当前位置: X-MOL 学术Phys. Chem. Earth Parts A/B/C › 论文详情
Our official English website, www.x-mol.net, welcomes your feedback! (Note: you will need to create a separate account there.)
Improving phenological monitoring of winter wheat by considering sensor spectral response in spatiotemporal image fusion
Physics and Chemistry of the Earth, Parts A/B/C ( IF 3.0 ) Pub Date : 2020-03-10 , DOI: 10.1016/j.pce.2020.102859
Ziyang Cao , Shaohui Chen , Feng Gao , Xueke Li

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.



中文翻译:

考虑时空图像融合中传感器光谱响应的冬小麦物候监测

多传感器图像融合的结果可能与准确反映冬小麦的物候阶段有所不同,因为卫星传感器对光谱的不同响应会导致不同遥感图像之间的辐射不一致。为了减少传感器之间的物理电磁频谱响应差异对通过融合结果监测冬小麦物候期的影响,在时空融合方法中应考虑传感器频谱响应(SSR)。通过将SSR引入到时空自适应反射融合模型(STARFM)中,提出了一种新颖的图像融合模型。通过矩阵运算将SSR在最小化传感器之间的系统差异对图像融合产品的影响方面的贡献参数化为校准因子,能够抵消不同传感器图像之间的系统不一致。建立不同土地覆盖类型和光谱带的线性回归方程,以计算STARFM中所需的权重,以改善相邻光谱相似像素的选择。使用包括四个ZY-3(5.8 m)和Landsat 8 OLI(30 m)场景的一个卫星数据集对该方法进行了评估,这些场景是在冬小麦从幼苗到收获的整个生长阶段获取的。定性和定量评估表明,与STARFM相比,该方法可以更好地监测冬小麦的物候,与观察值相比具有更好的时空一致性。建立了不同土地覆盖类型和光谱带的线性回归方程,以计算STARFM中所需的权重,以改善相邻光谱相似像素的选择。使用包括四个ZY-3(5.8 m)和Landsat 8 OLI(30 m)场景的一个卫星数据集对该方法进行了评估,这些场景是在冬小麦从幼苗到收获的整个生长阶段获取的。定性和定量评估表明,与STARFM相比,该方法可以更好地监测冬小麦的物候,与观察值相比具有更好的时空一致性。建立了不同土地覆盖类型和光谱带的线性回归方程,以计算STARFM中所需的权重,以改善相邻光谱相似像素的选择。使用包括四个ZY-3(5.8 m)和Landsat 8 OLI(30 m)场景的一个卫星数据集对该方法进行了评估,这些场景是在冬小麦从幼苗到收获的整个生长阶段获取的。定性和定量评估表明,与STARFM相比,所提出的方法可以更好地监测冬小麦的物候,与观察值相比具有更好的时空一致性。8 m)和Landsat 8 OLI(30 m)场景,这些场景是在冬小麦从幼苗到收获的生长阶段获得的。定性和定量评估表明,与STARFM相比,该方法可以更好地监测冬小麦的物候,与观察值相比具有更好的时空一致性。8 m)和Landsat 8 OLI(30 m)场景,这些场景是在冬小麦从幼苗到收获的生长阶段获得的。定性和定量评估表明,与STARFM相比,该方法可以更好地监测冬小麦的物候,与观察值相比具有更好的时空一致性。

更新日期:2020-03-10
down
wechat
bug