排序方式： 共有71条查询结果，搜索用时 15 毫秒
31.
本文比较了基于AMSR-E被动微波数据的3种土壤冻融判别算法在青藏高原相关地区的分类精度。3种算法分别是：双指标算法、决策树算法、判别函数算法。本文选取了来自青藏高原那曲、玛曲、阿里3个地区土壤温湿度观测网的地表温度数据,并结合AMSR-E被动亮温数据,对上述算法在以上地区的分类精度分别进行了比较评价。结果表明：不论是白天还是夜间,相较于干旱区微波信号来自深层土壤的难以准确探测,在青藏高原半湿润半干旱区算法可取得相对较好的判别准确率;双指标算法相较于其他2种算法,在观测区具有较高的分类精度,且夜间分类精度高于白天;实测数据存在资料代表性不普遍即网格所包含站点信息量不够的问题,这也是后续工作中提高分类精度值得关注的着手点。 相似文献
32.
在利用微波辐射计进行对地观测的过程中，陆地表面特性参数（如土壤水分、土壤粗糙度和植被冠层）是土壤微波辐射的重要影响因素。地表粗糙度的标定对于利用微波辐射计数据反演地表参数而言是十分重要的工作。地表粗糙度参数（h和Q）随着观测频率而变化。通常的标定方法是，假设h的空间分布是变化的，Q在全球均一地分布，则在沙漠地区首先采取h=0的近似，再对Q进行标定。但是事实上，h和Q在全球的分布都是变化着的，这与地面环境状况有关。以AMSR—E数据为例，在对MPD1分析的基础上，推导给出了简单的、基于理论模型的参数厂。厂可以直接由观测亮温值计算得到，它是一个与土壤水分无关，仅与植被层含水量7．0，和土壤粗糙度σ有关的参量，因此它可以用于地表粗糙度的标定和对植被层含水量、植被生长／变化的估计。本文选择干旱季节里的北非地区，在没有对h采取任何假设的前提下，利用参数厂实现了对地表粗糙度参数h和σ的标定，并与原有标定方法的结果做了比较分析。 相似文献
33.
一个针对被动微波AMSR-E数据反演地表温度的物理统计算法 总被引：2，自引：0，他引：2
用MODIS的地表温度产品和AMSR-E不同通道之间的亮度温度回归分析表明用89GHzV 做地表温度反演主通道的精度最高. 用AIEM模型模拟表明, 土壤粗糙度和土壤水分变化引起土壤辐射率变化可以通过不同极化波段的差值得到有效的消除, 从而克服了被动微波反演地表温度中辐射率不稳定的困难. 通过回归系数分析表明, 不同的地表覆盖类型的辐射机制是不同的. 要精确地反演地表温度, 至少对地表分成三种覆盖类型, 即水覆盖的地表、雪覆盖的地表以及非雪和水覆盖的地表. 以MODIS地表温度产品作为评价标准, 物理统计方法的平均精度在2~3℃. 相似文献
34.
A physics-based statistical algorithm for retrieving land surface temperature from AMSR-E passive microwave data 总被引：5，自引：0，他引：5
Mao KeBiao Shi JianCheng Li ZhaoLiang Qin ZhiHao Li ManChun Xu Bin 《中国科学D辑(英文版)》2007,50(7):1115-1120
AMSR-E and MODIS are two EOS (Earth Observing System) instruments on board the Aqua satellite. A regression analysis between the brightness of all AMSR-E bands and the MODIS land surface tem-perature product indicated that the 89 GHz vertical polarization is the best single band to retrieve land surface temperature. According to simulation analysis with AIEM,the difference of different frequen-cies can eliminate the influence of water in soil and atmosphere,and also the surface roughness partly. The analysis results indicate that the radiation mechanism of surface covered snow is different from others. In order to retrieve land surface temperature more accurately,the land surface should be at least classified into three types:water covered surface,snow covered surface,and non-water and non-snow covered land surface. In order to improve the practicality and accuracy of the algorithm,we built different equations for different ranges of temperature. The average land surface temperature er-ror is about 2―3℃ relative to the MODIS LST product. 相似文献
35.
The land surface temperature (LST) is an important parameter when studying the interface between the atmosphere and the Earth's surface. Compared to satellite thermal infrared (TIR) remote sensing, passive microwave (PMW) remote sensing is better able to overcome atmospheric influences and to estimate the LST, especially in cloudy regions. However, methods for estimating PMW LSTs at the country and continental scales are still rare. The necessity of training such methods from a temporally dynamic perspective also needs further investigations. Here, a temporally land cover based look-up table (TL-LUT) method is proposed to estimate the LSTs from AMSR-E data over the Chinese landmass. In this method, the synergies between observations from MODIS (Moderate Resolution Imaging Spectroradiometer) and AMSR-E (Advanced Microwave Scanning Radiometer for EOS), which are onboard the same Aqua satellite, are explored. Validation with the synchronous MODIS LSTs demonstrates that the TL-LUT method has better performances in retrieving LSTs with AMSR-E data than the method that uses a single brightness temperature in 36.5 GHz vertical polarization channel. The accuracy of the TL-LUT method is better than 2.7 K for forest and 3.2 K for cropland. Its accuracy varies according to land cover type, time of day, and season. When compared with the in-situ measured LSTs at four sites without urban warming in the Tibet Plateau, the standard errors of estimation between the estimated AMSR-E LST and in-situ measured LST are from 5.1 K to 6.0 K in the daytime and 3.1 K to 4.5 K in the nighttime. Further comparison with the in-situ measured air temperatures at 24 meteorological stations confirms the good performance of the TL-LUT method. The feasibility of PMW remote sensing in estimating the LST for China can complement the TIR data and can, therefore, aid in the generation of daily LST maps for the entire country. Further study of the penetration of PMW radiation would benefit the LST estimations in barren and other sparsely vegetated environments. 相似文献
36.
The sea surface wind speed (SSWS) derived by a microwave radiometer can be contaminated by changes of the brightness temperature
owing to the angle between the sensor azimuth and the wind direction (Relative Wind Direction effect: RWD effect). We attempt
to apply the method proposed by Konda and Shibata (2004) to the SSWS derived by Advanced Microwave Scanning Radiometer (AMSR)
on Advanced Earth Observing Satellite II (ADEOS-II), in order to correct for the RWD effect. The improvement of accuracy of
the SSWS estimation amounts to roughly 60% of the error caused by the RWD effect. Comparison with in situ observation at the
Tropical Atmosphere Ocean (TAO) array shows that the root mean square error of the corrected SSWS is 1.1 ms−1. It is found that the impact of the RWD effect on the estimation of the latent heat flux can amount to about 30 Wm−2 on average. We applied the method to the SSWS derived by AMSR for Earth Observing System (AMSR-E) and obtained a similar
result. 相似文献
37.
利用美国国家浮标数据中心NDBC和热带大气海洋计划TAO浮标的海表面温度数据,对WindSat 2004年-2013年近10年的海表面温度产品进行了验证。结果表明,在美国沿岸海域,WindSat反演得到的海表面温度的平均偏差为0.10℃,标准偏差为0.59℃;在近赤道太平洋海域,反演得到的海表面温度的平均偏差为-0.15℃,标准偏差为0.33℃。WindSat海表面温度在夏季相对浮标实测值有正偏差增大和负偏差缩小的趋势,在美国东海岸以及墨西哥湾区域部分站点反演得到的海表面温度的标准偏差较大,其标准偏差超过1℃。在5-10 m/s风速段,WindSat海表面温度反演效果比较理想,平均偏差和标准偏差相对恒定。当风速大于12 m/s时,WindSat海表面温度反演的不确定性明显增加。与AMSR-E月平均海表面温度产品对比发现,夏季,WindSat SST较AMSR-E偏低;冬季,WindSat SST较AMSR-E偏高。 相似文献
38.
采用基于质能平衡的积雪过程模型—雪热力模型(Snow Thermal Model, SNTHERM.89）来描述和模拟2008年中国甘肃省黑河实验冰沟流域的积雪过程与积雪特性参数,并将模型模拟的雪水当量与高级微波扫描辐射计(AMSR-E）的雪水当量产品进行了对比。模型验证结果表明,SNTHERM模型能准确模拟黑河冰沟流域的积雪变化过程和积雪特性,对积雪的演变特征作出合理的描述,表明SNTHERM在中国黑河冰沟流域有较好的适用性。对SNTHERM模型进行不同驱动气象参数和初始输入参数的敏感性分析的结果表明,积雪特性参数对辐射通量最敏感;各积雪特性参数对各自的初始输入比较敏感,密度则对初始输入的雪深、密度和颗粒大小都比较敏感,表明在需要准确模拟密度的情况下以及进行雪水当量同化工作时,初始输入的雪层深度、密度和颗粒都必须比较准确。 相似文献
39.
Existing satellite microwave algorithms for retrieving Sea Surface Temperature(Sst)and wind(SSW)are applicable primarily for non-raining cloudy conditions.With the launch of the Earth Observing System (EOS)Aqua satellite in 2002,the Advanced Microwave Scanning Radiometer(AMSR-E)onboard provides some unique measurements at lower frequencies which are sensitive to ocean surface parameters under ad-verse weather conditions.In this study,a new algorithm is developed to derive SST and SSW for hurricane predictions such as hurricane vortex analysis from the AMSR-E measurements at 6.925 and 10.65 GHz.In the algorithm,the effects of precipitation emission and scattering on the measurements are properly taken into account.The algorithm performances are evaluated with buoy measurements and aircraft dropsonde data.It is found that the root mean square (RMS) errors for SST and SSW are about 1.8K and 1.9m s(-1),respectively,when the results are compared with the buoy data over open oceans under precipitating clouds (e.g.,its liquid water path is larger than 0.5 mm),while they are 1.1 K for SST and 2.0 ms(-1)for SSW,respectively,when the retrievals are validated against the dropsonde measurements over warm oceans.These results indicate that our newly developed algorithm catl provide some critical surface information for trop-ical cycle predictions.Currently,this newly developed algorithm has been implemented into the hybrid variational scheme for the hurricane vortex analysis to provide predictions of SST and SSW fields. 相似文献
40.
Applications of AMSR-E Measurements for Tropical Cyclone Predictions Part Ⅰ： Retrieval of Sea Surface Temperature and Wind Speed 总被引：1，自引：0，他引：1
Existing satellite microwave algorithms for retrieving Sea Surface Temperature （SST） and Wind （SSW） are applicable primarily for non-raining cloudy conditions. With the launch of the Earth Observing System （EOS） Aqua satellite in 2002, the Advanced Microwave Scanning Radiometer （AMSRoE） onboard provides some unique measurements at lower frequencies which are sensitive to ocean surface parameters under adverse weather conditions. In this study, a new algorithm is developed to derive SST and SSW for hurricane predictions such as hurricane vortex analysis from the AMSRoE measurements at 6.925 and 10.65 GHz. In the algorithm, the effects of precipitation emission and scattering on the measurements are properly taken into account. The algorithm performances are evaluated with buoy measurements and aircraft dropsonde data. It is found that the root mean square （RMS） errors for SST and SSW are about 1.8 K and 1.9 m s^- 1, respectively, when the results are compared with the buoy data over open oceans under precipitating clouds （e.g., its liquid water path is larger than 0.5 mm）, while they are 1.1 K for SST and 2.0 m s^-1 for SSW, respectively, when the retrievals are validated against the dropsonde measurements over warm oceans. These results indicate that our newly developed algorithm can provide some critical surface information for tropical cycle predictions. Currently, this newly developed algorithm has been implemented into the hybrid variational scheme for the hurricane vortex analysis to provide predictions of SST and SSW fields. 相似文献