基于SWAP-IES的旱区春小麦长势和产量模拟

基于观测数据和作物模型相同化的田块尺度作物生长监测，对于农田精准管理具有重要意义。为构建能准确模拟旱区春小麦长势和产量的同化模拟模型，该研究利用SWAP(soil-water-atmosphere-plant)模型和迭代集合平滑器算法(iterative ensemble smoother,IES)，构建了适合旱区春小麦的SWAP-IES同化模拟系统，并利用2019—2020年田间观测试验数据，评估了同化叶面积指数(leaf area index,LAI)、土壤水分(soil water content,SW)及其组合在旱区春小麦生长模拟和估产中的作用。结果表明，相较于无同化情景，在吸收6次土壤水分观测数据后，模型对土壤水分模拟的R²从0.48提升到0.87。同化LAI时，各水分胁迫处理下LAI的模拟精度均最高，R²从无同化的0.35～0.62提升到0.76～0.96。同化LAI+SW时，各处理对生物量模拟的精度均最高，R²从无同化的0.40～0.67提升到0.73～0.96。轻度水分胁迫处理(T4～T5)下，仅同化LA...

Numerical simulation of spring wheat growth and yield in arid areas based on SWAP-IES

Field scale crop growth simulation based on the assimilation of observation data and crop growth models is an important method for optimizing field management, agricultural auxiliary decision-making, and crop growth evaluation, which is of great significance for precise management of farmland. In order to construct a numerical model that can accurately simulate the growth and yield of spring wheat in arid areas, this study combined the SWAP (soil water atmosphere plant) model with the IES (iterative ensemble smoother) algorithm to construct a SWAP-IES assimilation simulation system suitable for simulating spring wheat growth in arid areas. Using field test data from 2019 to 2020, the roles of soil water content (SW), leaf area index (LAI), and soil water combination in simulating the growth and yield of spring wheat in arid areas were evaluated, and the impact of assimilation data observation on the yield estimation accuracy of the SWAP-IES system was analyzed and evaluated. The results indicate that: 1) When only assimilating SW, the SWAP-IES system had the highest accuracy in simulating soil moisture (R²=0.87), indicating that assimilating soil moisture can lay the foundation for accurately simulating water stress conditions, evapotranspiration, etc. 2) When there was no assimilation, the R² of the SWAP-IES system for simulating LAI, plant height, and biomass of spring wheat ranged from 0.31 to 0.67, and all treatments were below medium accuracy. The accuracy of LAI simulation for spring wheat was significantly improved when assimilating LAI (R² between 0.76 and 0.96 for each treatment), while LAI+SW had the highest simulation accuracy for spring wheat biomass (R² between 0.73 and 0.92 for each treatment). There was no obvious pattern in plant height, and the simulation of plant height by assimilating LAI and LAI+SW achieved high accuracy (R² ranging from 0.71 to 0.96). Thus, it was necessary to select appropriate observation variables for assimilation simulation based on the research purpose. 3) The accuracy of the SWAP-IES system in predicting spring wheat yield without assimilation was relatively low, with R² of 0.45 and SRE ranging from 10.89% to 40.34%. The yield estimation had improved when assimilating SW, but the results were still of medium accuracy. The accuracy of yield estimation significantly was improved when assimilating LAI (R² was 0.79), while the overall accuracy of spring wheat yield simulation is the highest when assimilating LAI+SW (R² was 0.87). During the two years, T1, T2, and T3 treatments have the lowest estimated SRE when assimilating LAI+SW (SRE ranging from 3.87% to 8.38%), while T4 and T5 treatments had the lowest estimated SRE when assimilating LAI (all within 10%). 4) The effect of assimilating LAI+SW at flowering stage in single growth period observation data on improving the yield estimation accuracy of SWAP-IES system was the greatest (R² increased from 0.45 without assimilation to 0.74), followed by assimilating observation data at jointing stage and booting stage. Assimilating observation data from multiple growth stages can significantly improve the accuracy of the model''s yield estimation. When assimilating LAI+SW observation data from jointing and flowering stages, the estimated yield R² was 0.79, while when assimilating LAI+SW observation data from jointing, booting, and flowering stages, the estimated yield R² reached 0.83. The SWAP-IES assimilation simulation system constructed in this study can effectively simulate the growth and yield formation process of spring wheat under different water conditions by integrating LAI+SW observation data of key growth stages such as flowering and jointing stages, especially under water stress conditions. The results can provide valuable information for using observation equipment such as drones and ground cameras to carry out spring wheat growth monitoring, yield estimation, and precise management under different water management conditions in arid areas.