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Precise and timely prediction of crop yields is crucial for food security and the development of agricultural policies. However, crop yield is influenced by multiple factors within complex growth environments. Previous research has paid relatively little attention to the interference of environmental factors and drought on the growth of winter wheat. Therefore, there is an urgent need for more effective methods to explore the inherent relationship between these factors and crop yield, making precise yield prediction increasingly important. This study was based on four type of indicators including meteorological, crop growth status, environmental, and drought index, from October 2003 to June 2019 in Henan Province as the basic data for predicting winter wheat yield. Using the sparrow search algorithm combined with random forest (SSA-RF) under different input indicators, accuracy of winter wheat yield estimation was calculated. The estimation accuracy of SSA-RF was compared with partial least squares regression (PLSR), extreme gradient boosting (XG-Boost), and random forest (RF) models. Finally, the determined optimal yield estimation method was used to predict winter wheat yield in three typical years. Following are the findings: 1) the SSA-RF demonstrates superior performance in estimating winter wheat yield compared to other algorithms. The best yield estimation method is achieved by four types indicators' composition with SSA-RF) (R2 = 0.805, RRMSE = 9.9%. 2) Crops growth status and environmental indicators play significant roles in wheat yield estimation, accounting for 46% and 22% of the yield importance among all indicators, respectively. 3) Selecting indicators from October to April of the following year yielded the highest accuracy in winter wheat yield estimation, with an R2 of 0.826 and an RAISE of 9.0%. Yield estimates can be completed two months before the winter wheat harvest in June. 4) The predicted performance will be slightly affected by severe drought. Compared with severe drought year (2011) (R2 = 0.680) and normal year (2017) (R2 = 0.790), the SSA-RF model has higher prediction accuracy for wet year (2018) (R2 = 0.820). This study could provide an innovative approach for remote sensing estimation of winter wheat yield, yield. [ABSTRACT FROM AUTHOR]

[Display omitted] • A drought risk assessment framework was constructed using the random forest algorithm. • The drought vulnerability dimension was enriched by considering the impact of ecosystem resilience. • The spatial distribution pattern of drought risk under the influence of ecosystem resilience was assessed. Drought caused by global warming has a profound impact on the stability of terrestrial ecosystems and agricultural production. As one of the key indicators to measure the response of ecosystems to climate change, the spatial difference of ecosystem resilience will also affect the accuracy of regional drought risk assessment. In this study, the 12-month standardized precipitation evapotranspiration index (SPEI-12) was used to identify drought characteristics and combined with meteorological elements to characterize drought hazard. Drought vulnerability was characterized using crop and environmental sensitivity, disaster prevention, and mitigation capacity. Specifically, ecosystem resilience was also considered in assessing vulnerability. And drought exposure was investigated by considering crop sown area and population density. Based on this, a drought risk assessment framework was constructed using the random forest algorithm and applied to the Huang-Huai-Hai (HHH) Plain, China. The results showed that: (1) approximately 66.34 % of the HHH Plain had a drought hazard level above moderate; among them, southern Hebei, northern Anhui, northern Henan, and scattered small areas in Shandong were extreme high hazard areas. (2) The northeast Anhui, central and southern Shandong, and northeast Jiangsu were extreme high drought vulnerability areas, accounting for 10.34% of the total area. (3) The drought risk in the HHH Plain was low in the south and north, and high in the center. Among these, the drought risk in the border regions of Shandong and Anhui, the northeast part of Jiangsu, as well as central Henan were extreme high, accounting for 11.23%. (4) The drought risk assessment framework constructed by considering ecosystem resilience was more suitable for HHH Plain. This study result could provide scientific reference for managing water resources and preventing drought. [ABSTRACT FROM AUTHOR]