TY - GEN
T1 - Crop Fraction Layer (CFL) datasets derived through MODIS and LandSat for the continental US from year 2000-2016
AU - Shrestha, Ranjay
AU - Di, Liping
AU - Yu, Eugene G.
AU - Rahman, Md Shahinoor
AU - Lin, Li
AU - Hu, Lei
AU - Tang, Junmei
N1 - Publisher Copyright:
© 2017 IEEE.
PY - 2017/9/19
Y1 - 2017/9/19
N2 - With ever growing population and shrinkage of agricultural land, food security is an extremely important research topic. Besides human activates, natural disasters such as flood, drought also play adverse effect on food productivity. Understanding impact of these disaster on crop yield and making early estimation could help planning for any global food crisis. Among various available crop yield estimation methods, remote sensing platform provides numerous indices on crop monitoring. Moderate Resolution Imaging Spectroradiometer (MODIS) based vegetation indices are among the most extensively used parameter and can provide very high (250m) spatial resolution products with daily coverage which is ideal for crop growth monitoring, however it lacks crop type information. Depending on a single spectral pattern to identify the crop type is also not effective as it suffer from mix-pixel issue. To avoid the issue of mix-pixel, this research aims to provide pixel level crop percentage data, Crop Fraction Layer (CFL), derived by combining 250m MODIS dataset with higher spatial resolution LandSat land cover product. The annual CFL will be available between years 2000 to 2016 for 10 major crops in the continental US. Additionally these CFL datasets are made accessible to the end-user through web-based application.
AB - With ever growing population and shrinkage of agricultural land, food security is an extremely important research topic. Besides human activates, natural disasters such as flood, drought also play adverse effect on food productivity. Understanding impact of these disaster on crop yield and making early estimation could help planning for any global food crisis. Among various available crop yield estimation methods, remote sensing platform provides numerous indices on crop monitoring. Moderate Resolution Imaging Spectroradiometer (MODIS) based vegetation indices are among the most extensively used parameter and can provide very high (250m) spatial resolution products with daily coverage which is ideal for crop growth monitoring, however it lacks crop type information. Depending on a single spectral pattern to identify the crop type is also not effective as it suffer from mix-pixel issue. To avoid the issue of mix-pixel, this research aims to provide pixel level crop percentage data, Crop Fraction Layer (CFL), derived by combining 250m MODIS dataset with higher spatial resolution LandSat land cover product. The annual CFL will be available between years 2000 to 2016 for 10 major crops in the continental US. Additionally these CFL datasets are made accessible to the end-user through web-based application.
KW - Agriculture
KW - Crop Fraction
KW - LandSat
KW - MODIS
KW - Remote Sensing
UR - http://www.scopus.com/inward/record.url?scp=85032819363&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85032819363&partnerID=8YFLogxK
U2 - 10.1109/Agro-Geoinformatics.2017.8047068
DO - 10.1109/Agro-Geoinformatics.2017.8047068
M3 - Conference contribution
AN - SCOPUS:85032819363
T3 - 2017 6th International Conference on Agro-Geoinformatics, Agro-Geoinformatics 2017
BT - 2017 6th International Conference on Agro-Geoinformatics, Agro-Geoinformatics 2017
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 6th International Conference on Agro-Geoinformatics, Agro-Geoinformatics 2017
Y2 - 7 August 2017 through 10 August 2017
ER -
