Agriculture is the foundation of socio-economic development and is highly influenced by weather and climate conditions.  Drought is one of the most significant threats to agricultural development and food security.  Currently, in-situ drought monitoring based on weather stations and based on remote sensing data has limitations, including infrequent updates, limited coverage, and low accuracy.  This study leverages multi-source remote sensing data to monitor agricultural drought in Heilongjiang Province, China.  We developed multi-source composite drought indices (MCDIs) at various timescales (3, 6, 9, and 12 months) by integrating precipitation, land surface temperature, soil moisture, and vegetation indices.  Utilizing remote sensing data from various sources, we calculated a series of single drought indices, which are the precipitation condition index, soil moisture condition index, vegetation condition index, and temperature condition index.  These are then integrated into MCDIs using a multivariable linear regression approach.  The analysis reveals that MCDIs correlate more with standardized precipitation evapotranspiration index (SPEI) than single drought indices.  When examining the correlation between different MCDIs and the affected area of crops and major grain production, MCDI-9 showed the highest correlation with the affected area of crops, while MCDI-12 showed the highest correlation with grain production.  This suggests that these two MCDIs at different timescales are better indicators of agricultural drought.  The spatio-temporal analysis of MCDI indicates that drought in Heilongjiang Province primarily occurs in early spring, gradually spreading from the Greater Khingan Mountains region to the southeastern plains.  The drought gradually alleviates during the summer, ending by the autumn harvest period.  Therefore, the MCDIs constructed in this study can serve as effective methods and indicators for drought monitoring in Heilongjiang Province and similar regions.

Cotton (Gossypium spp.), a globally important cash crop, is increasingly threatened by abiotic stresses that significantly affect yield and fiber quality. In this study, data on 3,016 abiotic stress-related quantitative trait loci (QTLs) described in 31 published papers were integrated through meta-QTL analysis, a total of 34 MQTLs were identified. Nine major MQTLs with numerous initial QTLs, high R² values, narrow confidence intervals (CIs), and close colocalizations were successfully detected. Combined with the transcriptome data, the candidate gene GhPCMP-E17 was identified. Through virus-induced gene silencing (VIGS) technology, the role of GhPCMP-E17 in the response to abiotic stress was clarified. Compared with the TRV:00 plants, the GhPCMP-E17-silenced plants presented more severe wilting and yellowing under drought and salt stress conditions. Silencing GhPCMP-E17 weakens the function of antioxidant enzymes, thereby increasing the accumulation of reactive oxygen species. These results indicate that downregulation of GhPCMP-E17 gene expression enhances the sensitivity of cotton plants to drought and salt stress. This research provides excellent genetic resources for adaptive abiotic crop breeding in upland cotton.