UCL Discovery
UCL home » Library Services » Electronic resources » UCL Discovery

Crop Yield Mapping with ARC using only Optical Remote Sensing

Lewis, Philip E; Yin, Feng; Gómez-Dans, Jose Luis; Weiß, Thomas; Adam, Elhadi; (2024) Crop Yield Mapping with ARC using only Optical Remote Sensing. ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences , X-3 pp. 199-206. 10.5194/isprs-annals-x-3-2024-199-2024. Green open access

[thumbnail of Lewis_isprs-annals-X-3-2024-199-2024.pdf]
Preview
Text
Lewis_isprs-annals-X-3-2024-199-2024.pdf

Download (2MB) | Preview

Abstract

ARC is a new method to generates time series of a full set of biophysical parameters derived from optical EO. Here, we examine relationships between this ‘full’ set and maize yield. 15 Parameters per pixel are estimated over the US corn belt using ARC, to fully describe the phenology, soil, and crop status over time for typical behaviour. ARC is tested for a new model over an area of irrigated and rain-fed winter crop in South Africa. We find that care must be taken for episodic events, and robust filtering methods should be developed for ARC, but average magnitude and timing is well-expressed. We find that a robust yield model (over time and space) can be created at the county-level for maize using only EO parameters with RMSE of 704-938 kg/ha using a non-linear model, but the results are only slightly poorer if a linear model is used. It compares well to a model that also includes weather data, showing that a model can be driven by optical EO data alone.

Type: Article
Title: Crop Yield Mapping with ARC using only Optical Remote Sensing
Open access status: An open access version is available from UCL Discovery
DOI: 10.5194/isprs-annals-x-3-2024-199-2024
Publisher version: https://doi.org/10.5194/isprs-annals-x-3-2024-199-...
Language: English
Additional information: Copyright © Author(s) 2024. This work is distributed under the Creative Commons Attribution 4.0 License (https://creativecommons.org/licenses/by/4.0/).
Keywords: Remote Sensing, Radiative Transfer, Big Data, Crop Monitoring, Crop Yield, Sentinel-2 MSI, Biophysical Parameters
UCL classification: UCL
UCL > Provost and Vice Provost Offices > UCL SLASH
UCL > Provost and Vice Provost Offices > UCL SLASH > Faculty of S&HS
UCL > Provost and Vice Provost Offices > UCL SLASH > Faculty of S&HS > Dept of Geography
URI: https://discovery.ucl.ac.uk/id/eprint/10201557
Downloads since deposit
Loading...
8Downloads
Download activity - last month
Loading...
Download activity - last 12 months
Loading...
Downloads by country - last 12 months
Loading...

Archive Staff Only

View Item View Item