基于时空融合技术的高精度遥感蒸散计算

在利用遥感数据进行蒸散计算时，常因云雨污染和单一传感器限制，导致影像数据缺失严重，特别是在一些景观破碎化程度高的地区，较难获取高时空分辨率的蒸散序列。以黄河三角洲为研究区，基于增强型时空自适应融合模型构建高时空分辨率的遥感数据集，结合地表能量平衡系统模型采用先估算后融合和先融合后估算两种方式估算了区域2020年高时空分辨率的蒸散时间序列，探讨了融合算法和蒸散模型相结合的可行性和估算精度。结果表明：增强型时空自适应反射率融合模型(enhanced spatial and temporal adaptive reflectance fusion model, ESTARFM)算法性能较好，融合反射率与实际反射率的相关系数均在0.85以上；地表能量平衡系统(surface energy balance system, SEBS)模型估算的显热通量与通量站数据的均方根误差(root mean squared error, RMSE)仅为57.8 W/m²,融合模型估算的水面蒸发与蒸发皿折算数据的相关系数达到了0.93;黄河三角洲蒸散量随季节变化较大，其中夏季蒸散量约占全年...

High-precision remote sensing evapotranspiration estimation based on spatiotemporal fusion technology

When using remote sensing data for evapotranspiration calculation, it often leads to the serious lack of image data because of the limitation of a single sensor and cloud and rain pollution, especially in areas with high degree of landscape fragmentation, it is difficult to obtain high spatial and temporal resolution evapotranspiration sequence. Taking the Yellow River Delta as an example, based on the enhanced spatial and temporal adaptive reflectance fusion model, remote sensing datasets with high temporal and spatial resolution was constructed. The surface energy balance system model was used to estimate the regional evapotranspiration time series with high temporal-spatial resolution in 2020 by two different ways. The feasibility and estimation accuracy of combining fusion algorithm and evapotranspiration model were discussed. The results show that: The ESTARFM algorithm has good performance, and the correlation coefficients between the fusion reflectance and the actual reflectance are both above 0.85. The RMSE of sensible heat flux estimated by the SEBS model and flux station data is only 57.8W/m2, and the correlation coefficient between water evaporation estimated by the fusion data and pan evaporation converted data is 0.93. The evapotranspiration of the Yellow River Delta varies greatly with seasons, with summer evapotranspiration accounting for about 40% of the whole year. The annual evapotranspiration of different surface features varied significantly, ranging from 548.2 to 1289.9 mm/a. The spatial distribution pattern of evapotranspiration was significantly affected by land and sea distribution and land use, and the highest evapotranspiration was found in coastal tidal flat and water bodies, and natural vegetation was obviously higher than artificial features. The coupling mode of ESTARFM algorithm and SEBS model has a certain influence on the evapotranspiration results. The evapotranspiration accuracy of the fusion before estimation scheme is higher (R=0.88; RMSE=0.36 mm/d), which effectively improves the spatiotemporal resolution of evapotranspiration and is more suitable for the Yellow River Delta region.