Remote Sensing of Corn at High Spatial, Spectral, and Temporal Resolutions
Abstract
Small Unmanned Aircraft Systems (sUAS), together with large constellations of small satellites, offer an unprecedented opportunity to monitor the growth of agricultural crops at high resolution in the spatial, spectral, and temporal domains. This project acquired remote sensing and in-situ data for test fields of corn undergoing different nitrogen and irrigation treatments at the USDA Beltsville (Maryland, USA) experimental field site during the 2018 growing season. Several sUAS collections were performed using both visible/near infrared multispectral and hyperspectral imagers (Headwall Photonics Nano; 400-1000 nm). The hyperspectral imager payload also included a Velodyne VLP-16 lidar and an uncooled thermal infrared imager. In addition, mostly cloud-free PlanetScope satellite imagery was acquired on a near daily basis. In-situ measurements of crop height, chlorophyll, leaf area index, multispectral radiometry, and color photography were also acquired as frequently as two-to-three times per week. These data provide a comprehensive data set acquired at high spatial, spectral, and temporal resolutions suitable for use in quantifying the growth of the crop, toward accurate/precise yield modeling. During harvest, the yield for the various test plots will be quantified to study the relationship between yield and parameters derived from the high resolution data. A future use of the data will be to identify requirements for future land imaging satellites in agricultural monitoring applications.
- Publication:
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AGU Fall Meeting Abstracts
- Pub Date:
- December 2018
- Bibcode:
- 2018AGUFM.B24D..08K
- Keywords:
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- 0402 Agricultural systems;
- BIOGEOSCIENCESDE: 0434 Data sets;
- BIOGEOSCIENCESDE: 0480 Remote sensing;
- BIOGEOSCIENCESDE: 1910 Data assimilation;
- integration and fusion;
- INFORMATICS