Using USDA Crop Progress Data for the Evaluation of Greenup Onset Date Calculated from MODIS 250-Meter Data

Identification of the onset of vegetation greenup is a key factor in characterizing and monitoring vegetation dynamics over large areas. However, the relationship between greenup onset dates estimated from satellite imagery and the actual growth stage of vegetation is often unclear. Herein, we present an approach for comparing pixel-level onset dates to regional planting and emergence information for agricultural crops, with the goal of drawing reliable conclusions regarding the physical growth stage of the vegetation of interest at the time of greenup onset. To accomplish this, we calculated onset of greenup using MODIS 250 m, 16-day composite NDVI time series data for Kansas for 2001 and a recently proposed methodology for greenup detection. We then evaluated the estimated greenup dates using the locations of 1,417 large field sites that were planted to corn, soybeans, or sorghum in 2001, in conjunction with United States Department of Agriculture (USDA) weekly crop progress reports containing crop planting and emergence percentage estimates. Average greenup onset dates calculated for the three summer crops showed that the dates were consistent with the relative planting order of corn, sorghum, and soybeans across the state. However, the influence of pre-crop vegetation (weeds and “volunteer” crops) introduced an early bias for the greenup onset dates calculated for many field sites. This pre-crop vegetation signal was most pronounced for the later planted summer crops (soybeans and sorghum) and in areas of Kansas that receive higher annual precipitation. The most reliable results were obtained for corn in semi-arid western Kansas, where pre-crop vegetation had considerably less influence on the greenup onset date calculations. The greenup onset date calculated for corn in western Kansas was found to occur 23 days after 50 percent of the crop had emerged. Corn’s greenup onset was detected, on average, at the agronomic stage where plants are 15 to 45 cm (6 to 18 inches) tall and the crop begins its rapid growth. Introduction Remote sensing of vegetation phenology for large geographic areas is needed to characterize vegetation dynamics in support of global environmental change research. Timeseries vegetation index (VI) data derived from satellitebased sensors such as the Advanced Very High Resolution Using USDA Crop Progress Data for the Evaluation of Greenup Onset Date Calculated from MODIS 250-Meter Data Brian D. Wardlow, Jude H. Kastens, and Stephen L. Egbert Radiometer (AVHRR), with a near-daily global repeat coverage, have been widely used to monitor vegetation phenology at regional to global scales over the past decade (Lloyd, 1990; Reed et al., 1994; Moulin et al., 1997; Schwartz et al., 2002; Yu et al., 2004). VI data are well suited for phenology studies, given their correlation with biophysical parameters such as green leaf area (LAI) and green biomass (Asrar et al., 1989; Baret and Guyot, 1991). Changes in VI data over the growing season reflect the seasonal biophysical changes of vegetation (structural and physiological), from which a suite of vegetation phenological metrics (VPMs) (e.g., greenup onset, dormancy onset, and length of growing season) can be estimated. Numerous approaches have been applied to time-series VI data to identify VPMs (Lloyd, 1990; Reed et al., 1994; Moulin et al., 1997; White et al., 1997; Jonsson and Eklundh, 2002; Zhang et al., 2003; Yu et al., 2004). However, few studies have provided a thorough evaluation of the VPM results, which remains a key issue in remote sensing-based, large area phenology research (Schwartz and Reed, 1999; Zhang et al., 2003). Detailed evaluation of VPMs is difficult when using coarse resolution remotely sensed data. AVHRR normalized difference vegetation index (NDVI) data, with a nominal spatial resolution of 1 km (local area coverage) or 8 km (global area coverage), have been the primary data used for most large area phenology studies. At AVHRR’s coarse spatial resolution, the majority of pixels are mixed in the sense that they correspond to heterogeneous landscape mosaics of multiple land-cover or vegetation types that can have different phenological characteristics (Reed et al., 1994; Zhang et al., 2003). As a result, the time-series NDVI signal detected at the pixel-level is an integrated response of diverse vegetation types rather than a single type, confounding attempts at detailed evaluation. However, the Moderate Resolution Imaging Spectroradiometer (MODIS) launched in December, 1999, offers substantial potential for improved estimation and evaluation of VPMs. Time-series VI data from MODIS are produced at a higher spatial resolution (250 m) than AVHRR, which should result in a larger proportion of pixels corresponding to relatively homogeneous land cover types compared to pixels at the 1 km and 8 km resolutions. MODIS has a temporal resolution (16-day composite period) that allows the major phenological events traditionally calculated from remotely sensed data to be detected. Also, the MODIS VI data are of higher overall quality due to higher radiometric resolution (12-bit) and improved geometric registration, atmospheric correction, and cloud screening (Huete et al., 2002; Vermote et al., 2002; Wolfe et al., PHOTOGRAMMETRIC ENGINEER ING & REMOTE SENS ING Novembe r 2006 1225 Brian D. Wardlow is with the National Drought Mitigation Center (NDMC), University of Nebraska-Lincoln, 811 Hardin Hall, 3310 Holdrege Street, Lincoln, NE 68583-0988 ([email protected]). Jude H. Kastens and Stephen L. Egbert are with the Kansas Applied Remote Sensing (KARS) Program and Department of Geography, University of Kansas, Higuchi Hall, 2101 Constant Avenue, Lawrence, KS 66047-3759. Photogrammetric Engineering & Remote Sensing Vol. 72, No. 11, November 2006, pp. 1225–1234. 0099-1112/06/7211–1225/$3.00/0 © 2006 American Society for Photogrammetry and Remote Sensing 05-005 10/7/06 12:37 PM Page 1225