Abstract
Against the background of global warming, drought has become a prominent agrometeorological disaster affecting soybean production in Northeast China (NEC). The development of soybean drought indicators in NEC, based on comprehensive analysis of disaster processes, would greatly enhance dynamic monitoring and early warning systems for soybean drought. This research has significant implications for regional drought prevention and effective disaster mitigation strategies. In this study, the spatial variability of the water surplus and deficit index (\(\:{D}_{n,i}\)) was eliminated, the new index \(\:{CD}_{50,i}\) was constructed, and the initial discriminant value of drought was determined by inverting the historical drought disaster processes of soybean drought. The Kolmogorov‒Smirnov (K–S) test was conducted to determine the optimal distribution model of the sample sequence, and the t-distribution interval estimation method was used to obtain the indicator level threshold. Based on the newly constructed soybean drought indicators, soybean drought risk assessments were carried out. The findings demonstrated that the drought duration days (\(\:D\)) estimated according to \(\:{CD}_{50,i}\ge\:0.56\) as the dominant factor and the daily cumulative value (\(\:CV\)) with \(\:{CD}_{50,i}\ge\:0.56\) as the auxiliary factor could be used to monitor soybean drought in NEC more accurately, and the accuracy rate of the indicators reached 82.4%. There were spatial differences in the probability of each drought level. In terms of the drought risk level, the high-risk area was distributed mainly in the eastern part of Heilongjiang Province, and the low-risk area was distributed mainly in the central and western parts of the East Four Leagues, the western part of Liaoning Province, and a small part of Heilongjiang and Jilin Provinces. The results of this study can be used to dynamically monitor early warning signs of soybean drought so that drought assessment has greater pertinence and provides a technical guarantee for high, stable and efficient soybean production.