Environmental influences upon energy balance in areas of different vegetation types （i.e., forest at Kog-Ma in Thailand and at Yakutsk in Russia, grassland at Amdo in Chinese Tibet and at Arvaikheer in Mongolia, and mixed farmland at Tak in Thailand） in the GEWEX Asian Monsoon Experiment were investigated. The sites we investigated are geographically and climatologically different; and consequently had quite large variations in temperature （T）, water vapor pressure deficit （VPD）, soil moisture （SM）, and precipitation （PPT）. During May- October, the net radiation flux （Rn） （in W·m^-2） was 406.21 at Tak, 365.57 at Kog-Ma, 390.97 at Amdo, 316.65 at Arvaikheer, and 287.10 at Yakutsk. During the growing period, the Rn partitioned into latent heat flux （2E/Rn） was greater than that partitioned into sensible heat flux （H/Rn） at Tak and at Kog-Ma. In contrast, 2E/Rn was lower than H/Rn at Arvaikheer, H/Rn was less than 2E/Rn between DOY 149 and DOY 270 at Amdo, and between DOY 165 and DOY 235 at Yakutsk. The R, partitioned into ground heat flux was generally less than 0.15. The short-wave albedo was 0.12, 0.18, and 0.20 at the forest, mixed land, and grass sites, respectively. At an hourly scale, energy partitions had no correlation with environmental factors, based on average summer half- hourly values. At a seasonal scale energy partitions were linearly correlated （usually p 〈 0.05） with T, VPD, and SM. The 2E/Rn increased with increases in SM, T, and VPD at forest areas. At mixed farmlands, ）λE/Rn generally had positive correlations with SM, T, and VPD, but was restrained at extremely high values of VPD and T. At grasslands, λE/Rn was enhanced with increases of SM and T, but was decreased with VPD.
Fengshan LIUFulu TAOShenggong LIShuai ZHANGDengpan XIAOMeng WANG
Early crop yield forecasting is important for food safety as well as large-scale food related planning. The phenology-adjusted spectral indices derived from Moderate Resolution Imaging Spectroradiometer （MODIS） data were used to develop liner regression models with the county-level corn yield data in Northeast China. We also compared the different spectral indices in predicting yield. The results showed that, using Enhanced Vegetation Index （EVI）, Normalized Difference Water Index （NDWI） and Land Surface Water Index （LSWI）, the best time to predict corn yields was 55-60 days after green-up date. LSWI showed the strongest correlation （R2=0.568）, followed by EVI （R2=0.497） and NDWI （R2=0.495）. The peak correlation between Wide Dynamic Range Vegetation Index （WDRVI） and yield was detected 85 days after green-up date （RZ=0.506）. The correlation was generally low for Normalized Difference Vegetation Index （NDVI） （R2=0.385） and no obvious peak correlation existed for NDVI. The coefficients of determination of the different spectral indices varied from year to year, which were greater in 2001 and 2004 than in other years. Leave-one-year-out approach was used to test the performance of the model. Normalized root mean square error （NRMSE） ranged from 7.3 to 16.9% for different spectral indices. Overall, our results showed that crop phenology-tuned spectral indices were feasible and helpful for regional corn yield forecasting.
