基于SMAP亮温数据反演青藏高原玛曲区域土壤未冻水

未冻水和冰共同存在于冻土中,两者的相互转化即冻融变化深刻影响寒区地表水分循环和能量收支。被动微波遥感技术是土壤水分监测的主要手段,但目前大多应用于非冻结土壤的水分反演,对负温环境下冻结土壤中未冻水的反演研究较少。基于SMAP卫星升轨和降轨时刻的亮温观测数据和经改进后适用于青藏高原地区的零阶微波辐射模型,利用单通道算法（SCA）和双通道算法（DCA）,对青藏高原东部黄河源区玛曲区域季节冻土中的未冻水含量进行反演。结果表明：基于SMAP不同过境时刻亮温观测及不同算法的土壤未冻水反演结果均较同步地反映了研究区实测值的动态变化特征（相关系数R均大于0.9）。其中,基于SMAP降轨时刻亮温观测的反演结果在冻融交替的过渡季节存在明显低估,而基于升轨时刻亮温观测得到的反演结果精度更高。基于垂直极化亮温观测的单通道（SCA-V）和DCA算法得到的升轨时刻的反演值与实测值的无偏均方根误差（ubRMSE）分别为0.035 m³m^-3和0.039 m³m^-3,均达到SMAP任务的设计要求（即ubRMSE≤0.04 m³m^-3）,其中SCA-V对该研究区土壤未冻水的反演精度最高。与SMAP标准产品相比,基于SCA-V算法反演得到的暖季土壤水分精度更高。此外,该算法能成功反演得到冻结期土壤未冻水的动态变化,因此更适用于青藏高原地区冻融土壤条件下的水分反演。     

反演策略对SMOS土壤水分反演算法的影响研究

为降低SMOS土壤水分反演算法的复杂度、提高土壤水分反演精度,对SMOS土壤水分反演策略进行调整：将多参数反演改为单参数反演以简化观测与模拟亮温的代价函数,以固定步长（0.001 m3/m3）代替不定步长从而避免复杂的矩阵运算,将围绕土壤水分先验值的少量局部搜索调整为全土壤水分区间（0~0.05 m³/m³）的密集全局搜索。利用美国USCRN 44个站点实测土壤水分分别与SMOS官方反演的土壤水分和SMOS调整算法反演的土壤水分进行对比分析。结果表明：与SMOS相比,算法调整后土壤水分的平均绝对偏差MAD、均方根误差RMSE和无偏均方根误差ubRMSE分别降低了0.012、0.018和0.020 m3/m3。     

基于Sentinel-1雷达数据的青藏高原地区土壤水分反演研究

土壤水分是地—气能量交换和全球水循环的重要参数之一,也是水文、气象、农业等研究中的关键参数。高空间分辨率的土壤水分在探讨区域水文过程、生态环境保护及农业水资源管理等方面具有重要意义。基于Sentinel-1雷达数据发展了青藏高原地区高空间分辨率土壤水分反演算法,并获取了区域尺度空间分辨率为20 m的土壤水分。该算法首先基于地面数据、Sentinel-1雷达数据和MODIS归一化植被指数对水云模型进行了参数优化,其次利用优化后的水云模型构建了模拟数据库,利用人工神经网络算法对模拟数据进行训练,构建了基于神经网络的土壤水分反演算法。为了检验该算法,利用Sentinel-1雷达数据反演了青藏高原站点区域土壤水分值,并使用站点实测土壤水分数据对其进行了验证。结果表明：土壤水分反演值与站点实测值有良好的一致性,其相关系数为0.784—0.82,均方根误差为0.052 m³/m³—0.064 m³/m³。土壤水分反演值在时间序列上能够捕捉到土壤水分实测值的变化趋势。该研究可为青藏高原地区高空间分辨率的土壤水分监...     

基于地基微波辐射计数据评估不同土壤介电模型反演土壤湿度的适用性

以青藏高原开展的L波段地基微波辐射（ELBARA-III型）综合观测试验为依据,基于 \tau - \omega 辐射传输模型评估了Wang-Schmugge、Mironov、Dobson和 Four-Phase 4种土壤介电模型对L波段微波亮温模拟及土壤湿度反演的影响。结果表明：相同植被和粗糙度参数化方案条件下,4种土壤介电模型对微波亮温模拟存在明显差异,当土壤湿度小于0.23 m3·m^-3时,Wang-Schmugge模型与其他3种土壤介电模型微波亮温模拟结果差异最为显著,水平和垂直极化微波亮温模拟最大差值可达8.0 K和4.4 K;当模拟土壤湿度大于0.23 m3·m^-3时,Four-phase模型模拟的微波亮温显著高于其他3种土壤介电模型模拟结果;当土壤湿度饱和时,4种土壤介电模型间水平和垂直极化微波亮温模拟最大差值约为6.1 K和4.8 K,且4种土壤介电模型对水平极化微波亮温模拟的差异比垂直极化模拟的差异更为显著。而基于4种介电模型的土壤湿度反演对比试验表明,水平极化条件下基于Wang-Schmugge模型反演土壤湿度,较其他参数化方案,能有效减轻反演土壤湿度对观测土壤湿度的低估,Mironov模型减轻了垂直极化条件下反演土壤湿度对观测值的高估程度。在现有 \tau - \omega 模型参数化方案的基础上,总结了4种土壤介电模型在青藏高原典型草地下垫面的适用性,将为星载L波段辐射计青藏高原土壤湿度反演应用提供客观的土壤介电模型方案选取依据。     

Sentinel-1和Sentinel-2协同反演地表土壤水分

土壤水分是水文循环、生态环境、气候变化等研究中的关键参数,获取高分辨率长时间序列的土壤水分信息对农业管理、作物生长监测等具有重要的意义,同时也是研究的难点。基于时间序列(2019年至2020年)的Sentinel-1雷达数据和Sentinel-2光学数据,构建了地表土壤水分的雷达与光学数据协同反演模型,即裸土条件下地表土壤水分的变化检测方法,并利用归一化植被指数对植被影响进行校正,实现了青藏高原多年冻土区(五道梁)100 m空间分辨率的土壤水分反演。与地面实际观测的土壤水分进行对比验证,结果表明土壤水分反演结果与地面实测数据的相关系数介于0.672与0.941之间,无偏均方根误差介于0.031 m³/m³与0.073 m³/m³之间,土壤水分变化与区域降水事件和特征密切相关,验证了本文提出的考虑植被物候的变化检测方法在地势平坦、植被稀疏的青藏高原地区具有极高的适用性。     

基于Radarsat-2全极化数据的高原牧草覆盖地表土壤水分反演

大面积土壤水分反演对于青海湖流域草场的管理和保护具有重要的意义。利用C波段全极化的Radarsat-2 合成孔径雷达(SAR)影像数据,开展了青海湖流域刚察县附近草场的土壤水分反演研究,在“水-云”模型和Chen模型的基础上,发展了一种新的土壤水分反演算法。该算法消除了植被覆盖以及地表粗糙度对雷达后向散射系数的影响。实验结果表明:预测结果能够与实测数据很好地吻合,R²、RMSE和RPD分别达到0.71\,3.77%和1.64,反演精度较高,能够满足研究区土壤水分的反演精度要求。如果能够更细致地刻画植被层以及地表粗糙度对雷达后向散射系数的影响,土壤水分反演精度有望得到进一步提高。
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基于Sentinel-1及 Landsat 8数据的黑河中游农田土壤水分估算

土壤水分是陆地表层系统中的关键变量。利用主动微波遥感,特别是合成孔径雷达(Synthetic Aperture Radar,SAR)的观测,在监测和估计表层土壤水分时空分布方面已开展了诸多研究。然而,SAR土壤水分反演仍存在诸多挑战,特别是地表粗糙度和植被的影响。因此,本文提出了一种结合主动微波和光学遥感的优化估计方案,旨在同步反演植被含水量、地表粗糙度和土壤水分。反演算法首先在水云模型的框架下对模型中的植被透过率因子(与植被含水量密切相关)采用3种不同的光学遥感指数——修正的土壤调节植被指数(Modified Soil Adjusted Vegetation Index,MSAVI)、归一化植被指数(Normalized Difference Vegetation Index,NDVI)和归一化水体指数(Normalized Difference Water Index,NDWI)进行参数化估计,用于校正植被层的散射贡献。在此基础上,构造基于SAR观测和Oh模型的代价函数,利用复型洗牌全局优化算法进行土壤水分和地表粗糙度的联合反演。采用Sentinel-1 SAR和Landsat 8多光谱数据在黑河中游开展了反演试验,并利用相应的地面观测数据对结果进行了验证。结果表明反演结果与地面观测具有良好的一致性,其中基于NDWI的植被含水量反演效果最佳,与地面观测比较,土壤水分决定系数(R 2)在0.7以上,均方根误差(RMSE)为0.073 m^ 3/m^ 3;植被含水量R 2大于0.9,RMSE为0.885 kg/m 2,表明该方法能够较准确地估计土壤水分。同时发现植被含水量的估计结果,以及植被透过率的参数化方案对土壤水分的反演精度有一定的影响,在未来的研究中需要进一步探索。     

基于Sentinel-1与FY-3C数据反演植被覆盖地表土壤水分

基于新一代的Sentinel-1SAR数据与FY-3C的MWRI数据,研究植被覆盖地表土壤湿度反演方法。为消除植被对土壤湿度反演影响,首先利用FY-3C/MWRI的微波极化差异指数MPDI,建立植被含水量反演模型;然后,结合植被含水量反演模型和水—云模型,发展一种主被动微波联合反演植被覆盖地表土壤含水量模型;最后,在江淮地区开展反演试验,利用观测的土壤湿度数据进行反演结果的精度验证。结果表明:(1)对于植被覆盖地表土壤湿度反演,由FY3C/MWRI提取的MPDI对于去除植被影响效果较好;(2)相比于VH极化哨兵1号卫星数据,VV极化数据更适用于土壤含水量的反演,能够得到更高的土壤湿度反演精度;(3)哨兵1号卫星数据能够获得较高精度的土壤含水量反演结果,试验反演的土壤湿度值与实测值相关系数为0.561 2,均方根误差为0.044cm~3/cm~3。     

卫星微波遥感结合可见光遥感估算黄河源区土壤湿度研究

利用欧洲环境卫星(ENVISAT)搭载的高级合成孔径雷达ASAR(Advanced Synthesis Aperture Radar)交叉极化模式(APP)2009年8月9日和10月6日的数据对青藏高原东北部玛曲地区土壤湿度进行了估算。对于裸土区域采用表层微波后向散射几何光学模型GOM(Geometry Optics Model),对于植被覆盖度大的区域利用“水-云”模型处理植被层对后向散射系数的影响,取得了较好的结果:遥感估算的土壤湿度值和地面实测值之间的均方根误差RMSE<0.05,决定系数R²>0.82,表明该方法适合反演玛曲地区的土壤水分。从遥感估算的总体结果可以看出:山谷和陡峭山坡的反演结果相对较差,而在相对平坦的地区反演结果较好,估算的土壤湿度值在0.20~0.50 m³/m³之间。     

Radarsat-2/SAR和MODIS数据联合反演黄土高原地区植被覆盖下土壤水分研究

以黄土高原半干旱区定西为试验区,利用Radarsat-2/SAR和MODIS数据,将由MODIS NDVI估算的植被含水量(VWC)应用到微波散射Water-Cloud模型中校正植被的影响。采用交叉极化(VV/VH)组合方案对植被覆盖下土壤水分的反演进行初步探讨,结果表明:在植被影响校正前,模型反演土壤水分值出现明显低估现象;校正植被影响后,相关系数R由0.13提高到0.44,且通过α=0.01的显著性检验,标准差SD由5.02降低到4.30,有效提高了模型反演土壤水分的准确度。卫星反演的研究区土壤含水量大部分介于10%~30%之间,与实地考察情况一致,较好地反映出区域土壤湿度分布信息。表明,光学和微波协同遥感反演对于提高农田土壤水分遥感反演精度具有较大的应用潜力。     

Evaluation of Soil Moisture Retrieval Algorithms based on Ground-based Microwave Radiation Observation

The commonly used passive microwave soil moisture inversion algorithms include Single Channel Algorithm at H polarization (SCA-H), Single Channel Algorithm at V polarization (SCA-V), Dual-Channel Algorithm (DCA), Microwave Polarization Ratio Algorithm (MPRA) and Extended Dual Channel Algorithm (E-DCA). The five retrieval algorithms have different performance, systematic evaluation and analysis of these inversion algorithms will contribute to the improvement of the retrieval algorithm and the release of satellite soil moisture products. Verification of satellite product could bring some problems, such as scale matching and spatial heterogeneity. In order to avoid these issues, the above five soil moisture inversion algorithms are implemented, compared and analyzed based on ground-based microwave radiometer observation and supporting soil and vegetation parameter measurement data. The results show: (1) SCA has the best inversion performance. SCA-H has the highest correlation (R=0.83), and SCA-V has the smallest inversion error (RMSE=0.028 m³/m³, BIAS=-0.011 m³/m³), but SCA needs the accurate vegetation water content as an input. (2) The other three algorithms can get rid of the use of vegetation-aided data, with slightly poor performance but also meet the satellite detection requirements (less than or equal to 0.04 m³/m³). Among them, E-DCA and MPRA are slightly worse than the DCA. However, E-DCA is more advantageous in the vegetation water content inversion in our study.     

Retrieval of Soil Unfrozen Water in Maqu Region of Tibetan Plateau based on SMAP Brightness Temperature Measurement

Unfrozen water and ice co-exist in frozen soil, and their mutual transformation, namely freezing-thawing change, profoundly affects the surface water circulation and energy budget in cold regions. Passive microwave remote sensing technology is the main means of soil water monitoring, but it is mostly applied to the retrieval of water in non-frozen soil, and the retrieval of unfrozen water in frozen soil under negative temperature environment is less. Based on the brightness temperature measurement data obtained from the SMAP satellite ascending and descending overpass and the improved zero-order microwave radiation model applicable to the Tibetan Plateau, using Single-Channel Algorithm (SCA) and Dual-Channel Algorithm (DCA), The content of unfrozen water in the seasonal frozen soil in Maqu region which is the source region of the Yellow River in the east of Tibetan Plateau was inverted. The results show that the in-situ measured values dynamics are better captured by the retrieval values based on the brightness temperature measurement at the different moments of SMAP satellite overpass and different algorithms of soil unfrozen water in the study area(the correlation coefficient R is greater than 0.9). Among them, the retrieval results based on the brightness temperature measurement at the SMAP descending are significantly underestimated in the transition season of freezing-thawing cycle, while the retrieval results based on the brightness temperature measurement at the SMAP ascending are more accurate. The unbiased root-mean-square error (ubRMSE) of the retrieval values which obtained based on the V-polarization Single Channel Algorithm (SCA-V) and DCA and the in-situ values is 0.035 m³m^-3 and 0.039 m³m^-3, respectively, which are both meet the established requirements of SMAP mission. Compared with SMAP standard products, the soil moisture in warm season obtained by retrieval based on SCA-V algorithm is more accurate in this study. In addition, the algorithm adopted in this study can successfully retrieval the dynamic change of soil unfrozen water during freezing period, so it is more suitable for the retrieval of soil moisture under freezing and thawing conditions in Tibetan Plateau.     

Soil Moisture Retrieval over Vegetated Areas based on Sentinel-1 and FY-3C Data

This study aims to develop soil moisture retrieval model over vegetated areas based on Sentinel-1 SAR and FY-3C data.In order to remove vegetation effect,the MWRI data from FY-3C was applied to establish the inversion model of vegetation water content.The model was combined with the original water-cloud model,and developing a soil moisture retrieval model by combining active and passive microwave remote sensing data.Finally,the experiment of the soil moisture retrieval was conducted in Jiangsu and Anhui province,and validating the inversion accuracy of soil moisture by measured data.The results showed that：①For the vegetation-covered surface,the Microwave Polarization Difference Index obtain from FY-3C/MWRI was suitable for removing vegetation effect.②Compared with the Sentinel-1 VH polarization data,the backscattering coefficient of VV polarization was more suitable for soil moisture retrieval and get a higher accuracy of soil moisture retrieval.③Sentinel\|1 data can obtain high precision soil moisture estimation results,and the correlation coefficient between the estimated and measured soil moisture is 0.561 2 and RMSE is 0.044 cm³/cm³.     

空间遥感现状及未来发展

从第三十五届国际宇航联合会的空同遥感专业小组会议上可以看出,目前空间遥感的现状及未来发展前景。今后空间遥感将从具有单一遥感能力向具有综合遥感能力方面发展,不仅能对陆地,而且对海     

Sentinel-1 based Soil Moisture Estimation in Middle Reaches of Heihe River Basin

In this study, a change detection model, constructed using the Sentinel-1 Synthetic Aperture Radar (SAR) data and the simultaneous Normalized Difference Vegetation Index (NDVI) products from the Moderate Resolution Imaging Spectroradiometer (MODIS) and Landsat 8 sensors, is applied to estimate soil moisture in middle reaches of the Heihe River Basin, and the effects of two key parameters on retrieval accuracy are comprehensively investigated. The results show that: (1) when constructing the empirical relationship between backscattering coefficient difference ( \mathrm{\Delta } \sigma ) and Vegetation Index (VI) required by change detection model, the optimal sampling ratios in the ( \mathrm{\Delta } \sigma - V I ) space are approximately 2% and 4% for MODIS NDVI and Landsat 8 NDVI, respectively; (2) the Landsat 8 NDVI-based change detection model slightly outperforms the MODIS NDVI-based model in soil moisture retrieval accuracy, with Root Mean Square Error(RMSE) of 0.040 m³/m³ and 0.044 m³/m³respectively; (3) for the key parameters of the change detection method, replacing the ground-based initial soil moisture and scaling factor (maximum soil moisture difference between two adjacent dates \mathrm{\Delta } M s_{max} ) by the low-resolution SMAP/Sentinel-1 L2_SM_SP data will increase the RMSE by 0.01 m³/m³ and 0.04 m³/m³ respectively. Comparing to the parameter of initial soil moisture, the error in soil moisture scaling factor will lead to more significant degradation in the performance of the change detection method, thus it is recommended to use the high precision scaling factor for soil moisture estimation. This study confirms the promising potential of Sentinel-1 data for retrieving high-resolution soil moisture via change detection method and provides practical insight into its application.     

基于Sentinel-1数据的黑河中游土壤水分反演

基于Sentinel-1合成孔径雷达 (SAR) 数据及相同时段的中分辨率成像光谱仪（MODIS）和Landsat 8两种归一化植被指数（NDVI）,构建变化检测模型以估算黑河中游的高分辨率土壤水分,并探讨模型中具体参数设置对估算精度的影响。结果表明：①在对后向散射系数时间序列的差值 ( \mathrm{\Delta } \sigma ) 和植被指数 ( \mathrm{V} \mathrm{I} ) 进行线性建模过程中,MODIS NDVI和Landsat 8 NDVI这两种植被产品所构建的模型在 \mathrm{\Delta } \sigma - \mathrm{V} \mathrm{I} 空间中所选取的采样点比例分别为2%和4%时,各自取得最优精度; ②以土壤水分反演为目标,使用Landsat 8 NDVI构建的变化检测模型略优于使用MODIS NDVI构建的变化检测模型,两种模型的均方根误差RMSE分别为0.040 m³/m³和0.044 m³/m³,相关系数R分别为0.86和0.83; ③对于变化检测方法的关键参数,若使用低分辨率的SMAP/Sentinel-1 L2_SM_SP土壤水分数据分别代替站点观测的土壤水分初始值和缩放因子 (即两个连续时相土壤水分变化的最大值 \mathrm{\Delta } M s_{max} ) 这两个参数,则土壤水分RMSE将分别增加0.01 m³/m³和0.04 m³/m³。即土壤水分缩放因子这一参数的误差对反演结果的影响大于土壤水分初始值误差对反演结果的影响,故采用高精度的缩放因子进行变化检测估算。研究结论对于利用新兴的Sentinel-1 SAR数据,通过变化检测算法准确获取高分辨率土壤水分信息具有实际参考价值。     

Retrieval of soil moisture from passive and active L/S band sensor (PALS) observations during the Soil Moisture Experiment in 2002 (SMEX02)

The Soil Moisture Experiments in 2002 (SMEX02) were conducted in Iowa between June 25th and July 12th, 2002. A major aim of the experiments was examination of existing algorithms for soil moisture retrieval from active and passive microwave remote sensors under high vegetation water content conditions. The data obtained from the passive and active L and S band sensor (PALS) along with physical variables measured by in situ sampling have been used in this study to demonstrate the sensitivity of the instrument to soil moisture and perform soil moisture retrieval using statistical regression and physical modeling techniques. The land cover conditions in the region studied were predominantly soybean and corn crops with average vegetation water contents ranging from 0 to ∼5 kg/m². The PALS microwave sensitivity to soil moisture under these vegetation conditions was investigated for both passive and active measurements. The performance of the PALS instrument and retrieval algorithms has been analyzed, indicating soil moisture retrieval errors of approximately 0.04 g/g gravimetric soil moisture. Statistical regression techniques have been shown to perform satisfactorily with soil moisture retrieval error of around 0.05 g/g gravimetric soil moisture. The retrieval errors were higher for the corn than for the soybean fields due to the higher vegetation water content of the corn crops. However, the algorithms performed satisfactorily over the full range of vegetation conditions.