Short communicationMonitoring vegetation phenology using MODIS
Introduction
The phenological dynamics of terrestrial ecosystems reflect response of the Earth's biosphere to inter- and intra-annual dynamics of the Earth's climate and hydrologic regimes Myneni et al., 1997, Schwartz, 1999, White et al., 1997. Because of the synoptic coverage and repeated temporal sampling that satellite observations afford, remotely sensed data possess significant potential for monitoring vegetation dynamics at regional to global scales (e.g., Myneni et al., 1997). In the last decade, a number of different methods have been developed to determine the timing of vegetation greenup and senescence (i.e., the start and end of growing season) using time series of normalized difference vegetation index (NDVI) data from the Advanced Very High Resolution Radiometer (AVHRR). These methods have employed a variety of different approaches including the use of specific NDVI thresholds Lloyd, 1990, White et al., 1997, the largest NDVI increase (Kaduk & Heimann, 1996), backward-looking moving averages (Reed et al., 1994), or empirical equations (Moulin, Kergoat, Viovy, & Dedieu, 1997). However, such methods are difficult to apply at global scales, and generally do not account for ecosystems characterized by multiple growth cycles (e.g., double- or triple-crop agriculture, semiarid systems with multiple rainy seasons, etc.).
Satellite vegetation index (VI) data such as the NDVI are correlated with green leaf area index (LAI), green biomass, and percent green vegetation cover Asrar et al., 1989, Baret & Guyot, 1991. Until recently, the AVHRR provided the only source of global data for this purpose. However, because the AVHRR was never designed for land applications, these data are not well suited for vegetation monitoring applications. Specifically, the lack of precise calibration, poor geometric registration, and difficulties involved in cloud screening AVHRR data result in high levels of noise (Goward, Markham, Dye, Dulaney, & Yang, 1991). The radiometric and geometric properties of the Moderate Resolution Imaging Spectroradiometer (MODIS) onboard NASA's Terra spacecraft, in combination with improved atmospheric correction and cloud screening provided by MODIS science team activities, provide a substantially improved basis for studies of this nature. In this paper, we present the first attempt to study vegetation phenology using data from MODIS.
Section snippets
Monitoring phenology using remote sensing
For this work, the annual cycle of vegetation phenology inferred from remote sensing is characterized by four key transition dates, which define the key phenological phases of vegetation dynamics at annual time scales. These transition dates are: (1) greenup, the date of onset of photosynthetic activity; (2) maturity, the date at which plant green leaf area is maximum; (3) senescence, the date at which photosynthetic activity and green leaf area begin to rapidly decrease; (4) dormancy, the date
MODIS data
The MODIS instrument possesses seven spectral bands that are specifically designed for land applications with spatial resolutions that range from 250 m to 1 km (Justice et al., 1997). Using daily multi-angle, cloud-free, and atmospherically corrected surface reflectances collected over 16-day periods, the MODIS bi-directional reflectance distribution function (BRDF)/Albedo algorithm generates one nadir BRDF-adjusted reflectance (NBAR) for each MODIS land band at 1-km spatial resolution (Schaaf
Results
Fig. 3 presents representative results produced by the method described in Section 2 for a mixed forest pixel from New England. Visual inspection of this figure shows that the phenological transition dates are realistically detected. To provide a more regional perspective, Fig. 4 presents images of the northeastern United States showing the spatial variation for each of the phenological transition dates, and Fig. 5 shows the variation in greenup onset and dormancy onset as a function of
Discussion and conclusions
This communication presents a new methodology for studying vegetation phenology using remote sensing. The methodology provides a flexible means to monitor vegetation dynamics over large areas using remote sensing. Initial results using MODIS data for a region centered over New England demonstrate that the method provides realistic results that are geographically and ecologically consistent with the known behavior of vegetation in this region. In particular, the MODIS-based estimates of greenup
Acknowledgments
This work was funded under NASA contract number NAS5-31369.
References (20)
Automatic corn–soybean classification using Landsat MSS data: II. Early season crop proportion estimation
Remote Sensing of Environment
(1984)- et al.
Potentials and limits of vegetation indices for LAI and APAR assessment
Remote Sensing of Environment
(1991) - et al.
Normalized difference vegetation index measurements from the Advanced Very High Resolution Radiometer
Remote Sensing of Environment
(1991) - et al.
Development of methods for mapping global snow cover using moderate resolution imaging spectroradiometer data
Remote Sensing of Environment
(1995) - et al.
Overview of the radiometric and biophysical performance of the MODIS vegetation indices
Remote Sensing of Environment
(2002) - et al.
First operational BRDF, Albedo and Nadir reflectance products from MODIS
Remote Sensing of Environment
(2002) - et al.
Biomass accumulation and main stem elongation of durum wheat grown under Mediterranean conditions
Annals of Botany
(2001) - et al.
Estimation of plant canopy attributes from spectral reflectance measurements, Chap. 7
Crop emergence data determination from spectral data
Photogrammetric Engineering and Remote Sensing
(1980)- et al.
Global land cover mapping from MODIS: algorithms and early results
Remote Sensing of Environment
(2002)
Cited by (2033)
Classifying raw irregular time series (CRIT) for large area land cover mapping by adapting transformer model
2024, Science of Remote SensingSpatial domain transfer: Cross-regional paddy rice mapping with a few samples based on Sentinel-1 and Sentinel-2 data on GEE
2024, International Journal of Applied Earth Observation and GeoinformationOBSUM: An object-based spatial unmixing model for spatiotemporal fusion of remote sensing images
2024, Remote Sensing of EnvironmentAutomatedly identify dryland threatened species at large scale by using deep learning
2024, Science of the Total Environment