The influence of land cover-related changes on the NDVI-based satellite agricultural drought indices. Yagci, A. L., Di, L., & Deng, M. In 2014 IEEE Geoscience and Remote Sensing Symposium, pages 2054–2057, July, 2014.
doi  abstract   bibtex   
Drought is a natural climatic event that often causes sharp declines in agricultural production. In recent years, drought indices based on remote sensing products have been developed on the premise that photosynthetic rate of vegetation slows down under drought/water stress, and this can be accurately tracked by the satellite data and methods. The Normalized Difference Vegetation Index (NDVI) is the most popular index with the long historical record to monitor terrestrial vegetation state around the world. It has been suggested that drought-induced NDVI decline can be confused with non-drought-related NDVI decline (e.g., fire, flood, land cover/land use change, pest infestation) in the NDVI-based drought method. To investigate the effect of land cover-related changes on the NDVI-based drought indices, we selected the Vegetation Condition Index (VCI), a popular NDVI-based drought index. We found that deforestation (i.e., land cover change) is falsely classified as drought in the VCI method, hence producing the false drought signals during the non-drought years. However, these false drought signals can be eliminated from drought maps with the help of spatial filters (e.g., median filter) because they are small scale signals relative to the study area size. Furthermore, the rotation of agricultural crops (e.g., crop rotation between corn and soybean) reduces the accuracy of drought reporting when crop rotation is not considered in the drought index computation because crop rotation annually accounts for over 50% agricultural land in Iowa, it has a significant impact on the NDVI-based drought methods. In conclusion, it can be said that the influence of land cover-related changes on the NDVI-based drought indicators is proportional to the size of non-drought related changes relative to the study area.
@inproceedings{yagci_influence_2014,
	title = {The influence of land cover-related changes on the {NDVI}-based satellite agricultural drought indices},
	doi = {10.1109/IGARSS.2014.6946868},
	abstract = {Drought is a natural climatic event that often causes sharp declines in agricultural production. In recent years, drought indices based on remote sensing products have been developed on the premise that photosynthetic rate of vegetation slows down under drought/water stress, and this can be accurately tracked by the satellite data and methods. The Normalized Difference Vegetation Index (NDVI) is the most popular index with the long historical record to monitor terrestrial vegetation state around the world. It has been suggested that drought-induced NDVI decline can be confused with non-drought-related NDVI decline (e.g., fire, flood, land cover/land use change, pest infestation) in the NDVI-based drought method. To investigate the effect of land cover-related changes on the NDVI-based drought indices, we selected the Vegetation Condition Index (VCI), a popular NDVI-based drought index. We found that deforestation (i.e., land cover change) is falsely classified as drought in the VCI method, hence producing the false drought signals during the non-drought years. However, these false drought signals can be eliminated from drought maps with the help of spatial filters (e.g., median filter) because they are small scale signals relative to the study area size. Furthermore, the rotation of agricultural crops (e.g., crop rotation between corn and soybean) reduces the accuracy of drought reporting when crop rotation is not considered in the drought index computation because crop rotation annually accounts for over 50\% agricultural land in Iowa, it has a significant impact on the NDVI-based drought methods. In conclusion, it can be said that the influence of land cover-related changes on the NDVI-based drought indicators is proportional to the size of non-drought related changes relative to the study area.},
	booktitle = {2014 {IEEE} {Geoscience} and {Remote} {Sensing} {Symposium}},
	author = {Yagci, A. L. and Di, L. and Deng, M.},
	month = jul,
	year = {2014},
	keywords = {agricultural crop rotation, agricultural land, agricultural production, Agriculture, climatology, corn, Crop rotation, crops, deforestation, Drought, drought index computation, drought maps, drought reporting, false drought signals, fire, flood, floods, geophysical signal processing, hydrology, Indexes, Iowa, land cover, Land Cover Change, land cover-related changes, land use, land use change, median filter, median filters, Meteorology, Monitoring, natural climatic event, NDVI, NDVI-based drought method, NDVI-based drought methods, NDVI-based satellite agricultural drought indices, nondrought related changes, nondrought years, nondrought-related NDVI decline, Normalized Difference Vegetation Index, pest infestation, photosynthesis, Remote sensing, remote sensing products, satellite data, Satellites, soybean, spatial filters, terrain mapping, terrestrial vegetation state, VCI, vegetation, vegetation condition index, Vegetation Condition Index, Vegetation mapping, vegetation photosynthetic rate, water stress, wildfires},
	pages = {2054--2057},
	file = {IEEE Xplore Abstract Record:/Volumes/mini-disk1/Google Drive/_lib/zotero/storage/2VA6TYCI/6946868.html:text/html;IEEE Xplore Full Text PDF:/Volumes/mini-disk1/Google Drive/_lib/zotero/storage/MDG7V7QG/Yagci et al. - 2014 - The influence of land cover-related changes on the.pdf:application/pdf}
}
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