Monday, 1 August 2016

A hybrid approach for detecting corn and soybean phenology with time-series MODIS data

Published Date

August 2016, Vol.181:237–250, doi:10.1016/j.rse.2016.03.039

Title

A hybrid approach for detecting corn and soybean phenology with time-series MODIS data

Author

Linglin Zeng^a,b,^,

Brian D. Wardlow^c^,

Rui Wang^d^,

Jie Shan^e^,

Tsegaye Tadesse^f^,

Michael J. Hayes^f^,

Deren Li^b,g^,

aSchool of Resource and Environmental Science, Wuhan University, 129 Luoyu Road, Wuhan 430079, China
bCollaborative Innovation Center of Geospatial Technology, 129 Luoyu Road, Wuhan 430079, China
cCenter for Advanced Land Management Information Technologies, University of Nebraska-Lincoln, 3310 Holdrege St., Lincoln 68583, USA
dChangjiang Geotechnical Engineering Corporation, 1863 Jiefang Street, Wuhan 430010, China
eSchool of Civil Engineering, Purdue University, 550 Stadium Mall Dr., West Lafayette 47907, USA
fNational Drought Mitigation Center, University of Nebraska-Lincoln, 3310 Holdrege St., Lincoln 68583, USA
gState Key Laboratory of Information Engineering in Surveying, Mapping and Remote Sensing, Wuhan University, 129 Luoyu Road, Wuhan 430079, China

Received 25 October 2015. Revised 12 March 2016. Accepted 31 March 2016. Available online 29 April 2016.

Highlights

•
A new methodology to estimate crop phenology based on only remote sensed data
•
The method is incorporated with physical model and remote-sensed data.
•
Factors such as air temperature and photoperiod were taken into consideration.
•
The model was validated at both field scale as well as regional scale.
•
The method is potential to be extended to phenology detection of other crop types.

Abstract

Monitoring crop phenology provides essential information for crop management, as well as for understanding regional to global scale vegetation dynamics. In this study, a hybrid phenology detection method is presented that incorporates the “shape-model fitting” concept of the two-step filtering method and a simulation concept of the crop models to detect the critical vegetative stages and reproductive stages of corn (Zea mays L.) and soybeans (Glycine max L.) from MODIS 250-m Wide Dynamic Range Vegetation Index (WDRVI) time-series data and 1000-m Land Surface Temperature (LST) data. The method was first developed and tested at the field scale over a ten-year period (2003–2012) for three experimental study sites in eastern Nebraska of USA, where the estimated phenology dates were compared to the ground-based phenology observations for both corn and soybeans. The average root mean square error (RMSE) of phenology stage estimation of the individual development stages across all sites ranged from 1.9 to 4.3 days for corn and from 1.9 to 4.9 days for soybeans. The approach was then tested at a regional scale over eastern Nebraska and the state of Iowa to evaluate its ability to characterize the spatio-temporal variation of targeted corn and soybean phenology stage dates over a larger area. Quantitative regional assessments were conducted by comparing the estimated crop stage dates with crop developmental stage statistics reported by the USDA NASS Crop Progress Reports (NASS-CPR) for both eastern Nebraska and Iowa. The accuracy of the regional-scale phenology estimation in Iowa (RMSE ranged from 2.6 to 3.9 days for corn and from 3.2 to 3.9 days for soybeans) was slightly lower than in eastern Nebraska (RMSE ranged from 1.8 to 2.9 days for corn and from 1.7 to 2.9 days for soybeans), However, the estimation accuracy in Iowa was still reasonable with the estimated phenology dates being within 4 days or less of the observed dates for both corn and soybeans.