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Sensor intercomparison of distributed surface radiation measurement system

机译:分布式表面辐射测量系统的传感器比对

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The Wireless Sensor Networks of Coarse-resolution Pixel Parameters (CPP-WSN) was established to monitor the heterogeneity of coarse spatial resolution pixel, with consideration of different categories of land surface parameters in Huailai, Hebei province, China (40.349°N, 115.785°E). The observation network of radiation parameters (RadNet) in CPP-WSN was developed for multi-band radiation measurement and consisted of 6 nodes covering 2km*2km area to capture its heterogeneity. Each node employed four sensors to observe the five radiation parameters. The number and location of nodes in RadNet were determined through the representativeness-based sampling method. Thus, the RadNet is a distributed observation system with nodes work synchronously and measurements used together. The intercomparison experiment for RadNet is necessary and was conducted in Huailai Remote Sensing Station from 5th Aug to 10th Aug in 2012. Time series observations from various sensors were collected and analyzed. The maximum relative differences among sensors of UVR, SWR, LWR, PAR, and LST are 4.83%, 5.3%, 3.71%, 11%, and 0.54%, respectively. Sensor/parameter differences indeed exist and are considerable large for PAR, SWR, UVR, and LWR, which cannot be ignored. The linear normalization and quadratic polynomial normalization perform similar for CUV5/UVR, PQS1/PAR, CNR4/SWR, and SI-111/LST. As for CNR4/LWR, quadratic polynomial normalization show higher accuracy than linear normalization, especially in node2, node4, and node5. Thus, the LWR measured by CNR4 is proved to be nonlinear, and should be normalized with quadratic polynomial coefficients for higher precision.
机译:建立了粗分辨率像素参数无线传感器网络(CPP-WSN),以监测粗略的空间分辨率像素的异质性,并考虑了河北省怀来市不同类别的地表参数(40.349°N,115.785° E)。 CPP-WSN中的辐射参数观测网络(RadNet)是为多波段辐射测量而开发的,它由覆盖2km * 2km区域的6个节点组成,以捕获其异质性。每个节点使用四个传感器观察五个辐射参数。 RadNet中节点的数量和位置是通过基于代表性的采样方法确定的。因此,RadNet是一个分布式观测系统,其中节点同步工作,并且测量值一起使用。 RadNet的比对实验是必要的,于2012年8月5日至8月10日在怀来遥感站进行。收集并分析了来自各种传感器的时间序列观测结果。 UVR,SWR,LWR,PAR和LST传感器之间的最大相对差异分别为4.83%,5.3%,3.71%,11%和0.54%。确实存在传感器/参数差异,并且对于PAR,SWR,UVR和LWR而言,差异非常大,这是不容忽视的。线性归一化和二次多项式归一化对CUV5 / UVR,PQS1 / PAR,CNR4 / SWR和SI-111 / LST的执行效果相似。对于CNR4 / LWR,二次多项式归一化显示出比线性归一化更高的准确性,尤其是在node2,node4和node5中。因此,CNR4测得的LWR被证明是非线性的,应使用二次多项式系数进行归一化以提高精度。

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