Climate change is one of the most important challenges threatening agricultural grain yield and food security. Determining the factors influencing grain yield in Jilin Province and the weights of their contribution are a very important task, because Jilin Province is an important agriculture base in China. In this study, the accumulation factor sequence evaluating data method was used to analyze the climate and economic-technical factor contribution weights to grain yield and grain yield changes in each city of Jilin Province. Climate yield was also estimated to study the climate effect on the grain yield, and it was calculated in two ways:an improved algorithm and a traditional quadratic method. The results show that the climate and economic-technical factors have different contribution weights to grain yield in different cities in Jilin Province. The contribution weight of the climate factor to grain yield was 0.212-0.349, while that the economic-technical factor was 0.651-0.788. Furthermore, the changes of the climate factor contributing to grain yield changes accounted for 0.296-0.546, and the changes of the economic-technical factor accounted for 0.454-0.704. The weights of climate and economic-technical factor contributing to grain yield are very different between the eastern and western cities in Jilin Province, but their weights contributing to the grain yield change are similar in these cities. In general, the amount of fertilizer used per hectare (FUPH) is the main factor affecting grain yields and yield changes from 1980 to 2008. It is noted that when the FUPH growth rate stabilized after 1995, the effects of the climate factor on the grain yield become more obvious than before. The improved algorithm is effective for estimating climate yield in Jilin Province, and the climate yields were mostly between-500 kg/ha and 500 kg/ha, and showed a slightly rising trend in most cities.
The fraction of photosynthetically active radiation(FPAR) is a key variable in the assessment of vegetation productivity and land ecosystem carbon cycles.Based on ground-measured corn hyperspectral reflectance and FPAR data over Northeast China,the correlations between corn-canopy FPAR and hyperspectral reflectance were analyzed,and the FPAR estimation performances using vegetation index(VI) and neural network(NN) methods with different two-band-combination hyperspectral reflectance were investigated.The results indicated that the corncanopy FPAR retained almost a constant value in an entire day.The negative correlations between FPAR and visible and shortwave infrared reflectance(SWIR) bands are stronger than the positive correlations between FPAR and near-infrared band reflectance(NIR).For the six VIs,the normalized difference vegetation index(NDVI) and simple ratio(SR) performed best for estimating corn FPAR(the maximum R2 of 0.8849 and 0.8852,respectively).However,the NN method esti-mated results(the maximum R2 is 0.9417) were obviously better than all of the VIs.For NN method,the two-band combinations showing the best corn FPAR estimation performances were from the NIR and visible bands;for VIs,however,they were from the SWIR and NIR bands.As for both the methods,the SWIR band performed exceptionally well for corn FPAR estimation.This may be attributable to the fact that the reflectance of the SWIR band were strongly controlled by leaf water content,which is a key component of corn photosynthesis and greatly affects the absorption of photosynthetically active radiation(APAR),and makes further impact on corn-canopy FPAR.
