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ARS Home » Northeast Area » Beltsville, Maryland (BARC) » Beltsville Agricultural Research Center » Hydrology and Remote Sensing Laboratory » Research » Publications at this Location » Publication #373619
Research Project: Improving Agroecosystem Services by Measuring, Modeling, and Assessing Conservation Practices

Location: Hydrology and Remote Sensing Laboratory

Title: Improving phenological monitoring of winter wheat by considering sensor spectral response in spatiotemporal image fusion

Author
item CAO, Z. - Collaborator
item CHEN, S. - Institute Of Remote Sensing And Digital Earth, Chinese Academy Of Sciences
item Gao, Feng
item LI, X - Collaborator

Submitted to: Physics and Chemistry of the Earth
Publication Type: Peer Reviewed Journal
Publication Acceptance Date: 3/4/2020
Publication Date: 3/10/2020
Citation: Cao, Z., Chen, S., Gao, F.N., Li, X. 2020. Improving phenological monitoring of winter wheat by considering sensor spectral response in spatiotemporal image fusion. Physics and Chemistry of the Earth. 116:102859. https://doi.org/10.1016/j.pce.2020.102859.
DOI: https://doi.org/10.1016/j.pce.2020.102859

Interpretive Summary: Crop condition monitoring at the field scale requires high-resolution remote sensing data in both time and space. Remote sensing data from a single sensor cannot satisfy the requirement at present. The data fusion approach has been developed to fuse remote sensing imagery acquired from multiple sensors. These sensors have different bandwidth and spectral response characteristics. Built on the Spatial and Temporal Adaptive Reflectance Fusion Model (STARFM), this paper considers the difference in spectral response in the model. The method was evaluated by fusing Landsat (30 m) and ZY-3 (5.8 m) images for winter wheat monitoring. Compared to STARFM, data fusion results are improved by considering the difference in spectral responses from two sensors. Accurate data fusion results will improve crop condition monitoring, which is needed for crop management and yield prediction.

Technical Abstract: Multisensor image fusion results may deviate from accurately reflecting the phenological stages of winter wheat because different responses of satellite sensors to the spectrum lead to the radiometric inconsistency between different remote sensing images. To reduce the effect of the difference in the physical electromagnetic spectrum responses between sensors on monitoring the phenological stages of winter wheat by fusion results, Sensor Spectral Response (SSR) should be considered in spatiotemporal fusion methods. This paper proposes a novel image fusion model by introducing SSR into the Spatial and Temporal Adaptive Reflectance Fusion Model (STARFM). The contribution of SSR in minimizing the effect of the system difference between sensors on image fusion products is parameterized as a calibration factor by matrixing operation, which is able to offset the systematic inconsistency between different sensor images. Linear regression equation for different land cover type and spectral band is established to calculate the weights needed in STARFM for improving the selection of neighboring spectrally similar pixels. This proposed method is evaluated using one satellite datasets including four ZY-3 (5.8 m) and Landsat 8 OLI (30 m) scenes which are acquired during the growth stages of winter wheat from seedling to harvest. Qualitative and quantitative evaluation shows that the proposed method can better monitor the phenology of winter wheat with an improved spatial and temporal consistency with the observations than STARFM.