scholarly journals A data fusion approach for mapping daily evapotranspiration at field scale

2013 ◽  
Vol 49 (8) ◽  
pp. 4672-4686 ◽  
Author(s):  
C. Cammalleri ◽  
M. C. Anderson ◽  
F. Gao ◽  
C. R. Hain ◽  
W. P. Kustas
Keyword(s):  
Data Fusion ◽  
Field Scale ◽  
Daily Evapotranspiration ◽  
Fusion Approach

scholarly journals Continuous Daily Evapotranspiration Estimation at the Field-Scale over Heterogeneous Agricultural Areas by Fusing ASTER and MODIS Data

2018 ◽  
Vol 10 (11) ◽  
pp. 1694 ◽  
Author(s):  
Zhenyan Yi ◽  
Hongli Zhao ◽  
Yunzhong Jiang
Keyword(s):  
Data Fusion ◽  
Water Resource Management ◽  
Mean Absolute Percentage Error ◽  
Percentage Error ◽  
Agricultural Fields ◽  
Field Scale ◽  
Spatiotemporal Resolution ◽  
Absolute Percentage Error ◽  
Daily Evapotranspiration ◽  
Agricultural Areas

Continuous daily evapotranspiration (ET) monitoring at the field-scale is crucial for water resource management in irrigated agricultural areas in arid regions. Here, an integrated framework for daily ET, with the required spatiotemporal resolution, is described. Multi-scale surface energy balance algorithm evaluations and a data fusion algorithm are combined to optimally exploit the spatial and temporal characteristics of image datasets, collected by the advanced space-borne thermal emission reflectance radiometer (ASTER) and the moderate resolution imaging spectroradiometer (MODIS). Through combination with a linear unmixing-based method, the spatial and temporal adaptive reflectance fusion model (STARFM) is modified to generate high-resolution ET estimates for heterogeneous areas. The performance of this methodology was evaluated for irrigated agricultural fields in arid and semiarid areas of Northwest China. Compared with the original STARFM, a significant improvement in daily ET estimation accuracy was obtained by the modified STARFM (overall mean absolute percentage error (MAP): 12.9% vs. 17.2%; overall mean absolute percentage error (RMSE): 0.7 mm d−1 vs. 1.2 mm d−1). The modified STARFM additionally preserved more spatial details than the original STARFM for heterogeneous agricultural fields, and provided field-to-field variability in water use. Improvements were further evident in the continuous daily ET, where the day-to-day dynamics of ET estimates were captured. ET data fusion provides a unique means of monitoring continuous daily crop ET values at the field-scale in agricultural areas, and may have value in supporting operational water management decisions.

scholarly journals Monitoring daily evapotranspiration over two California vineyards using Landsat 8 in a multi-sensor data fusion approach

2016 ◽  
Vol 185 ◽  
pp. 155-170 ◽  
Author(s):  
Kathryn A. Semmens ◽  
Martha C. Anderson ◽  
William P. Kustas ◽  
Feng Gao ◽  
Joseph G. Alfieri ◽  
...  
Keyword(s):  
Data Fusion ◽  
Sensor Data ◽  
Landsat 8 ◽  
Sensor Data Fusion ◽  
Multi Sensor Data Fusion ◽  
Daily Evapotranspiration ◽  
Fusion Approach

Procuring cooperative intelligence in autonomous vehicles for object detection through data fusion approach

2020 ◽  
Vol 14 (11) ◽  
pp. 1410-1417 ◽  
Author(s):  
Alfred Daniel ◽  
Karthik Subburathinam ◽  
Bala Anand Muthu ◽  
Newlin Rajkumar ◽  
Seifedine Kadry ◽  
...  
Keyword(s):  
Data Fusion ◽  
Object Detection ◽  
Autonomous Vehicles ◽  
Fusion Approach

Validation of synthetic daily Landsat NDVI time series data generated by the improved spatial and temporal data fusion approach

2018 ◽  
Vol 40 ◽  
pp. 34-44 ◽  
Author(s):  
Mingquan Wu ◽  
Wenjiang Huang ◽  
Zheng Niu ◽  
Changyao Wang ◽  
Wang Li ◽  
...  
Keyword(s):  
Time Series ◽  
Data Fusion ◽  
Time Series Data ◽  
Series Data ◽  
Temporal Data ◽  
Ndvi Time Series ◽  
Fusion Approach

Data fusion approach to verifying handwritten signatures on bank checks

1999 ◽  
Author(s):  
D. J. Scott ◽  
Otman A. Basir ◽  
Khaled S. Hassanein ◽  
John S. Zelek
Keyword(s):  
Data Fusion ◽  
Bank Checks ◽  
Fusion Approach
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