为模拟详细的温度分布信息,进一步理解地表能量平衡过程,提高植被冠层温度反演精度,根据土壤阴影表面和光照表面的热源和蒸发速率的差异,扩展了CU P ID模型,实现了光照和阴影土壤组分温度分布模拟。采用实测数据分别对冬小麦和夏玉米冠...为模拟详细的温度分布信息,进一步理解地表能量平衡过程,提高植被冠层温度反演精度,根据土壤阴影表面和光照表面的热源和蒸发速率的差异,扩展了CU P ID模型,实现了光照和阴影土壤组分温度分布模拟。采用实测数据分别对冬小麦和夏玉米冠层下的土壤组分温度进行了模拟和验证。在冬小麦地,模拟光照和阴影土壤温度绝对差值为2.8 K和2.4K,平均差值为-1.5 K和-0.7 K;在夏玉米地,模拟与实测温度绝对偏差为3.8 K左右,平均偏差为-0.5 K。总体来说,模拟与实测数据吻合较好,说明扩展模型能够较为真实地反映土壤组分温度分布及其变化。扩展模型可在组分温度反演和农业旱情监测等领域得到应用。展开更多
An Ensemble Kalman Filter(EnKF)-based assimilation algorithm was implemented to estimate root-zone soil moisture(RZSM)using a Soil-Vegetation-Atmosphere Transfer(SVAT)model during a complete growing season of corn in ...An Ensemble Kalman Filter(EnKF)-based assimilation algorithm was implemented to estimate root-zone soil moisture(RZSM)using a Soil-Vegetation-Atmosphere Transfer(SVAT)model during a complete growing season of corn in Central Mexico.Synthetic and field soil moisture(SM)observations and NASA SMAP SM retrievals were used to understand the effect of vertically spatial updates and uncertainties in meteorological forcings on RZSM estimates.Assimilation of RZSM every 3 days using SM observations at 4 depths lowered the averaged standard deviation(ASD)and the root mean square error(RMSE)by 60%and 50%,respectively,compared to the open-loop ASD.The assimilation of synthetic SM at the top 0-5 cm obtained RZSM closer to observations compared to THEXMEX-18 SM measurements and SMAP SM retrievals.Differences between EnKF estimates and SM observations and SMAP SM retrievals are mainly due to misrepresentation of vegetation conditions.The results improved SM estimates up to 10-cm depth using SMAP SM retrievals;however,additional studies are needed to improve SM at deeper layers.The implemented methodology can estimate SM at the top 10 cm of the soil every 3 days to mitigate the impact of the climate change on agricultural production over rainfed areas,particularly in developing countries.展开更多
基金supported by Consejo Nacional de Ciencia y Tecnología and the Mexican Space Agency[AEM-2017-01-292774]Instituto Politécnico Nacional[SIP-2018-1090,SIP-2020-1876]National Aeronautics and Space Administration[Terrestrial Hydrology Program-NNX16AQ24G].
文摘An Ensemble Kalman Filter(EnKF)-based assimilation algorithm was implemented to estimate root-zone soil moisture(RZSM)using a Soil-Vegetation-Atmosphere Transfer(SVAT)model during a complete growing season of corn in Central Mexico.Synthetic and field soil moisture(SM)observations and NASA SMAP SM retrievals were used to understand the effect of vertically spatial updates and uncertainties in meteorological forcings on RZSM estimates.Assimilation of RZSM every 3 days using SM observations at 4 depths lowered the averaged standard deviation(ASD)and the root mean square error(RMSE)by 60%and 50%,respectively,compared to the open-loop ASD.The assimilation of synthetic SM at the top 0-5 cm obtained RZSM closer to observations compared to THEXMEX-18 SM measurements and SMAP SM retrievals.Differences between EnKF estimates and SM observations and SMAP SM retrievals are mainly due to misrepresentation of vegetation conditions.The results improved SM estimates up to 10-cm depth using SMAP SM retrievals;however,additional studies are needed to improve SM at deeper layers.The implemented methodology can estimate SM at the top 10 cm of the soil every 3 days to mitigate the impact of the climate change on agricultural production over rainfed areas,particularly in developing countries.
