Field-Scale Crop Seeding Date Estimation from MODIS Data and Growing Degree Days in Manitoba, Canada

Taifeng Dong,Catherine Champagne,Budong Qian,Dan Macdonald,Ted Huffman,Jing M Chen,Brian Mcconkey,Bahram Daneshfar,Jiangui Liu,Andrew Davidson,Jiali Shang,Qi Jing

doi:10.3390/rs11151760

Taifeng Dong, Catherine Champagne + Show 10 more

Open Access

https://doi.org/10.3390/rs11151760

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Abstract

Information on crop seeding date is required in many applications; such as crop management and yield forecasting. This study presents a novel method to estimate crop seeding date at the field level from time-series 250-m Moderate Resolution Imaging Spectroradiometer (MODIS) data and growing degree days (GDD; base 5 °C; °C-days). The start of growing season (SOS) was first derived from time-series EVI2 (two-band Enhanced Vegetation Index) calculated from a MODIS 8-day composite surface reflectance product (MOD09Q1; Collection 6). Based on GDD; calculated from the Daymet gridded estimates of daily weather parameters; a simple model was developed to establish a linkage between the observed seeding date and the SOS. Calibration and validation of the model was conducted on three major crops; spring wheat; canola and oats; in the Province of Manitoba; Canada. The estimated SOS had a strong linear correlation with the observed seeding date; with a deviation of a few days depending on the year. The seeding date of the three crops can be calculated from the SOS by adjusting the number of days needed to accumulate GDD (AGDD) for emergence. The overall root-mean-square-difference (RMSD) of the estimated seeding date was less than 10 days. Validation showed that the accuracy of the estimated seeding date was crop-type independent. The developed method is useful for estimating the historical crop seeding date from remote sensing data in Canada; to support studies of the interactions among seeding date; crop management and crop yield under climate change. It is anticipated that this method can be adapted to other crops in other locations using the same or different satellite data.

Highlights

Seeding date is an important factor influencing the development and productivity of field crops
Crop phenological metrics are usually derived from time-series vegetation indices (VIs) through curve fitting, and the indices are usually calculated from reflectance data in the visible and near-infrared (NIR) bands
This study demonstrated the potential of seeding-date estimation by assimilating time series remotely sensed biophysical parameters (e.g., leaf area index (LAI) and fraction of absorbed photosynthetically active radiation (f APAR)) into crop growth models

Summary

Introduction

Seeding date is an important factor influencing the development and productivity of field crops. Estimation of crop phenological metrics using RS data depends largely on greenness information captured by optical satellite sensors. Studies have found that remotely sensed crop phenological metrics, especially the start of growing season (SOS, green-up date), can provide spatially explicit information on crop development stages, including the seeding date [7,12,13]. Crop phenological metrics are usually derived from time-series vegetation indices (VIs) through curve fitting, and the indices are usually calculated from reflectance data in the visible and near-infrared (NIR) bands. The Normalized Difference Vegetation Index (NDVI), the Enhanced Vegetation Index (EVI), and the two-band EVI without a blue band (EVI2) [11,14,15] are common VIs. In general, the SOS derived from optical satellite data is close to the earliest time of vegetation growth, such as leaf emergence [11,16,17]. The SOS responds to actual growth stages; the specific relationship varies with the methods used [17,18,19]

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