A classification-based spatiotemporal adaptive fusion model for the evaluation of remotely sensed evapotranspiration in heterogeneous irrigated agricultural area
A classification-based spatiotemporal adaptive fusion model for the evaluation of remotely sensed evapotranspiration in heterogeneous irrigated agricultural area
复制标题
基于分类的异质灌溉农区遥感蒸散发评估的时空自适应融合模型
DOI：
10.1016/j.rse.2022.112962
复制
发表时间：
2022-05
影响因子：
13.5
通讯作者：
Zailin Huo
中科院分区：
文献类型：
--
作者：
Shuai Wang;Chaozi Wang;Chenglong Zhang;Jie Xue;Pu Wang;Xuanyu Wang;Weishu Wang;Xin Zhang;Wangcheng Li;Guanhua Huang;Zailin Huo
Remotely sensed evapotranspiration (ET) with high spatial and temporal resolution is frequently required to understand the regional hydrological processes, particularly in agricultural areas with complex planting structures. Most of the existing spatio-temporal fusion models lacked the fusion of ET because they ignored the physiological characteristics of the vegetation moisture condition. Therefore, we propose a classification-based spatiotemporal adaptive fusion model (CSAFM) for the evaluation of remotely sensed ET in an irrigated agricultural area with a complex planting structure. This model combines the unmixing-based and weight-based fusion approaches to produce ET maps with high spatiotemporal resolution. It uses the mainstream weight-based fusion algorism—the spatial and temporal adaptive reflectance fusion model (STARFM)—in the fusion step. However, in contrast to the existing reflectance-based fusion algorithms, the CSAFM considers the effects of soil moisture and crop category on evapotranspiration rates. It replaces the unmixing window with an irregular hydrological response unit (HRU) containing homogeneous meteorological and irrigation conditions, and then unmixing the mixed pixels using a planting structure map. Moreover, an ET correction method was proposed in CSAFM to restore the spatial heterogeneity. The performance of CSAFM was compared to that of two mainstream fusion models using Landsat-ET and MODIS-ET based on the surface energy balance algorithm for land (SEBAL): the enhanced spatial and temporal adaptive reflectance fusion model (ESTARFM) and the flexible spatiotemporal data fusion method (FSDAF). The models were validated with ground-based ET monitored by eddy covariance observed ET and Landsat inverted ET. It was found that the CSAFM model (mean MAE: 0.40 mm/day) beat the ESTARFM model (mean MAE: 0.49 mm/day) and the FSDAF (mean MAE: 0.53 mm/day) in accurately fusion ET and reproduce the details of complex surface landscapes. Additionally, CSAFM (RMSE: 0.66–1.10 mm/day) is less sensitive to the update frequency of input data in the crop growing season than ESTARFM (RMSE: 0.79–1.36 mm/day) and FSDAF (RMSE: 0.79–1.17 mm/day), indicating its suitability in areas with limited input dataset. Overall, the proposed CSAFM model can greatly improve the ET fusion accuracy in irrigated agricultural areas. • A new spatiotemporal fusion model is proposed for remote sensing ET in irrigated agriculture. • The model takes into account the differences and similarities of different crops at the growth stage. • The model alleviates the boundary effect and soil spatial heterogeneity in the decomposition process. • The new model has better fusion accuracy and less sensitivity to the integrity of the basic remote sensing data.
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影响因子：
13.5
作者：
Johnson, David M.
通讯作者：
Johnson, David M.
DOI：
10.1109/jstars.2012.2194696
发表时间：
2012-04-01
影响因子：
5.5
作者：
Bioucas-Dias, Jose M.;Plaza, Antonio;Chanussot, Jocelyn
通讯作者：
Chanussot, Jocelyn
DOI：
10.3390/rs13173420
发表时间：
2021-08
期刊：
Remote. Sens.
影响因子：
--
作者：
Jie Xue;Martha C. Anderson;F. Gao;C. Hain;Yun Yang;K. Knipper;W. Kustas;Yang Yang-Yang
通讯作者：
Jie Xue;Martha C. Anderson;F. Gao;C. Hain;Yun Yang;K. Knipper;W. Kustas;Yang Yang-Yang
影响因子：
13.5
作者：
Hilker, Thomas;Wulder, Michael A.;White, Joanne C.
通讯作者：
White, Joanne C.
影响因子：
13.5
作者：
Barrett, Brian;Nitze, Ingmar;Cawkwell, Fiona
通讯作者：
Cawkwell, Fiona