Monitoring agricultural drought effectively and timely is important to support drought management and food security. Effective drought monitoring requires a suite of drought indices to capture the evolution process of drought. Thermal infrared signals respond rapidly to vegetation water stress, thus being regarded useful for drought monitoring at the early stage. Several temperature-based drought indices have been developed considering the role of land surface temperature (LST) in surface energy and water balance. Here, we compared the recently proposed Temperature Rise Index (TRI) with several agricultural drought indices that also use thermal infrared observations, including Temperature Condition Index (TCI), Vegetation Health Index (VHI) and satellite-derived evapotranspiration ratio anomaly (Δf_RET) for a better understanding of these thermal infrared drought indices. To do so, we developed a new method for calculating TRI directly from the top-of-atmosphere brightness temperatures in the two split-window channels (centered around ∼11 and 12 μm) rather than from LST. TRI calculated using the Himawari-8 brightness temperatures (TRI_BT) and LST retrievals (TRI_LST), along with the other LST-based indices, were calculated for the growing season (July–October) of 2015−2019 over the Australian wheatbelt. An evaluation was conducted by spatiotemporally comparing the indices with the drought indices used by the Australian Bureau of Meteorology in the official drought reports: the Precipitation Condition Index (PCI) and the Soil Moisture Condition Index (SMCI). All the LST-based drought indices captured the wet conditions in 2016 and dry conditions in 2019 clearly. Ranking of Pearson correlations of the LST-based indices with regards to PCI and SMCI produced very similar results. TRI_BT and TRI_LST showed the best agreement with PCI and SMCI (r > 0.4). TCI and VHI presented lower consistency with PCI and SMCI compared with TRI_BT and TRI_LST. Δf_RET had weaker correlations than the other LST-based indices in this case study, possibly because of outliers affecting the scaling procedure. The capability of drought early warning for TRI was demonstrated by comparing with the monthly time series of the greenness index Vegetation Condition Index (VCI) in a case study of 2018 considering the relatively slow response of the greenness index to drought. TRI_BT and TRI_LST had a lead of one month in showing the changing dryness conditions compared with VCI. In addition, the LST-based indices were correlated with annual wheat yield. Compared to wheat yields, all LST-based indices had a peak correlation in September. TRI_BT and TRI_LST had strong peak and average correlations with wheat yield (r ≥ 0.8). We conclude that TRI has promise for agricultural drought early warning, and TRI_BT appears to be a good candidate for efficient operational drought early warning given the readily accessible inputs and simple calculation approach.

有效和及时地监测农业干旱对支持干旱管理和粮食安全至关重要。有效的干旱监测需要一套干旱指数来捕获干旱的演变过程。红外热信号对植被的水分胁迫反应迅速，因此被认为对早期干旱监测很有用。考虑到陆地表面温度（LST）在表面能和水平衡中的作用，已经开发了几种基于温度的干旱指数。在这里，我们将最近提出的温升指数（TRI）与也使用热红外观测的几种农业干旱指数进行了比较，包括温度条件指数（TCI），植被健康指数（VHI）和卫星衍生的蒸散率异常（Δf _RET）以更好地了解这些热红外干旱指数。为此，我们开发了一种直接从两个分割窗口通道（以11和12μm为中心）的大气顶亮度温度直接计算TRI的新方法，而不是LST。利用Himawari-8亮度温度（TRI_BT）和LST反演（TRI_LST）以及其他基于LST的指数计算出的TRI是针对澳大利亚小麦带上2015-2019年的生长季节（7月至10月）计算的。通过时空比较指标与澳大利亚气象局在官方干旱报告中使用的干旱指标进行了评估：降水状况指数（PCI）和土壤水分状况指数（SMCI）。所有基于LST的干旱指数清楚地反映了2016年的潮湿状况和2019年的干旱状况。基于LST的指数与PCI和SMCI的皮尔逊相关性排名产生了非常相似的结果。TRI_BT和TRI_LST与PCI和SMCI表现出最好的协议（r  > 0.4）。与TRI_BT和TRI_LST相比，TCI和VHI与PCI和SMCI的一致性较低。Δ ˚F _RET在本案例研究中，与其他基于LST的索引相比，它们的相关性较弱，这可能是因为离群值影响了缩放过程。考虑到绿色指数对干旱的响应相对较慢，在2018年的案例研究中，通过与绿色指数植被状况指数（VCI）的每月时间序列进行比较，证明了TRI的干旱预警能力。与VCI相比，TRI_BT和TRI_LST在显示干燥条件变化方面领先一个月。此外，基于LST的指数与小麦年产量相关。与小麦单产相比，所有基于LST的指数在9月均具有峰值相关性。TRI_BT和TRI_LST与小麦产量具有强烈的峰值和平均相关性（r ≥0.8）。我们得出的结论是，TRI具有农业干旱预警的希望，而TRI_BT似乎是有效的操作性干旱预警的良好候选者，因为输入数据易于获取且计算方法简单。