Rapid Flood Progress Monitoring in Cropland with NASA SMAP. Rahman, M. S., Di, L., Yu, E., Lin, L., Zhang, C., & Tang, J. Remote Sensing, 11(2):191, January, 2019.
Rapid Flood Progress Monitoring in Cropland with NASA SMAP [link]Paper  doi  abstract   bibtex   
Research in different agricultural sectors, including in crop loss estimation during flood and yield estimation, substantially rely on inundation information. Spaceborne remote sensing has widely been used in the mapping and monitoring of floods. However, the inability of optical remote sensing to cloud penetration and the scarcity of fine temporal resolution SAR data hinder the application of flood mapping in many cases. Soil Moisture Active Passive (SMAP) level 4 products, which are model-driven soil moisture data derived from SMAP observations and are available at 3-h intervals, can offer an intermediate but effective solution. This study maps flood progress in croplands by incorporating SMAP surface soil moisture, soil physical properties, and national floodplain information. Soil moisture above the effective soil porosity is a direct indication of soil saturation. Soil moisture also increases considerably during a flood event. Therefore, this approach took into account three conditions to map the flooded pixels: a minimum of 0.05 m3m−3 increment in soil moisture from pre-flood to post-flood condition, soil moisture above the effective soil porosity, and the holding of saturation condition for the 72 consecutive hours. Results indicated that the SMAP-derived maps were able to successfully map most of the flooded areas in the reference maps in the majority of the cases, though with some degree of overestimation (due to the coarse spatial resolution of SMAP). Finally, the inundated croplands are extracted from saturated areas by Spatial Hazard Zone areas (SHFA) of Federal Emergency Management Agency (FEMA) and cropland data layer (CDL). The flood maps extracted from SMAP data are validated with FEMA-declared affected counties as well as with flood maps from other sources.
@article{rahman_rapid_2019,
	title = {Rapid {Flood} {Progress} {Monitoring} in {Cropland} with {NASA} {SMAP}},
	volume = {11},
	copyright = {http://creativecommons.org/licenses/by/3.0/},
	url = {https://www.mdpi.com/2072-4292/11/2/191},
	doi = {10.3390/rs11020191},
	abstract = {Research in different agricultural sectors, including in crop loss estimation during flood and yield estimation, substantially rely on inundation information. Spaceborne remote sensing has widely been used in the mapping and monitoring of floods. However, the inability of optical remote sensing to cloud penetration and the scarcity of fine temporal resolution SAR data hinder the application of flood mapping in many cases. Soil Moisture Active Passive (SMAP) level 4 products, which are model-driven soil moisture data derived from SMAP observations and are available at 3-h intervals, can offer an intermediate but effective solution. This study maps flood progress in croplands by incorporating SMAP surface soil moisture, soil physical properties, and national floodplain information. Soil moisture above the effective soil porosity is a direct indication of soil saturation. Soil moisture also increases considerably during a flood event. Therefore, this approach took into account three conditions to map the flooded pixels: a minimum of 0.05 m3m\−3 increment in soil moisture from pre-flood to post-flood condition, soil moisture above the effective soil porosity, and the holding of saturation condition for the 72 consecutive hours. Results indicated that the SMAP-derived maps were able to successfully map most of the flooded areas in the reference maps in the majority of the cases, though with some degree of overestimation (due to the coarse spatial resolution of SMAP). Finally, the inundated croplands are extracted from saturated areas by Spatial Hazard Zone areas (SHFA) of Federal Emergency Management Agency (FEMA) and cropland data layer (CDL). The flood maps extracted from SMAP data are validated with FEMA-declared affected counties as well as with flood maps from other sources.},
	language = {en},
	number = {2},
	urldate = {2019-01-23},
	journal = {Remote Sensing},
	author = {Rahman, Md Shahinoor and Di, Liping and Yu, Eugene and Lin, Li and Zhang, Chen and Tang, Junmei},
	month = jan,
	year = {2019},
	pages = {191},
	file = {Full Text PDF:/Volumes/mini-disk1/Google Drive/_lib/zotero/storage/74YCYW48/Rahman et al. - 2019 - Rapid Flood Progress Monitoring in Cropland with N.pdf:application/pdf;Snapshot:/Volumes/mini-disk1/Google Drive/_lib/zotero/storage/2Q6983R9/191.html:text/html}
}

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