Next Article in Journal
City-Scale Mapping of Urban Façade Color Using Street-View Imagery
Next Article in Special Issue
Understanding Growth Dynamics and Yield Prediction of Sorghum Using High Temporal Resolution UAV Imagery Time Series and Machine Learning
Previous Article in Journal
Leveraging River Network Topology and Regionalization to Expand SWOT-Derived River Discharge Time Series in the Mississippi River Basin
Previous Article in Special Issue
Development of a Mobile Platform for Field-Based High-Throughput Wheat Phenotyping
 
 
Article

Top-of-Atmosphere Retrieval of Multiple Crop Traits Using Variational Heteroscedastic Gaussian Processes within a Hybrid Workflow

1
Image Processing Laboratory (IPL), Parc Científic, Universitat de València, 46980 Paterna, Spain
2
Department of Geography, Ludwig-Maximilians-Universität München (LMU), Luisenstr. 37, 80333 Munich, Germany
3
Magellium, 31520 Toulouse, France
4
Secretary of Research and Postgraduate, CONACYT-UAN, Tepic 63155, Mexico
*
Author to whom correspondence should be addressed.
Academic Editors: Damiano Gianelle and Roshanak Darvishzadeh
Remote Sens. 2021, 13(8), 1589; https://doi.org/10.3390/rs13081589
Received: 25 February 2021 / Revised: 6 April 2021 / Accepted: 12 April 2021 / Published: 20 April 2021
(This article belongs to the Special Issue Remote and Proximal Assessment of Plant Traits)
In support of cropland monitoring, operational Copernicus Sentinel-2 (S2) data became available globally and can be explored for the retrieval of important crop traits. Based on a hybrid workflow, retrieval models for six essential biochemical and biophysical crop traits were developed for both S2 bottom-of-atmosphere (BOA) L2A and S2 top-of-atmosphere (TOA) L1C data. A variational heteroscedastic Gaussian process regression (VHGPR) algorithm was trained with simulations generated by the combined leaf-canopy reflectance model PROSAILat the BOA scale and further combined with the Second Simulation of a Satellite Signal in the Solar Spectrum (6SV) atmosphere model at the TOA scale. Established VHGPR models were then applied to S2 L1C and L2A reflectance data for mapping: leaf chlorophyll content (Cab), leaf water content (Cw), fractional vegetation coverage (FVC), leaf area index (LAI), and upscaled leaf biochemical compounds, i.e., LAI ∗ Cab (laiCab) and LAI ∗ Cw (laiCw). Estimated variables were validated using in situ reference data collected during the Munich-North-Isar field campaigns within growing seasons of maize and winter wheat in the years 2017 and 2018. For leaf biochemicals, retrieval from BOA reflectance slightly outperformed results from TOA reflectance, e.g., obtaining a root mean squared error (RMSE) of 6.5 μμg/cm2 (BOA) vs. 8 μμg/cm2 (TOA) in the case of Cab. For the majority of canopy-level variables, instead, estimation accuracy was higher when using TOA reflectance data, e.g., with an RMSE of 139 g/m2 (BOA) vs. 113 g/m2 (TOA) for laiCw. Derived maps were further compared against reference products obtained from the ESA Sentinel Application Platform (SNAP) Biophysical Processor. Altogether, the consistency between L1C and L2A retrievals confirmed that crop traits can potentially be estimated directly from TOA reflectance data. Successful mapping of canopy-level crop traits including information about prediction confidence suggests that the models can be transferred over spatial and temporal scales and, therefore, can contribute to decision-making processes for cropland management. View Full-Text
Keywords: biophysical and biochemical traits; top-of-atmosphere reflectance; Sentinel-2; variational heteroscedastic Gaussian process regression; hybrid model biophysical and biochemical traits; top-of-atmosphere reflectance; Sentinel-2; variational heteroscedastic Gaussian process regression; hybrid model
Show Figures

Graphical abstract

MDPI and ACS Style

Estévez, J.; Berger, K.; Vicent, J.; Rivera-Caicedo, J.P.; Wocher, M.; Verrelst, J. Top-of-Atmosphere Retrieval of Multiple Crop Traits Using Variational Heteroscedastic Gaussian Processes within a Hybrid Workflow. Remote Sens. 2021, 13, 1589. https://doi.org/10.3390/rs13081589

AMA Style

Estévez J, Berger K, Vicent J, Rivera-Caicedo JP, Wocher M, Verrelst J. Top-of-Atmosphere Retrieval of Multiple Crop Traits Using Variational Heteroscedastic Gaussian Processes within a Hybrid Workflow. Remote Sensing. 2021; 13(8):1589. https://doi.org/10.3390/rs13081589

Chicago/Turabian Style

Estévez, José, Katja Berger, Jorge Vicent, Juan Pablo Rivera-Caicedo, Matthias Wocher, and Jochem Verrelst. 2021. "Top-of-Atmosphere Retrieval of Multiple Crop Traits Using Variational Heteroscedastic Gaussian Processes within a Hybrid Workflow" Remote Sensing 13, no. 8: 1589. https://doi.org/10.3390/rs13081589

Find Other Styles
Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.

Article Access Map by Country/Region

1
Back to TopTop