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首页> 外文期刊>Remote Sensing of Environment: An Interdisciplinary Journal >An improved method for separating soil and vegetation component temperatures based on diurnal temperature cycle model and spatial correlation
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An improved method for separating soil and vegetation component temperatures based on diurnal temperature cycle model and spatial correlation

机译：基于昼夜温度周期模型及空间相关性分离土壤和植被部件温度的改进方法

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This paper proposed an improved method for separating soil and vegetation component temperatures from one pixel land surface temperature (LST) using multi-pixel and multi-temporal data. The two main features of the method are (1) the use of a diurnal temperature cycle (DTC) model to describe component temperatures and (2) the application of a spatial weighting matrix to consider the spatial correlation of component temperatures. The proposed method was evaluated using an extensive simulated dataset with five component temperature types, three LST errors and 69 fractional vegetation cover (FVC) types, and field measurements with a high temporal frequency. Due to the time extendibility of DTC model, the possibility for retrieving component temperatures at any time was analyzed. Correspondingly, the schemes for selecting the best observations for four representative periods, i.e., 10:00-12:00, 09:00-18:00, 18:00-03:00 (on the next day) and 09:00-03:00, were determined. The validations showed satisfactory accuracies, and it was found that the errors were significantly influenced by the original LST retrieval error. In addition, the difference between the ideal temperature pattern from the DTC model and the actual temperature variation also affected the accuracy of the temporally extended component temperatures. Furthermore, sensitivity analyses indicated that the separation accuracy was independent of the uncertainty of the component emissivity but was influenced by FVC. Specifically, the retrieval accuracy was sensitive to the size and variation of FVC, and the latter had a more significant influence, but the result was less sensitive to the retrieval error and angular effect of FVC. Considering its accuracy, operability and robustness, the proposed method is effective for separating soil and vegetation component temperatures.
机译：本文提出了一种利用多像素和多时间数据将土壤和植被部件温度分离土壤和植被部件温度的改进方法。该方法的两个主要特征是（1）使用昼夜温度循环（DTC）模型来描述组件温度和（2）空间加权矩阵的应用，以考虑组分温度的空间相关性。使用具有五种组分温度类型，三个LST误差和69分数植被覆盖（FVC）类型的广泛的模拟数据集来评估所提出的方法，以及具有高时间频率的现场测量。由于DTC模型的时间可扩展性，分析了随时检索组分温度的可能性。相应地，用于选择四个代表性期间的最佳观察的方案，即10：00-12：00，09：00-18：00,18：00-03：00（在第二天）和09：00- 03:00，确定。验证表现出令人满意的精度，发现误差受到原始LST检索误差的显着影响。另外，来自DTC模型的理想温度模式与实际温度变化之间的差异也影响了时间延伸的部件温度的精度。此外，敏感性分析表明，分离精度与组分发射率的不确定性无关，但受FVC的影响。具体而言，检索精度对FVC的大小和变化敏感，后者的影响更大，但结果对FVC的检索误差和角度效应不太敏感。考虑到其准确性，可操作性和鲁棒性，该方法对于分离土壤和植被组分温度是有效的。

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