高成本与有限范围的实地监测已经无法满足植被物候研究的要求,而遥感物候监测方式又经常受到卫星传感器的时空分辨率等限制,这些局限使得图像融合成为高精度植被物候反演的关键.本研究基于Google Earth Engine(GEE)平台,以4个PhenoCam物候相机所观测的水稻、落叶林、玉米和灌木为研究对象,利用ESTARFM(Enhanced Spatial and Temporal Adaptive Reflectance Fusion Model)算法融合 Landsat 8影像与 MODIS产品,生成了2018 年 1d、3d、5d、7 d、9d、11 d 的 30 m 遥感 EVI 时间序列,并采用 Savitzky-Golay 滤波和 Maximum Separation方法提取生长季开始期SOS(Start of Season)、结束期EOS(End of Season)、生长季长度LOS(Length of Season)等物候信息.我们发现:(1)与实测物候对比,整体上呈现时间分辨率越高,物候误差越低的趋势,且当时间分辨率小于7 d时,融合物候的误差基本处在同一水平;(2)融合影像与Landsat 8影像的空间特征基本一致,空间效率SPAEF(Spatial Efficiency)指标为0.14-0.74,其中水稻、灌木与实际的空间一致性偏低;(3)融合结果与实地观测到的时间变化趋势吻合(RMSE:0.01-0.02,r:0.73-0.95),可以反演出较为准确的物候参数,SOS、EOS、LOS的平均误差为4.25 d、4.75 d、7.5 d;(4)与MODIS物候反演结果相比,非农用地(落叶林和灌木)的物候参数误差缩小较为明显,而农业用地(水稻和玉米)的提升效果相对较小.本研究从空间和时间维度验证了 ESTARFM算法生成的高时间分辨率EVI序列的可靠性,评估了其在物候监测能力上相比MODIS数据的提升效果,并探讨了影响融合效果的因素,可为精细化的植被动态监测和生态系统研究提供理论支撑和数据参考.
Accurately retrieving vegetation phenology at high spatial and temporal resolutions based on GEE and multi-source remote sensing data fusion
Vegetation phenology is considered the most direct and sensitive indicator for assessing environmental changes.At present,costly and limited in situ observations at field scales are impractical for phenology monitoring.Alternatively,various aspects of phenology have been successfully characterized using remote sensing images.Image fusion has become a breakthrough in deriving fine-resolution phenological metrics due to the trade-off between the spatial and temporal resolutions of satellite sensors.Most existing studies have retrieved phenology with sparse time series(with intervals of＞8 days);consequently,they fail to capture small intra-annual variations in phenology.In addition,a few scholars have used relatively dense time series,but obtained less robust results.Thus,extracting phenological metrics from fused results at fine temporal resolutions and comprehensively validating them are necessary to increase our knowledge regarding the fusion method.In this study,we implemented a spatiotemporal fusion approach,called the Enhanced Spatial and Temporal Adaptive Reflectance Fusion Model(ESTARFM),on the Google Earth Engine(GEE)platform.By leveraging this model,we generated a 30 m time series of the Enhanced Vegetation Index(EVI)with different resolutions(1,3,5,7,9,and 11 days)by fusing Landsat 8 and Moderate Resolution Imaging Spectroradiometer(MODIS)images in 2018.We selected four PhenoCam sites in North America.Among which,the predominant vegetation types are paddy rice,deciduous forests,corn,and shrubs(hereinafter referred to as Caml—Cam4,respectively).We used Savitzky-Golay filtering and the maximum separation method to estimate phenological metrics,such as Start of Season(SOS),End of Season(EOS),and Length of Season(LOS).We evaluated the effect of temporal resolution on the phenometrics derived from the fusion results with field data(from the PhenoCam project).Then,we used the daily fused time series for further illustration.The fused images were validated via true Landsat 8 observations with sufficient unmasked pixels.Finally,we used MODIS data to assess the improvement in accuracy of the derived phenometrics fusion results.We generated 133,273,126,and 288 images via ESTARFM at daily resolution at Caml—Cam4,respectively.These fused images obtained more spatial phenological details than low-resolution images(e.g.,MODIS),and the errors of phenometrics derived from the fusion results generally increased with temporal resolution.The errors became more evident when time resolution reached 7 days.We conducted research and discussions under daily resolution.The correlational results prove that the fused images can accurately capture reliable spatial patterns with spatial efficiency ranging from 0.14 to 0.74,and they are temporally consistent with field observations(root mean square error:0.01-0.02,r:0.73-0.95).The derived phenological metrics are relatively accurate,with mean errors of 4.25,4.75,and 7.5 days for SOS,EOS,and LOS,respectively.Compared with MODIS data,ESTARFM evidently reduces the errors of fusion-derived phenometrics for deciduous forests and shrubs.This research deployed a new framework on the GEE platform to estimate phenological metrics derived from satellite images fused by ESTARFM and assessed accuracy with field data in North America.Our findings revealed the reliability of the EVI time series with fine resolution generated by ESTARFM.This framework helps us obtain authentic details of spatiotemporal phenological distributions.Our study will surely provide important supportive theories and data basis for issues concerning vegetation dynamics and ecosystem.
万方数据
维普
