Remote Sensing (RS) offers efficient tools for drought monitoring, especially in countries with a lack of reliable and consistent in-situ multi-temporal datasets. In this study, a novel RS-based Drought Index (RSDI) named Temperature-Vegetation-soil Moisture-Precipitation Drought Index (TVMPDI) was proposed. To the best of our knowledge, TVMPDI is the first RSDI using four different drought indicators in its formulation. TVMPDI was then validated and compared with six conventional RSDIs including VCI, TCI, VHI, TVDI, MPDI and TVMDI. To this end, precipitation and soil temperature in-situ data have been used. Different time scales of meteorological Standardized Precipitation Index (SPI) index have also been used for the validation of the RSDIs. TVMPDI was highly correlated with the monthly precipitation and soil temperature in-situ data at 0.76 and 0.81 values respectively. The correlation coefficients between the RSDIs and 3-month SPI ranged from 0.07 to 0.28, identifying the TVMPDI as the most suitable index for subsequent analyses. Since the proposed TVMPDI could considerably outperform the other selected RSDIs, all spatiotemporal drought monitoring analyses in Iran were conducted by TVMPDI over the past 21 years. In this study, different products of the Moderate Resolution Imaging Spectrometer (MODIS), Tropical Rainfall Measuring Mission (TRMM), and Global Precipitation Measurement (GPM) datasets containing 15,206 images were used on the Google Earth Engine (GEE) cloud computing platform. According to the results, Iran experienced the most severe drought in 2000 with a 0.715 TVMPDI value lasting for almost two years. Conversely, the TVMPDI showed a minimum value equal to 0.6781 in 2019 as the lowest annual drought level. The drought severity and trend in the 31 provinces of Iran have also been mapped. Consequently, various levels of decrease over the 21 years were found for different provinces, while Isfahan and Gilan were the only provinces showing an ascending drought trend (with a 0.004% and 0.002% trendline slope respectively). Khuzestan also faced a worrying drought prevalence that occurred in several years. In summary, this study provides updated information about drought trends in Iran using an advanced and efficient RSDI implemented in the cloud computing GEE platform. These results are beneficial for decision-makers and officials responsible for environmental sustainability, agriculture and the effects of climate change.

遥感 (RS) 为干旱监测提供了有效的工具，特别是在缺乏可靠且一致的原位多时数据集的国家。在这项研究中，提出了一种新的基于 RS 的干旱指数 (RSDI)，即温度-植被-土壤水分-降水干旱指数 (TVMPDI)。据我们所知，TVMPDI 是第一个在其公式中使用四种不同干旱指标的 RSDI。然后验证 TVMPDI 并与六种常规 RSDI 进行比较，包括 VCI、TCI、VHI、TVDI、MPDI 和 TVMDI。为此，就地降水和土壤温度数据已被使用。不同时间尺度的气象标准化降水指数 (SPI) 指数也已用于验证 RSDI。TVMPDI 与月降水量和土壤温度原位数据高度相关，分别为 0.76 和 0.81。RSDI 与 3 个月 SPI 之间的相关系数介于 0.07 至 0.28 之间，将 TVMPDI 确定为最适合后续分析的指标。由于提议的 TVMPDI 可以大大优于其他选定的 RSDI，因此在过去 21 年中，伊朗的所有时空干旱监测分析均由 TVMPDI 进行。在这项研究中，中分辨率成像光谱仪（MODIS）的不同产品，热带降雨测量任务 (TRMM) 和包含 15,206 张图像的全球降水测量 (GPM) 数据集用于谷歌地球引擎 (GEE) 云计算平台。根据结果​​，伊朗在 2000 年经历了最严重的干旱，TVMPDI 值为 0.715，持续了近两年。相反，TVMPDI 在 2019 年显示最小值等于 0.6781，为最低的年度干旱水平。还绘制了伊朗 31 个省的干旱严重程度和趋势。因此，不同省份在过去的 21 年中发现了不同程度的下降，而伊斯法罕和吉兰是仅有的显示干旱趋势上升的省份（趋势线斜率分别为 0.004% 和 0.002%）。胡齐斯坦还面临着几年来发生的令人担忧的干旱。总之，本研究使用在云计算 GEE 平台中实施的先进高效的 RSDI，提供有关伊朗干旱趋势的最新信息。这些结果有益于负责环境可持续性、农业和气候变化影响的决策者和官员。