Predicting riparian evapotranspiration from MODIS vegetation indices and meteorological data
Predicting riparian evapotranspiration from MODIS vegetation indices and meteorological data
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DOI：
10.1016/j.rse.2004.08.009
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发表时间：
2005-01
影响因子：
13.5
通讯作者：
P. Nagler;J. Cleverly;E. Glenn;Derrick Lampkin;A. Huete;Z. Wan
中科院分区：
文献类型：
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作者：
P. Nagler;J. Cleverly;E. Glenn;Derrick Lampkin;A. Huete;Z. Wan
基于EOS-1 Terra卫星上的中分辨率成像光谱仪（MODIS）数据和地面气象数据，建立了新墨西哥州格兰德河中游河岸植被的植被指数（VI）模型，用于预测蒸散量（ET）。在四个河岸站点涡度相关塔获得的地面ET测量与MODIS维斯，MODIS陆面温度（LSTs），地面微气象数据超过四年。地点包括两个saltcedar（Tamarix ramosissima）和两个格兰德河棉白杨（Populus deltoides ssp. Wislizennii）占主导地位。增强植被指数（EVI）更密切相关（r=0.76）与ET比归一化差异植被指数（NDVI; r=0.68）的ET数据结合的网站和物种。从塔上测量的冠层空气温度（Ta）是与ET相关性最密切的气象变量（r=0.82）。在1公里和5公里分辨率的MODIS LST数据太粗糙，准确地测量辐射表面温度在狭窄的河岸走廊，因此，能量平衡方法估计ET使用MODIS LST是不成功的。另一方面，一个多变量回归方程预测ET从EVI和Tahad的r2=0.82跨网站，物种和年份。该方程类似于为作物物种开发的VI-ET模型。ET预测不需要物种特异性方程的发现是显着的，因为这些是混合植被区，不能很容易地映射在物种水平。
A vegetation index (VI) model for predicting evapotranspiration (ET) from data from the Moderate Resolution Imaging Spectrometer (MODIS) on the EOS-1 Terra satellite and ground meteorological data was developed for riparian vegetation along the Middle Rio Grande River in New Mexico. Ground ET measurements obtained from eddy covariance towers at four riparian sites were correlated with MODIS VIs, MODIS land surface temperatures (LSTs), and ground micrometeorological data over four years. Sites included two saltcedar (Tamarix ramosissima) and two Rio Grande cottonwood (Populus deltoides ssp. Wislizennii) dominated stands. The Enhanced Vegetation Index (EVI) was more closely correlated (r=0.76) with ET than the Normalized Difference Vegetation Index (NDVI; r=0.68) for ET data combined over sites and species. Air temperature (Ta) measured over the canopy from towers was the meteorological variable that was most closely correlated with ET (r=0.82). MODIS LST data at 1- and 5-km resolutions were too coarse to accurately measure the radiant surface temperature within the narrow riparian corridor; hence, energy balance methods for estimating ET using MODIS LSTs were not successful. On the other hand, a multivariate regression equation for predicting ET from EVI and Tahad an r2=0.82 across sites, species, and years. The equation was similar to VI–ET models developed for crop species. The finding that ET predictions did not require species-specific equations is significant, inasmuch as these are mixed vegetation zones that cannot be easily mapped at the species level.