Abstract This study aims to model diurnal Land Surface Temperature (LST) on a local scale of an arid environment by utilizing the Artificial Neural Network (ANN) and time series analysis of Landsat-8 satellite imageries. An arid region containing an in-situ LST station (DRA) located in Nevada, United States, was chosen as a test site. 78 Landsat-8 satellite imageries covering the test site were utilized to calculate spectral indexes. Since the spectral indexes represent the surface of Earth as land cover indexes, they can be used as indicators that affect the LST. The relationship between ten spectral indexes and in-situ LST were investigated, and the highly correlated indexes were determined as Built-up Area Extraction Index (BAEI) and Normalized Difference Bareness Index (NDBaI). The BAEI and NDBaI showed −0.80 and −0.94 correlation coefficients (r), respectively, with in-situ LST. Those two indexes and meteorological data, namely relative humidity (RH) and air temperature (AT), were used as inputs in the ANN model. A multi-layer perceptron (MLP) feed-forward network was considered in this study. The ANN model presented highly accurate results in the training and testing process with Root Mean Square Error (RMSE) values 0.74 K and 2.54 K, respectively. After learning and testing processes, the weights and biases were extracted to form the mathematical equation of the ANN model, and the equation was utilized to map LST for three data sets which were acquired during the winter and summer times and were not utilized in the ANN model. The results showed that the LST difference was lower than 1 K with regard to wintertime LST images. However, the LST difference was 2.49 K in the summertime. To test the spatial variability of ANN-based LST, MODIS LST products were considered and ANN-based LST was resampled to 1 km, the same resolution as the MODIS data, for comparison. As a result of the comparison, the highest mean LST difference (for all pixels in the scene) between ANN-based and MODIS LST was calculated as −1.1 K. Although the proposed method tended to underestimate LST as it increased, the obtained results showed that the ANN method would be a powerful tool for predicting and modeling the diurnal LST in an arid environment.

中文翻译：

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使用人工神经网络 (ANN) 和 Landsat-8 派生光谱指数的时间序列在干旱环境的局部尺度上模拟昼夜地表温度

摘要 本研究旨在利用人工神经网络 (ANN) 和 Landsat-8 卫星图像的时间序列分析，在干旱环境的局部尺度上模拟昼夜地表温度 (LST)。一个包含位于美国内华达州的原位 LST 站 (DRA) 的干旱地区被选为测试地点。覆盖测试场地的 78 幅 Landsat-8 卫星图像用于计算光谱指数。由于光谱指数将地球表面表示为土地覆盖指数，因此它们可以用作影响 LST 的指标。研究了10个光谱指标与原位LST的关系，确定高度相关的指标为建筑面积提取指数（BAEI）和归一化差异裸露指数（NDBaI）。BAEI 和 NDBaI 分别为 -0.80 和 -0。分别使用原位 LST 获得 94 个相关系数 (r)。这两个指标和气象数据，即相对湿度（RH）和气温（AT），被用作人工神经网络模型的输入。本研究考虑了多层感知器（MLP）前馈网络。ANN 模型在训练和测试过程中呈现出高度准确的结果，均方根误差 (RMSE) 值分别为 0.74 K 和 2.54 K。经过学习和测试过程，提取权重和偏差形成人工神经网络模型的数学方程，并利用该方程映射三个数据集的LST，这些数据集是在冬季和夏季获得的，在人工神经网络中没有使用模型。结果表明，冬季 LST 图像的 LST 差异小于 1 K。然而，夏季的 LST 差异为 2.49 K。为了测试基于 ANN 的 LST 的空间可变性，考虑了 MODIS LST 产品并将基于 ANN 的 LST 重新采样到 1 公里，与 MODIS 数据的分辨率相同，以进行比较。作为比较的结果，基于 ANN 和 MODIS LST 之间的最高平均 LST 差异（对于场景中的所有像素）计算为 -1.1 K。尽管所提出的方法随着 LST 的增加而倾向于低估，但获得的结果表明ANN 方法将成为在干旱环境中预测和建模昼夜 LST 的强大工具。