A Systematic Review on Case Studies of Remote-Sensing-Based Flood Crop Loss Assessment

Md Shahinoor Rahman and Liping Di
Additional contact information
Md Shahinoor Rahman: Center for Spatial Information Science and Systems, George Mason University, Fairfax, VA 22030, USA
Liping Di: Center for Spatial Information Science and Systems, George Mason University, Fairfax, VA 22030, USA

Agriculture, 2020, vol. 10, issue 4, 1-30

Abstract: This article reviews case studies which have used remote sensing data for different aspects of flood crop loss assessment. The review systematically finds a total of 62 empirical case studies from the past three decades. The number of case studies has recently been increased because of increased availability of remote sensing data. In the past, flood crop loss assessment was very generalized and time-intensive because of the dependency on the survey-based data collection. Remote sensing data availability makes rapid flood loss assessment possible. This study groups flood crop loss assessment approaches into three broad categories: flood-intensity-based approach, crop-condition-based approach, and a hybrid approach of the two. Flood crop damage assessment is more precise when both flood information and crop condition are incorporated in damage assessment models. This review discusses the strengths and weaknesses of different loss assessment approaches. Moderate Resolution Imaging Spectroradiometer (MODIS) and Landsat are the dominant sources of optical remote sensing data for flood crop loss assessment. Remote-sensing-based vegetation indices (VIs) have significantly been utilized for crop damage assessments in recent years. Many case studies also relied on microwave remote sensing data, because of the inability of optical remote sensing to see through clouds. Recent free-of-charge availability of synthetic-aperture radar (SAR) data from Sentinel-1 will advance flood crop damage assessment. Data for the validation of loss assessment models are scarce. Recent advancements of data archiving and distribution through web technologies will be helpful for loss assessment and validation.

Keywords: flood; crop; loss assessment; remote sensing; damage assessment (search for similar items in EconPapers)
JEL-codes: Q1 Q10 Q11 Q12 Q13 Q14 Q15 Q16 Q17 Q18 (search for similar items in EconPapers)
Date: 2020
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (9)

Downloads: (external link)
https://www.mdpi.com/2077-0472/10/4/131/pdf (application/pdf)
https://www.mdpi.com/2077-0472/10/4/131/ (text/html)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:gam:jagris:v:10:y:2020:i:4:p:131-:d:346261

Access Statistics for this article

Agriculture is currently edited by Ms. Leda Xuan

More articles in Agriculture from MDPI
Bibliographic data for series maintained by MDPI Indexing Manager ().