Next Article in Journal
Recent Progress in Quantitative Land Remote Sensing in China
Next Article in Special Issue
Using Time Series of High-Resolution Planet Satellite Images to Monitor Grapevine Stem Water Potential in Commercial Vineyards
Previous Article in Journal
Monitoring the Impact of Land Cover Change on Surface Urban Heat Island through Google Earth Engine: Proposal of a Global Methodology, First Applications and Problems
Previous Article in Special Issue
Deep Recurrent Neural Network for Agricultural Classification using multitemporal SAR Sentinel-1 for Camargue, France
Open AccessArticle

Assessing the Variability of Corn and Soybean Yields in Central Iowa Using High Spatiotemporal Resolution Multi-Satellite Imagery

1
USDA, Agricultural Research Service, Hydrology and Remote Sensing Laboratory, Beltsville, MD 20705, USA
2
USDA, National Agricultural Statistics Service, Washington, DC 20250, USA
*
Author to whom correspondence should be addressed.
Remote Sens. 2018, 10(9), 1489; https://doi.org/10.3390/rs10091489
Received: 2 July 2018 / Revised: 30 August 2018 / Accepted: 14 September 2018 / Published: 18 September 2018
(This article belongs to the Special Issue High Resolution Image Time Series for Novel Agricultural Applications)
The utility of remote sensing data in crop yield modeling has typically been evaluated at the regional or state level using coarse resolution (>250 m) data. The use of medium resolution data (10–100 m) for yield estimation at field scales has been limited due to the low temporal sampling frequency characteristics of these sensors. Temporal sampling at a medium resolution can be significantly improved, however, when multiple remote sensing data sources are used in combination. Furthermore, data fusion approaches have been developed to blend data from different spatial and temporal resolutions. This paper investigates the impacts of improved temporal sampling afforded by multi-source datasets on our ability to explain spatial and temporal variability in crop yields in central Iowa (part of the U.S. Corn Belt). Several metrics derived from vegetation index (VI) time-series were evaluated using Landsat-MODIS fused data from 2001 to 2015 and Landsat-Sentinel2-MODIS fused data from 2016 and 2017. The fused data explained the yield variability better, with a higher coefficient of determination (R2) and a smaller relative mean absolute error than using a single data source alone. In this study area, the best period for the yield prediction for corn and soybean was during the middle of the growing season from day 192 to 236 (early July to late August, 1–3 months before harvest). These findings emphasize the importance of high temporal and spatial resolution remote sensing data in agricultural applications. View Full-Text
Keywords: crop yield; Landsat; Sentinel-2; MODIS; data fusion; vegetation index crop yield; Landsat; Sentinel-2; MODIS; data fusion; vegetation index
Show Figures

Graphical abstract

MDPI and ACS Style

Gao, F.; Anderson, M.; Daughtry, C.; Johnson, D. Assessing the Variability of Corn and Soybean Yields in Central Iowa Using High Spatiotemporal Resolution Multi-Satellite Imagery. Remote Sens. 2018, 10, 1489. https://doi.org/10.3390/rs10091489

AMA Style

Gao F, Anderson M, Daughtry C, Johnson D. Assessing the Variability of Corn and Soybean Yields in Central Iowa Using High Spatiotemporal Resolution Multi-Satellite Imagery. Remote Sensing. 2018; 10(9):1489. https://doi.org/10.3390/rs10091489

Chicago/Turabian Style

Gao, Feng; Anderson, Martha; Daughtry, Craig; Johnson, David. 2018. "Assessing the Variability of Corn and Soybean Yields in Central Iowa Using High Spatiotemporal Resolution Multi-Satellite Imagery" Remote Sens. 10, no. 9: 1489. https://doi.org/10.3390/rs10091489

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
Search more from Scilit
 
Search
Back to TopTop