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In order to propose a more accurate forecast methods suitable for the reference crop evapotranspiration of rice in Jiangxi Province, this paper uses the PM formula and the historical meteorological data of 26 meteorological stations to calculate ET0, and uses this as a reference value to calibrate the Hargreaves-Samani (HS) model, the Blaney-Criddle (BC) model and the McCloud(MC) model. Weather forecast data is used to evaluate the applicability of the three ET0 forecast models at various stations in Jiangxi Province. The results show that after parameter calibration, the average accuracy rates of ET0 forecasts of HS model, BC model and MC model at 26 stations are 86.63%, 85.81%, 87.12%, and the average absolute errors are 0.75, 0.80 and 0.76 mm/d, the average root mean square errors are 0.98, 1.00, 0.99 mm/d, and the average correlation coefficients are 0.76, 0.76, 0.78, respectively. 16 stations recommend using the HS model for ET0 forecasting for the stations in Jiangxi Province, the HS model has the best forecast accuracy, followed by the MC model, and the BC model is the worst. On the whole, using the calibrated HS model for forecasting can provide more accurate ET0 forecasting data for irrigation forecasting and decision-making in Jiangxi Province. [ABSTRACT FROM AUTHOR]

In order to better apply the light energy utilization model in subtropical evergreen coniferous forest areas, take Qianyanzhou, Taihe County, Jiangxi Province as an example, using satellite remote sensing data（GLASS LAI leaf area index product data set） from 2003 to 2005, MODIS surface reflectance product data set（MOD09 A1） and MODIS GPP product（MOD17 A2）) and China Terrestrial Ecosystem Flux Research Network（ChinaFLUX） the flux observation data of evergreen coniferous forests in Qianyanzhou, fraction of absorbed photosynthetically active radiation（FPAR）, 7 kinds of moisture limitation factors（fθ） and 3 kinds of temperature limitation factors（ft） in the 13 different energy efficiency models were reconstructed. A better model combination was obtained by comparing the coefficient of determination and the root mean square error（RMSE） of the flux station observations with the estimated values of 273 model combinations. At the same time, the optimization model was used to estimate gross primary productivity（GPP） and conduct sensitivity analysis. The results showed that the optimal model combination obtained by optimizing: the photosynthetically active radiation absorption ratio expressed in EVI, the moisture influence factor fθ-3 PG in the 3 PG model, and the temperature influence factor ft-TEM in the TEM model（R²=0.86, RMSE=0.47 μmol/m²·s） had the best simulation effect. The GPP simulation values of the optimized model were better than MODIS terrestrial level four standard data products（MOD17 A2）. Sensitivity analysis resultsshowed that the maximum light quantum efficiency αmax,enhanced vegetation index（ EVI） and photosynthetically active radiation（ PAR） were the direct linear variables of the model and had the greatest impact. Other parameters were determined by the order from big to small was light and minimum temperature Tmin,photosynthetic optimum temperature Topt,monthly average temperature T,and photosynthetic maximum temperature Tmax. Therefore,the optimization model in this paper had strong practical significance and was of great significance to further improve the accuracy of GPP estimation. [ABSTRACT FROM AUTHOR]