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1.
In this study, sea surface salinity(SSS) Level 3(L3) daily product derived from soil moisture active passive(SMAP)during the year 2016, was validated and compared with SSS daily products derived from soil Moisture and ocean salinity(SMOS) and in-situ measurements. Generally, the root mean square error(RMSE) of the daily SSS products is larger along the coastal areas and at high latitudes and is smaller in the tropical regions and open oceans. Comparisons between the two types of daily satellite SSS product revealed that the RMSE was higher in the daily SMOS product than in the SMAP, whereas the bias of the daily SMOS was observed to be less than that of the SMAP when compared with Argo floats data. In addition, the latitude-dependent bias and RMSE of the SMAP SSS were found to be primarily influenced by the precipitation and the sea surface temperature(SST). Then, a regression analysis method which has adopted the precipitation and SST data was used to correct the larger bias of the daily SMAP product. It was confirmed that the corrected daily SMAP product could be used for assimilation in high-resolution forecast models, due to the fact that it was demonstrated to be unbiased and much closer to the in-situ measurements than the original uncorrected SMAP product.  相似文献   
2.
基于SMAP卫星雷达资料的海冰密集度反演技术研究   总被引:1,自引:0,他引:1  
SMAP是美国于2015年初发射的一颗卫星,搭载了L波段的雷达。它采用圆锥扫描方式,具有固定的入射角、较大的幅宽和千米级的分辨率,在海冰监测方面具有独特的优势。本文利用SMAP卫星雷达资料分别与德国Bremen大学海冰密集度产品和美国国家冰雪数据中心(NSIDC)海冰密集度产品建立3.125 km和25 km匹配数据集,分析了L波段雷达后向散射系数、极化比和归一化极化差与海冰密集度之间相关性,建立基于人工神经网络的海冰密集度反演算法。为了验证SMAP卫星雷达资料反演海冰密集度的精度,本文选择德国Bremen大学和美国冰雪数据中心发布的海冰密集度产品分别与SMAP海冰密集度产品进行对比分析,SMAP海冰密集度与Bremen海冰密集度的偏差为0.07、均方根误差为0.14;与NSIDC海冰密集度的偏差为0.04、均方根误差为0.18,这表明SMAP海冰密集度产品与现有业务化海冰密集度产品具有很好的一致性。  相似文献   
3.
针对传统海表盐度遥感反演精度不高、影响因素较少等问题,本文基于SMAP(Soil Moisture Active Passive)卫星L2C(Level 2 C)数据、Argo(Array for Real-time Geostrophic Oceanography)数据和其他辅助数据,以太平洋部分海域(160°E~120°W,0°~30°N)为研究区域,综合考虑海面粗糙度以及白冠覆盖率等参量,利用径向基神经网络建立RBF亮温增量模型,并对平静海面亮温进行修正,然后基于Meissner-Wentz介电常数模型得到反演后的盐度值。验证结果表明:模型预测盐度和SMAP卫星盐度相对于Argo实测盐度的均方根误差分别为0.4和0.5,平均绝对误差分别为0.3和0.4。实验证明,利用RBF神经网络建立的亮温增量模型可以提高海表盐度反演的精度,对海表盐度反演具有实用意义。  相似文献   
4.
In this study, the short-term offshore extension of Brahmaputra-Ganges(BG) and Irrawaddy freshwater plumes to the central northern Bay of Bengal(BoB) was investigated based on in situ and satellite observations. In the summer and winter of 2015, two significant freshening events with periods of weeks were observed from a moored buoy at 15°N, 90°E in the BoB. Soil Moisture Active Passive(SMAP) satellite sea surface salinity compares well with the in situ data and shows that these freshening events are directly related to the short-term offshore extension of the BG and Irrawaddy freshwater, respectively. These data combined with the altimeter sea level anomaly data show that the offshore extending plumes result from freshwater modulated by eddies. During summer, the BG freshwater is modulated by a combination of three closely located eddies: a large anticyclonic eddy(ACE) off the northwestern BoB coast and two cyclonic eddies in the northern BoB. Consequently, the freshwater extends offshore from the river mouth and forms a long and narrow tongue-shaped plume extending southwestward to the central BoB. During winter, the Irrawaddy freshwater is modulated by two continuous ACEs evolved from Rossby wave propagating westward from the Irrawaddy Delta off Myanmar, forming a tongueshaped plume extending to the central BoB. Strong salinity fronts are formed along the boundaries of these tongue-shaped plumes. These findings confirm good capability of the SMAP data to investigate the short-term offshore extension of the BG and Irrawaddy freshwater. This study provides direct evidences of the pathways of the offshore extension of the BG and Irrawaddy freshwater and highlights the role of eddies in the northern BoB freshwater plume variability.  相似文献   
5.
The coarse resolution soil moisture (SM) data from NASA SMAP mission have been steadily produced with the expected performance since April 2015. These coarse resolution observations could be downscaled to fine resolution using fine scale observations of SM sensitive quantities from existing satellite sensors. For operational users who need near-real-time (NRT) high resolution SM data, the downscaling approach should be feasible for operational implementation, requiring limited ancillary information and primarily depending on readily available satellite observations. Based on these principles, nine potential candidate downscaling schemes were selected for developing an optimal downscaling strategy. Using remotely sensed land surface temperature (LST) and enhanced vegetation index (EVI) observations, the optimal downscaling approach was tested for operational producing a NRT 1 km SM data product from SMAP. Comprehensive assessments on the 1 km SM product were conducted based on agreement statistics with in-situ SM measurements. Statistical results show that the accuracy of the original coarse spatial resolution SMAP SM product can be significantly improved by 8% by the downscaled 1 km SM. With respect to the in-situ measurements, the 1 km SM mapping capability developed here presents a clear advantage over the SMAP/Sentinel SM data product; and it also provides better data availability for users. This study suggests that a NRT 1 km SMAP SM data product could be routinely generated from SMAP at the centre for Satellite Applications and Research of NOAA NESDIS for operational users.  相似文献   
6.
针对传统海表盐度的物理机制反演模型拟合过程复杂且反演精度不高等问题,借助大范围、全天时、L波段探测的SMAP卫星微波海洋遥感产品,以北太平洋(135°~165°E,15°~45°N)范围为研究海域,利用深层神经网络(Deep Neural Network,DNN)和支持向量机(Support Vector Machine,SVM)建立海表盐度(Sea Surface Salinity,SSS)遥感反演模型。验证结果表明:DNN与SVM模型测试集反演SSS与Argo(Array for Real-time Geostrophic Oceanography)实测SSS的均方根误差(Root Mean Square Error,RMSE)分别为0.1790和0.2570,平均绝对误差(Mean Absolute Error,MAE)为0.1293和0.1821,最小绝对误差为0.6426和2.0380,最大绝对误差为1.3241和2.3732,反演模型数据与实测Argo数据拟合后的的相关系数分别为0.89和0.84。总体来看,DNN模型比SVM模型的反演精度更高,但两者均显著提高了SMOS盐度产品精度,能够为相关研究提供数据支撑。  相似文献   
7.
海表盐度是研究海洋变化及其气候效应重要的物理量。本文将2018年SMAP卫星的月均、日均海表盐度产品分别与Argo月均网格化产品、实时散点盐度数据进行比较,评定其精度,并分析全球海表盐度分布特征。结果表明:SMAP卫星月均产品RMSE为0.17,BIAS为0.11,STD为0.17,R为0.98,t检验呈显著相关;SMAP卫星日均产品RMSE为0.28,BIAS为0.23,STD为0.26,R为0.81,相较月均产品,精度较低。SMAP卫星月均产品偏差在中纬度海域较小,在高纬度海域较大;SMAP卫星日均产品偏差在太平洋海域为-0.6~0.6,在地中海海域超过1.0。全球海表盐度在25.0~40.0之间,沿纬度方向呈带状分布,其中大西洋海表盐度普遍高于太平洋和印度洋。  相似文献   
8.
Surface soil moisture is an important parameter in hydrology and climate investigations. Current and future satellite missions with L-band passive microwave radiometers can provide valuable information for monitoring the global soil moisture. A factor that can play a significant role in the modeling and inversion of microwave emission from land surfaces is the surface roughness. In this study, an L-band parametric emission model for exponentially correlated surfaces was developed and implemented in a soil moisture retrieval algorithm. The approach was based on the parameterization of an effective roughness parameter of Hp in relation with the geometric roughness variables (root mean square height s and correlation length l) and incidence angle. The parameterization was developed based on a large set of simulations using an analytical approach incorporated in the advanced integral equation model (AIEM) over a wide range of geophysical properties. It was found that the effective roughness parameter decreases as surface roughness increases, but increases as incidence angle increases. In contrast to previous research, Hp was found to be expressed as a function of a defined slope parameter m = s2/l, and coefficients of the function could be well described by a quadratic equation. The parametric model was then tested with L-band satellite data in soil moisture retrieval algorithm over the Little Washita watershed, which resulted in an unbiased root mean square error of about 0.03 m3/m3 and 0.04 m3/m3 for ascending and descending orbits, respectively.  相似文献   
9.
Soil moisture prediction is of great importance in crop yield forecasting and drought monitoring. In this study, the multi-layer root zone soil moisture (0-5, 0-10, 10-40 and 40-100 cm) prediction is conducted over an agriculture dominant basin, namely the Xiang River Basin, in southern China. The support vector machines (SVM) coupled with dual ensemble Kalman filter (EnKF) technique (SVM-EnKF) is compared with SVM for its potential capability to improve the efficiency of soil moisture prediction. Three remote sensing soil moisture products, namely SMAP, ASCAT and AMSR2, are evaluated for their performance in multi-layer soil moisture prediction with SVM and SVM-EnKF, respectively. Multiple cases are designed to investigate the performance of SVM, the effectiveness of coupling dual EnKF technique and the applicability of the remote sensing products in soil moisture prediction. The main results are as follows: (a) The efficiency of soil moisture prediction with SVM using meteorological variables as inputs is satisfactory for the surface layers (0-5 and 0-10 cm), while poor for the root zone layers (10-40 and 40-100 cm). Adding SMAP as input to SVM can improve its performance in soil moisture prediction, with more than 47% increase in the R-value and at least 11% reduction in RMSE for all layers. However, adding ASCAT or AMSR2 has no improvement for its performance. (b) Coupling dual EnKF can significantly improve the performance of SVM in the soil moisture prediction of both surface and the root zone layers. The increase in R-value is above 80%, while the reduction in BIAS and RMSE is respectively more than 90% and 63%. However, adding remote sensing soil moisture products as inputs can no further improve the performance of SVM-EnKF. (c) The SVM-EnKF can eliminate the influence of remote sensing soil moisture extreme values in soil moisture prediction, therefore, improve its accuracy.  相似文献   
10.
文凤平  赵伟  胡路  徐红新  崔倩 《遥感学报》2021,25(4):962-973
土壤水分不仅是陆面过程中重要的变量,同时也是全球水循环中的关键参数。为了获得高分辨率的土壤水分数据,本文将基于自适应窗口的土壤水分降尺度方法应用在闪电河流域,以1 km MODIS产品(地表温度和归一化植被指数)作为辅助数据,对9 km的SMAP被动微波土壤水分(SMAP土壤水分)数据进行降尺度,得到研究区1 km的降尺度土壤水分数据。利用地面站点实测土壤水分和机载被动微波土壤水分(机载土壤水分)对降尺度土壤水分和SMAP土壤水分进行了验证,并对辅助数据和降尺度方法本身展开分析以探讨降尺度过程中的不确定性来源。结果表明:(1)本文使用的基于自适应窗口的土壤水分降尺度方法能够有效地提高SMAP土壤水分的空间分辨率,在进一步丰富土壤水分分布细节变化信息的同时,还能够保留SMAP土壤水分的空间变化特征并与其保持值域一致。(2) 3种基于像元尺度的土壤水分数据(机载土壤水分、SMAP土壤水分和降尺度土壤水分)与站点实测土壤水分之间的相关性并不高,这主要与点、面数据之间的空间匹配不一致、空间代表性不同以及有效验证的数据量有限有关。而与站点数据验证相比,降尺度土壤水分和SMAP土壤水分均和机载土壤水分数据相关性较好。(3) SMAP土壤水分与辅助数据之间的相关性比机载土壤水分与辅助数据之间的较高,而这两种土壤水分数据之间存在的这种偏差主要受到空间尺度、观测配置、参数反演算法和选用的辅助数据等因素的影响。(4)针对验证结果的不确定性,通过增加辅助数据或改变土壤水分估算模型结构进而修改降尺度模型的方式在本研究中并不能显著提高降尺度结果的精度,如何进一步提高降尺度精度仍是未来需要研究的重点。  相似文献   
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