen University and Research, Droevendaalsesteeg 3, 6708PB Wageningen, The Netherlands
2
Center for Research and Assistance in Technology and Design of the State of Jalisco, 800 Normalistas Ave. Colinas de la Normal, 44270 Guadalajara, Jalisco, Mexico
3
Civil, Surveying and Environmental Engineering, The University of Newcastle, Callaghan 2308, Australia
*
Author to whom correspondence should be addressed.
Remote Sens. 2020, 12(1), 87; https://doi.org/10.3390/rs12010087
Submission received: 14 November 2019 / Revised: 11 December 2019 / Accepted: 24 December 2019 / Published: 25 December 2019
(This article belongs to the Section Environmental Remote Sensing)
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Abstract

Temporal delays and spatial randomness between ground-based data and satellite overpass involve important deviations between the empirical model output and real data; these are factors poorly considered in the model calibration. The inorganic matter-generated turbidity in Lake Chapala (Mexico) was taken as a study case to expose the influence of such factors. Ground-based data from this study and historical records were used as references. We take advantage of the at-surface reflectance from Landsat-8, sun-glint corrections, a reduced NIR-band range, and null organic matter incidence in these wavelengths to diminish the physical phenomena-related radiometric artifacts; leaving the spatio-temporal relationships as the principal factor inducing the model uncertainty. Non-linear correlations were assessed to calibrate the best empirical model; none of them presented a strong relationship (<73%), including that based on hourly delays. This last model had the best predictability only for the summer-fall season, explaining 71% of the turbidity variation in 2016, and 59% in 2017, with RMSEs < 24%. The instantaneous turbidity maps depicted the hydrodynamic complexity of the lake, highlighting a strong component of spatial randomness associated with the temporal delays. Reasonably, robust empirical models will be developed if several dates and sampling-sites are synchronized with more satellite overpasses.
Keywords: temporal delay; spatial randomness; empirical model; turbidity; Landsat-8; NIR reflectance; subtropical zone; shallow lake; Lake Chapala temporal delay; spatial randomness; empirical model; turbidity; Landsat-8; NIR reflectance; subtropical zone; shallow lake; Lake Chapala
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MDPI and ACS Style

Otto, P.; Vallejo-Rodríguez, R.; Keesstra, S.; León-Becerril, E.; de Anda, J.; Hernández-Mena, L.; del Real-Olvera, J.; Díaz-Torres, J.d.J. Time Delay Evaluation on the Water-Leaving Irradiance Retrieved from Empirical Models and Satellite Imagery. Remote Sens. 2020, 12, 87. https://doi.org/10.3390/rs12010087

AMA Style

Otto P, Vallejo-Rodríguez R, Keesstra S, León-Becerril E, de Anda J, Hernández-Mena L, del Real-Olvera J, Díaz-Torres JdJ. Time Delay Evaluation on the Water-Leaving Irradiance Retrieved from Empirical Models and Satellite Imagery. Remote Sensing. 2020; 12(1):87. https://doi.org/10.3390/rs12010087

Chicago/Turabian Style

Otto, Peter, Ramiro Vallejo-Rodríguez, Saskia Keesstra, Elizabeth León-Becerril, José de Anda, Leonel Hernández-Mena, Jorge del Real-Olvera, and José de Jesús Díaz-Torres. 2020. "Time Delay Evaluation on the Water-Leaving Irradiance Retrieved from Empirical Models and Satellite Imagery" Remote Sensing 12, no. 1: 87. https://doi.org/10.3390/rs12010087

APA Style

Otto, P., Vallejo-Rodríguez, R., Keesstra, S., León-Becerril, E., de Anda, J., Hernández-Mena, L., del Real-Olvera, J., & Díaz-Torres, J. d. J. (2020). Time Delay Evaluation on the Water-Leaving Irradiance Retrieved from Empirical Models and Satellite Imagery. Remote Sensing, 12(1), 87. https://doi.org/10.3390/rs12010087

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