Chapter 16
Flood Monitoring and Crop Damage
Assessment
Ranjay M. Shrestha and Md. Shahinoor Rahman
Abstract In recent years, the occurrence and impact of inland and coastal flood
events have become more frequent and damaging, especially within agricultural
fields, due to the global climate change and consistent sea level rise. Monitoring
and measuring the magnitude of flood events in a timely manner and assessing the
subsequent crop damages accurately are precursors in minimizing detrimental
consequences that could potentially lead to a global food security crisis. Traditional gauge-based measurements with sophisticated hydrological models are
capable of monitoring flood events precisely but limited within the smaller spatial
extent, time-consuming, and costly. In recent decades, advancement in airborneand satellite-based remote sensing technologies offering products at a daily global
spatial extent with various spectral resolution helps address the shortcomings of
the traditional in situ approaches in flood monitoring. Furthermore, the methods
such as classification and band ratioing using remote sensing products are simple
and effective in assessing flood-induced agricultural damages. The combination of
remote sensing products and geographic information systems along with the
current development in web mapping, users now can get near real-time flood
monitoring and crop damage assessments, albeit dependent upon the quality of
available data. A case study to quantify the impact of the 2011 Missouri Mississippi River flooding on the surrounding cornfield was performed through a regression model. The model was trained using historical daily NDVI and corn yield
across Nebraska and Missouri, and the overall accuracy in estimating corn yield
was about 90%. The method implemented in this localized case study could be
extended at a larger geographical scale.
R. M. Shrestha (*)
Science Systems and Applications, Inc., Lanham, MD, USA
NASA Goddard Space Flight Center, Greenbelt, MD, USA
e-mail: ranjay.m.shrestha@nasa.gov
M. S. Rahman
New Jersey City University, Jersey City, NJ, USA
© Springer Nature Switzerland AG 2021
L. Di, B. Üstündağ (eds.), Agro-geoinformatics, Springer Remote Sensing/
Photogrammetry, https://doi.org/10.1007/978-3-030-66387-2_16
321
Flood Monitoring and Crop Damage
Assessment
Ranjay M. Shrestha and Md. Shahinoor Rahman
Abstract In recent years, the occurrence and impact of inland and coastal flood
events have become more frequent and damaging, especially within agricultural
fields, due to the global climate change and consistent sea level rise. Monitoring
and measuring the magnitude of flood events in a timely manner and assessing the
subsequent crop damages accurately are precursors in minimizing detrimental
consequences that could potentially lead to a global food security crisis. Traditional gauge-based measurements with sophisticated hydrological models are
capable of monitoring flood events precisely but limited within the smaller spatial
extent, time-consuming, and costly. In recent decades, advancement in airborneand satellite-based remote sensing technologies offering products at a daily global
spatial extent with various spectral resolution helps address the shortcomings of
the traditional in situ approaches in flood monitoring. Furthermore, the methods
such as classification and band ratioing using remote sensing products are simple
and effective in assessing flood-induced agricultural damages. The combination of
remote sensing products and geographic information systems along with the
current development in web mapping, users now can get near real-time flood
monitoring and crop damage assessments, albeit dependent upon the quality of
available data. A case study to quantify the impact of the 2011 Missouri Mississippi River flooding on the surrounding cornfield was performed through a regression model. The model was trained using historical daily NDVI and corn yield
across Nebraska and Missouri, and the overall accuracy in estimating corn yield
was about 90%. The method implemented in this localized case study could be
extended at a larger geographical scale.
R. M. Shrestha (*)
Science Systems and Applications, Inc., Lanham, MD, USA
NASA Goddard Space Flight Center, Greenbelt, MD, USA
e-mail: ranjay.m.shrestha@nasa.gov
M. S. Rahman
New Jersey City University, Jersey City, NJ, USA
© Springer Nature Switzerland AG 2021
L. Di, B. Üstündağ (eds.), Agro-geoinformatics, Springer Remote Sensing/
Photogrammetry, https://doi.org/10.1007/978-3-030-66387-2_16
321