Desertification has been listed as one of the most critical global environmental issues, posing a significant threat to life, particularly in arid and semiarid regions. Therefore, gaining a comprehensive understanding of the present and future desertification trends becomes imperative. This study employs a feature space model, which effectively captures land surface changes related to desertification, enabling the extraction of pertinent information. Subsequently, time series models are used to determine the most accurate desertification simulation. Twenty-one ETM + sensor images were utilized to calculate the Topsoil Grain Size (TGSI) and Albedo remotely sensed indexes. Constructing the Albedo-TGSI feature space, the Desertification Degree Index (DDI) was extracted for each year. Different levels of desertification were identified by applying a natural break classification on the DDI values, and corresponding break values were obtained. The representative desertification degree for each year was determined by calculating the average of the minimum and maximum break values, resulting in the generation of five distinct time series for five desertification degrees. Different ARIMA models and wavelet transforms were selected to simulate the various desertification degrees based on the analysis of autocorrelation and partial autocorrelation functions and trial and error, respectively. The most suitable ARIMA models with the lowest errors were identified as follows: ARIMA (1,0,7) for severe desertification, ARIMA (0,1,6) for high desertification, ARIMA (0,0,7) for moderate desertification, and ARIMA (3,0,6) for non-desertification degrees. Among the various wavelet transform families tested, the Symlet family proved to be the most effective, except for the low desertification degree. The following wavelet transforms yielded the best results for each degree of desertification: Symlet3 for severe desertification, Symlet7 for high desertification, Symlet7 for moderate desertification, Daubechies 5 (db5) for low desertification, and Symlet7 for non-desertification degree simulations, all exhibiting the minimum error rates.

荒漠化已被列为全球最严重的环境问题之一，对生命构成重大威胁，特别是在干旱和半干旱地区。因此，全面了解当前和未来的荒漠化趋势势在必行。本研究采用特征空间模型，有效捕捉与荒漠化相关的地表变化，从而提取相关信息。随后，使用时间序列模型来确定最准确的荒漠化模拟。利用 21 张 ETM + 传感器图像来计算表土粒度 (TGSI) 和反照率遥感指数。构建Albedo-TGSI特征空间，提取每年的荒漠化程度指数（DDI）。通过对DDI值进行自然断裂分类来识别不同程度的荒漠化，并获得相应的断裂值。通过计算最小和最大断裂值的平均值来确定每年的代表性荒漠化程度，从而为五个荒漠化程度生成五个不同的时间序列。在自相关和偏自相关函数分析以及试错的基础上，分别选择不同的ARIMA模型和小波变换来模拟不同荒漠化程度。确定了误差最低的最合适的 ARIMA 模型如下：严重荒漠化的 ARIMA (1,0,7)，高度荒漠化的 ARIMA (0,1,6)，中度荒漠化的 ARIMA (0,0,7)，和 ARIMA (3,0,6) 用于非荒漠化程度。在测试的各种小波变换家族中，Symlet家族被证明是最有效的，除了荒漠化程度较低之外。以下小波变换对每种荒漠化程度都产生了最佳结果：严重荒漠化的 Symlet3、高度荒漠化的 Symlet7、中度荒漠化的 Symlet7、低度荒漠化的 Daubechies 5 (db5) 和非荒漠化程度模拟的 Symlet7，所有这些都表现出最小错误率。