## 4 Discussion
### Future Research  
Satellite SIF data have advantages in drought detection and assessment, but the temporal and spatial resolutions of the continuous SIF data currently available are relatively coarse [43]. Thus, it is difficult to monitor and evaluate large-scale crops of a single species based on the original satellite SIF data directly. The distribution of sugarcane-planted regions is also not contiguous in a large area [63]. Following previous studies [83; 84], we tried to use downscaled SIF to detect the response of sugarcane to drought stress in this work. In the next few years, multiple satellite remote sensing sensors can be used for SIF inversion, which will significantly improve the spatial resolution of SIF. For example, the first global mission specifically designed for SIF measurements of terrestrial vegetation,the fluorescence detector (FLEX), is scheduled to launch [85]. Mission concepts all rely on a single payload fluorescence imaging spectrometer with a spectral range from 500 to 780 nm [43; 85]. The spectrometer of FLEX has a strip width of 150 km and a spatial resolution of 300 \(\times\) 300 m [43; 85]. In addition, the products of OCO-2, OCO-3, TROPOMI and FLEX will complement each other in spectral resolution, spatial coverage, data acquisition and repetition period [86]. The synergistic use of these products will provide unprecedented opportunities for the real-time monitoring of crop drought, vegetation productivity and the terrestrial carbon cycle.  
Many previous studies have suggested that integrated drought indices, which consider multiple aspects of agricultural drought, such as vegetation conditions, soil moisture, LST, and evapotranspiration, outperform the single variable drought index in monitoring capability [87; 88]. The integrated drought index based on downscaled satellite SIF and an automated sugarcane drought monitoring system should be developed to monitor sugarcane drought in the future. Furthermore, sugarcane growth status changes relatively rapidly and satellite SIF cannot cover sugarcane-planting areas in time, which makes the actual early response of sugarcane to drought stresses potentially undetectable by satellite SIF. There may be various ground objects, such as sugarcane and corn, in a pixel of the downscaled satellite SIF, which may cause certain errors in the response of sugarcane to drought detected by downscaled satellite SIF. Therefore, ground experiments are needed to detect more details about the response of sugarcane to drought stress in future research.  
Many previous studies have used SIF to analyze the effects of different stresses on crops. For example, SIF was used to detect and quantify the effects of iron deficiency on rice seedlings [89], nutrient deficiency on soybean [90], phosphorus deficiency on cucumber [91], Cymidium ringspot virus on tobacco plants [92], stripe rust on wheat [93], heat stress on winter wheat [27], and cold stress on faba beans [94]. These previous studies mainly quantified the effects of one environmental stress on a specific crop, and suggested that SIF has a great potential for crop stress detection. However, crops are likely to suffer from multiple environmental stresses simultaneously, and thus, further research, especially research based on SIF, is needed to determine which stress is the actual problem for crops.