Constructing the drought dynamic threshold of spring maize in Northeast China using SIF index

Spring maize is suffering from the ever-increasing drought under global warming in Northeast China in recent years. Most previous studies focused on the disaster level threshold of segmented agrometeorological drought, according to the crop development stage. However, it cannot fully meet the practical application under complex conditions at present. This study aims to construct the drought dynamic threshold of spring maize in Northeast China from 2000 to 2020. Time series solar-induced chlorophyll fluorescence (SIF) index with the actual data of drought disaster was utilized to construct the drought and drought-free sample sets. Binomial, trinomial and Gaussian fitting were selected to determine the different drought level curves, according to the curve change of SIF value in the study period. The optimal fitting model was obtained using the coefficient of determination (R²), Akaike information criterion (AIC) and Bayesian information criterion (BIC). The average of the fitting curves for the adjacent drought levels was then taken as the dynamic critical threshold of each drought level during the whole growth period of maize. The optimal dynamic critical threshold was finally verified by independent samples and typical drought events. The results showed that the Gaussian fitting model was more effective in representing the SIF values of different drought levels, compared with the binomial and trinomial fitting. The dynamic thresholds of different drought levels better represented the actual drought situation of spring maize. The drought level identification was fully consistent with the actual disaster level in 82.76% of cases, and basically consistent with the actual disaster level in 91.03% of cases, indicating the high verification accuracy. Taking the typical drought event in Liaoning Province as an example, the drought process was verified by the threshold value. The times of drought occurrence and end were completely consistent with the actual disaster records. Spatially, the drought-affected areas were also consistent in the actual disaster records. Taking Chifeng City as a typical drought site, the drought events with threshold values were verified to be consistent in both the time and level of drought occurrence. There was basically 100% matching between the actual disaster records and threshold verification in the whole drought, indicating a high verification accuracy. Therefore, the dynamic drought threshold can be expected to better reflect the spatiotemporal evolution characteristics of spring maize drought, including the occurrence and development dynamics of drought disasters. Better recognition of drought level was achieved in the dynamic drought threshold, compared with the segmented threshold in the development stage. But some challenges also remained. The SIF data makes it difficult to capture the phenomenon of sudden drought, indicating the drought duration less than 8 days. In addition, there is a certain impact on the identification, because the phenology of maize varies in the climate change, leading to the advanced or postponed seedling stage.