Detecting Spatiotemporal Changes of CornDevelopmental Stages in the U.S. Corn BeltUsing MODIS WDRVI Data
2011
- 902Usage
Metric Options: CountsSelecting the 1-year or 3-year option will change the metrics count to percentiles, illustrating how an article or review compares to other articles or reviews within the selected time period in the same journal. Selecting the 1-year option compares the metrics against other articles/reviews that were also published in the same calendar year. Selecting the 3-year option compares the metrics against other articles/reviews that were also published in the same calendar year plus the two years prior.
Example: if you select the 1-year option for an article published in 2019 and a metric category shows 90%, that means that the article or review is performing better than 90% of the other articles/reviews published in that journal in 2019. If you select the 3-year option for the same article published in 2019 and the metric category shows 90%, that means that the article or review is performing better than 90% of the other articles/reviews published in that journal in 2019, 2018 and 2017.
Citation Benchmarking is provided by Scopus and SciVal and is different from the metrics context provided by PlumX Metrics.
Example: if you select the 1-year option for an article published in 2019 and a metric category shows 90%, that means that the article or review is performing better than 90% of the other articles/reviews published in that journal in 2019. If you select the 3-year option for the same article published in 2019 and the metric category shows 90%, that means that the article or review is performing better than 90% of the other articles/reviews published in that journal in 2019, 2018 and 2017.
Citation Benchmarking is provided by Scopus and SciVal and is different from the metrics context provided by PlumX Metrics.
Metrics Details
- Usage902
- Downloads874
- Abstract Views28
Article Description
The dates of crop developmental stages are important variables for many applications including assessment of the impact of abnormal weather on crop yield. Time-series 250-m vegetation-index (VI) data acquired from the Moderate Resolution Imaging Spectroradiometer (MODIS) provide valuable information for monitoring the spatiotemporal changes of corn growth across large geographic areas. The goal of this study is to evaluate the performance of a new crop phenology detection method, namely, two-step filtering (TSF), for revealing the spatiotemporal pattern of specific corn developmental stages (early vegetative: V2.5; silking: R1; dent: R5; mature: R6) over an eight-year period (2001–2008) across Iowa, Illinois, and Indiana using MODIS derived Wide Dynamic Range VI data. Weekly crop progress reports produced by the U.S. Department of Agriculture National Agricultural Statistics Service (NASS) were used to assess the accuracy of TSF-based estimates of corn developmental stages. The results showed that the corn developmental stages could be estimated with high accuracy (the root mean squared error ranged from 4.1 to 5.5 days, the determination coefficient ranged from 0.66 to 0.84, and the coefficient of variation ranged from 2.1% to 3.7%) based on NASS-derived statistics on an agricultural statistics district level. In particular, the annual changes in the spatiotemporal patterns of the estimated silking stage had a high level of agreement with those of the NASS-derived statistics. These results suggested that the TSF method could provide local-scale information of corn phenological stages, which had an advantage over the NASS-derived statistics particularly in terms of the spatial resolution.
Bibliographic Details
Provide Feedback
Have ideas for a new metric? Would you like to see something else here?Let us know