Satellite surface soil moisture from SMOS and Aquarius: Assessment for applications in agricultural landscapes

Satellite surface soil moisture has become more widely available in the past five years, with several missions designed specifically for soil moisture measurement now available, including the Soil Moisture and Ocean Salinity (SMOS) mission and the Soil Moisture Active/Passive (SMAP) mission. With a wealth of data now available, the challenge is to understand the skill and limitations of the data so they can be used routinely to support monitoring applications and to better understand environmental change. This paper examined two satellite surface soil moisture data sets from the SMOS and Aquarius missions against in situ networks in largely agricultural regions of Canada. The data from both sensors was compared to ground measurements on both an absolute and relative basis. Overall, the root mean squared errors for SMOS were less than 0.10 m³ m⁻³ at most sites, and less where the in situ soil moisture was measured at multiple sites within the radiometer footprint (sites in Saskatchewan, Manitoba and Ontario). At many sites, SMOS overestimates soil moisture shortly after rainfall events compared to the in situ data; however this was not consistent for each site and each time period. SMOS was found to underestimate drying events compared to the in situ data, however this observation was not consistent from site to site. The Aquarius soil moisture data showed higher root mean squared errors in areas where there were more frequent wetting and drying cycles. Overall, both data sets, and SMOS in particular, showed a stable and consistent pattern of capturing surface soil moisture over time.     

DisPATCh as a tool to evaluate coarse-scale remotely sensed soil moisture using localized in situ measurements: Application to SMOS and AMSR-E data in Southeastern Australia

Validating coarse-scale satellite soil moisture data still represents a big challenge, notably due to the large mismatch existing between the spatial resolution (> 10 km) of microwave radiometers and the representativeness scale (several m) of localized in situ measurements. This study aims to examine the potential of DisPATCh (Disaggregation based on Physical and Theoretical scale Change) for validating SMOS (Soil Moisture and Ocean Salinity) and AMSR-E (Advanced Microwave Scanning Radiometer-Earth observation system) level-3 soil moisture products. The ∽40–50 km resolution SMOS and AMSR-E data are disaggregated at 1 km resolution over the Murrumbidgee catchment in Southeastern Australia during a one year period in 2010–2011, and the satellite products are compared with the in situ measurements of 38 stations distributed within the study area. It is found that disaggregation improves the mean difference, correlation coefficient and slope of the linear regression between satellite and in situ data in 77%, 92% and 94% of cases, respectively. Nevertheless, the downscaling efficiency is lower in winter than during the hotter months when DisPATCh performance is optimal. Consistently, better results are obtained in the semi-arid than in a temperate zone of the catchment. In the semi-arid Yanco region, disaggregation in summer increases the correlation coefficient from 0.63 to 0.78 and from 0.42 to 0.71 for SMOS and AMSR-E in morning overpasses and from 0.37 to 0.63 and from 0.47 to 0.73 for SMOS and AMSR-E in afternoon overpasses, respectively. DisPATCh has strong potential in low vegetated semi-arid areas where it can be used as a tool to evaluate coarse-scale remotely sensed soil moisture by explicitly representing the sub-pixel variability.     

Investigating flash floods potential areas using ASCAT and TRMM satellites in the Western Cape Province,South Africa

The aim of the study was to evaluate flash flood potential areas in the Western Cape Province of South Africa, by integrating remote sensing products of high rainfall intensity, antecedent soil moisture and topographic wetness index (TWI). Rainfall has high spatial and temporal variability, thus needs to be quantified at an area in real time from remote sensing techniques unlike from sparsely distributed, point gauge network measurements. Western Cape Province has high spatial variation in topography which results in major differences in received rainfall within areas not far from each other. Although high rainfall was considered as the major cause of flash flood, also other contributing factors such as topography and antecedent soil moisture were considered. Areas of high flash flood potential were found to be associated with high rainfall, antecedent precipitation and TWI. Although TRMM 3B42 was found to have better rainfall intensity accuracy, the product is not available in near real time but rather at a rolling archive of three months; therefore, Multi- sensor precipitation estimate rainfall estimates available in near real time are opted for flash flood events. Advanced Scatterometer (ASCAT) soil moisture observations were found to have a reasonable r value of 0.58 and relatively low MAE of 3.8 when validated with in situ soil moisture measurements. The results of this study underscore the importance of ASCAT and TRMM satellite datasets in mapping areas at risk of flooding.     

Soil moisture variability over Odra watershed: Comparison between SMOS and GLDAS data

Monitoring of temporal and spatial soil moisture variability is an important issue, both from practical and scientific point of view. It is well known that passive, L-band, radiometric measurements provide best soil moisture estimates. Unfortunately as it was observed during Soil Moisture and Ocean Salinity (SMOS) mission, which was specially dedicated to measure soil moisture, these measurements suffer significant data loss. It is caused mainly by radio frequency interference (RFI) which strongly contaminates Central Europe and even in particularly unfavorable conditions, might prevent these data from being used for regional or watershed scale analysis. Nevertheless, it is highly awaited by researchers to receive statistically significant information on soil moisture over the area of a big watershed. One of such watersheds, the Odra (Oder) river watershed, lies in three European countries – Poland, Germany and the Czech Republic. The area of the Odra river watershed is equal to 118,861 km² making it the second most important river in Poland as well as one of the most significant one in Central Europe.This paper examines the SMOS soil moisture data in the Odra river watershed in the period from 2010 to 2012. This attempt was made to check the possibility of assessing, from the low spatial resolution observations of SMOS, useful information that could be exploited for practical aims in watershed scale, for example, in water storage models even while moderate RFI takes place. Such studies, performed over the area of a large watershed, were recommended by researchers in order to obtain statistically significant results. To meet these expectations, Centre Aval de Traitement des Donnes SMOS (CATDS), 3-days averaged data, together with Global Land Data Assimilation System (GLDAS) National Centers for Environmental Prediction/Oregon State University/Air Force/Hydrologic Research Lab (NOAH) model 0.25 soil moisture values were used for statistical analyses and mutual comparisons.The results obtained using various statistical tools unveil high scientific potential of CATDS SMOS data to study soil moisture over the Odra river watershed. This was also confirmed by reasonable agreement between results derived from CATDS SMOS Ascending and GLDAS data sets. This agreement was achieved mainly by using these data spatially averaged over the whole watershed area, and for observations performed in the period longer than three-day averaging time. Comparisons of separate three-day data in a given pixel position, or at smaller areas would be difficult because of data gaps. Hence, the results of the work suggest that despite of RFI interferences, SMOS observations can provide effective input for analysis of soil moisture at regional scales. Moreover, it was shown that CATDS SMOS soil moisture data are better correlated with rainfall rate than GLDAS ones.     

闪电河流域农牧交错带微波遥感土壤水分产品评价

空间网格分辨率为9 km的SMAP (Soil Moisture Active and Passive)、0.1D (Degree)的ASCAT (The Advanced Scatterometer)、 25 km的FY-3B以及25 km ESA-CCI (European Space Agency-Climate Change Initiative)是较为广泛应用的卫星遥感土壤水分产品,对数据质量的评价是进一步应用于旱情监测、蒸散发估算等研究的前提。本研究基于2018年9月在闪电河流域内蒙古农牧交错带区域开展的碳、水循环与能量平衡遥感综合试验,采用近似同步的两种尺度观测数据即点尺度地面实测土壤水分数据以及面尺度(1 km×1 km)机载土壤水分数据,利用RMSE (Root Mean Square Error), MAE (Mean Absolute Error),R (Correlation Coefficient),Bias以及ubRMSE (unbiased Root Mean Square Error)等评价指标分别对SMAP, ASCAT, FY-3B, ESA-CCI土壤水分卫星遥感产品进行了评价。本研究利用机载土壤水分数据作为桥梁,实现了从点尺度地面实测土壤水分数据、至面尺度(1 km×1 km)机载土壤水分数据、再至粗格网面尺度(9 km×9 km、0.1 D×0.1 D、25 km×25 km)卫星遥感土壤水分产品的对比分析过程。利用地面观测值对机载观测土壤水分开展评价分析,发现在裸土区域,机载土壤水分数据与地面实测数据较为一致,RMSE, MAE, Bias, ubRMSE以及R值分别为0.033 cm~3/cm~3,0.030 cm~3/cm~3,-0.004 cm~3/cm~3, 0.033 cm~3/cm~3, 0.474。对卫星土壤水分产品的评价结果显示,SMAP的9 km土壤水分卫星产品与地面观测更为一致,其RMSE,MAE,Bias,ub RMSE以及R值分别为0.037 cm~3/cm~3,0.032 cm~3/cm~3,-0.008 cm~3/cm~3, 0.036 cm~3/cm~3, 0.507。SMAP, ASCAT, FY-3B以及ESA-CCI与机载土壤水分数据有更高的相关性,R值分别为0.735, 0.558, 0.558, 0.575。综上,闪电河流域实验区内的4种卫星遥感土壤水分产品中,SMAP产品与地面土壤水分、机载土壤水分数据均较为一致,其次是FY-3B与ESA-CCI。     

Assessing the SMOS Soil Moisture Retrieval Parameters With High-Resolution NAFE'06 Data

The spatial and temporal invariance of Soil Moisture and Ocean Salinity (SMOS) forward model parameters for soil moisture retrieval was assessed at 1-km resolution on a diurnal basis with data from the National Airborne Field Experiment 2006. The approach used was to apply the SMOS default parameters uniformly over 27 1-km validation pixels, retrieve soil moisture from the airborne observations, and then to interpret the differences between airborne and ground estimates in terms of land use, parameter variability, and sensing depth. For pastures (17 pixels) and nonirrigated crops (5 pixels), the root mean square error (rmse) was 0.03 volumetric (vol./vol.) soil moisture with a bias of 0.004 vol./vol. For pixels dominated by irrigated crops (5 pixels), the rmse was 0.10 vol./vol., and the bias was $-$0.09 vol./vol. The correlation coefficient between bias in irrigated areas and the 1-km field soil moisture variability was found to be 0.73, which suggests either 1) an increase of the soil dielectric roughness (up to about one) associated with small-scale heterogeneity of soil moisture or/and 2) a difference in sensing depth between an L-band radiometer and the in situ measurements, combined with a strong vertical gradient of soil moisture in the top 6 cm of the soil.      

一种遥感土壤湿度误差空间协方差的估算方法

针对遥感反演土壤湿度空间相关的误差协方差难以估计的问题,提出了一种遥感反演数据误差空间协方差估算方法——3类数据集成分析误差协方差（triple collocation covariance,TC_Cov）,将土壤湿度场的每个单元（像元）看作一个空间随机变量,用两个随机变量表示的土壤湿度值的时间序列作为样本进行空间协方差估计,由任何两个随机变量的协方差形成多个随机变量（随机场）的协方差矩阵。利用先进散射计（ad-vanced scatterometer,ASCAT）和热带降雨测量卫星（tropical rainfall measuring mission,TRMM）的遥感土壤湿度数据以及ERA-Interim土壤湿度再分析数据作为TC_Cov方法的输入数据,分别估算了ERA-Interim、AS-CAT和TRMM在澳大利亚Murrumbidgee流域的土壤湿度误差协方差矩阵,验证了估算方法的合理性和可行性。     

An algorithm for soil moisture estimation using GPS-interferometric reflectometry for bare and vegetated soil

Ground-reflected global positioning system signals measured by a geodetic-quality GPS system can be used to infer temporal changes in near-surface soil moisture for the area surrounding the antenna. This technique, known as GPS-interferometric reflectometry, analyzes changes in the interference pattern of the direct and reflected signals, which are recorded in signal-to-noise ratio (SNR) data, as interferograms. Temporal fluctuations in the phase of the interferogram are indicative of changes in near-surface volumetric soil moisture content. However, SNR phase is also highly sensitive to changes in overlying vegetation, and thus, the effects of seasonal vegetation changes on the ground-reflected signal must be considered. Here a method is described for determining whether SNR data are significantly corrupted by vegetation and for correcting these effects. Absolute soil moisture content must be determined for each site using ancillary data for the residual moisture content. Accounting for vegetation effects significantly improves the agreement between GPS-derived soil moisture and in situ measurements.     

山区地形对被动微波遥感影响的研究进展

随着土壤湿度与海水盐度卫星( SMOS)发射计划的顺利开展和AMSR -E(Advanced Microwave Scanning Radiometer- Earth Observing System)业务化运行服务之后,人类用星载微波辐射计监测土壤水分是空间技术上的又一次飞跃,但土壤水分的反演精度受到微波辐射计低空间...     

Simulating L-band emission of coniferous forests using a discrete model and a detailed geometrical representation

A discrete model, based on the radiative transfer theory, is used to simulate coniferous forest emissivity at L-band. Inputs to the model are given by using a detailed geometrical representation of Les Landes forest. Simulated emissivities are compared against EuroSTARRS campaign measurements, which were made over the same forest at nominally vertical polarization and several angles. The model has also been used to investigate the sensitivity of L-band radiometers to soil moisture under forests. Results of this investigation indicate that the soil contribution to emission is potentially appreciable, even under developed forests. This may be a useful result, in view of future satellite missions, such as SMOS and HYDROS.     

Considering combined or separated roughness and vegetation effects in soil moisture retrievals

For more than six years, the Soil Moisture and Ocean Salinity (SMOS) mission has provided multi angular and full-polarization brightness temperature (TB) measurements at L-band. Geophysical products such as soil moisture (SM) and vegetation optical depth at nadir (τ_nad) are retrieved by an operational algorithm using TB observations at different angles of incidence and polarizations. However, the quality of the retrievals depends on several surface effects, such as vegetation, soil roughness and texture, etc. In the microwave forward emission model used in the retrievals (L-band Microwave Emission Model, L-MEB), soil roughness is modelled with a semi-empirical equation using four main parameters (Q_r, H_r, N_rp, with p = H or V polarizations). At present, these parameters are calibrated with data provided by airborne studies and in situ measurements made at a local scale that is not necessarily representative of the large SMOS footprints (43 km on average) at global scale. In this study, we evaluate the impact of the calibrated values of N_rp and H_r on the SM and τ_nad retrievals based on SMOS TB measurements (SMOS Level 3 product) over the Soil Climate Analysis Network (SCAN) network located in North America over five years (2011–2015). In this study, Q_r was set equal to zero and we assumed that N_rH = N_rV. The retrievals were performed by varying N_rp from −1 to 2 by steps of 1 and H_r from 0 to 0.6 by steps of 0.1. At satellite scale, the results show that combining vegetation and roughness effects in a single parameter provides the best results in terms of soil moisture retrievals, as evaluated against the in situ SM data. Even though our retrieval approach was very simplified, as we did not account for pixel heterogeneity, the accuracy we obtained in the SM retrievals was almost systematically better than those of the Level 3 product. Improved results were also obtained in terms of optical depth retrievals. These new results may have key consequences in terms of calibration of roughness effects within the algorithms of the SMOS (ESA) and the SMAP (NASA) space missions.     

Impact of sub-pixel heterogeneity on modelled brightness temperature for an agricultural region

Knowledge of sub-pixel heterogeneity, particularly at the passive microwave scale, can improve the brightness temperature (and ultimately the soil moisture) estimation. However, the impact of surface heterogeneity (in terms of soil moisture, soil temperature and vegetation water content) on brightness temperature in an agricultural setting is relatively unknown. The Soil Moisture Active Passive Validation Experiment 2012 (SMAPVEX12) provided an opportunity to evaluate sub-pixel heterogeneity at the scale of a Soil Moisture Ocean Salinity (SMOS) or the Soil Moisture Active Passive (SMAP) radiometer footprint using field measured data. The first objective of this study was to determine if accounting for surface heterogeneity reduced the error between estimated brightness temperature (T_b) and T_b measured by SMOS. It was found that when accounting for variation in surface soil moisture, temperature and vegetation water content within the pixel footprint, the error between the modelled T_b and the measured T_b was less than if a homogeneous pixel were modelled. The correlation between the surface parameters and the error associated with not accounting for surface heterogeneity were investigated. It was found that there was low to moderate correlation between the error and the coefficient of variance associated with the measured soil moisture, soil temperature and vegetation volumetric water content during the field campaign. However, it was found that the correlations changed depending on the stage of vegetation growth and the amount of time following a precipitation event. At the start of the field campaign (following a precipitation event), there was strong correlation between the error and all three surface parameters (r ≥ 0.75). Following a precipitation event close to the middle of the field campaign (during which there was rapid growth in vegetation), there was strong correlation between the error and the variability in vegetation water content (r = 0.89), moderate correlation with soil moisture (r = 0.61) and low correlation with soil temperature (r = 0.26).     

Rainfall-runoff modelling by using SM2RAIN-derived and state-of-the-art satellite rainfall products over Italy

Satellite rainfall products (SRPs) are becoming more accurate with ever increasing spatial and temporal resolution. This evolution can be beneficial for hydrological applications, providing new sources of information and allowing to drive models in ungauged areas. Despite the large availability of rainfall satellite data, their use in rainfall-runoff modelling is still very scarce, most likely due to measurement issues (bias, accuracy) and the hydrological community acceptability of satellite products.In this study, the real-time version (3B42-RT) of Tropical Rainfall Measurement Mission Multi-satellite Precipitation Analysis, TMPA, and a new SRP based on the application of SM2RAIN algorithm (Brocca et al., 2014) to the ASCAT (Advanced SCATterometer) soil moisture product, SM2R_ASC, are used to drive a lumped hydrologic model over four basins in Italy during the 4-year period 2010–2013.The need of the recalibration of model parameter values for each SRP is highlighted, being an important precondition for their suitable use in flood modelling. Results shows that SRPs provided, in most of the cases, performance scores only slightly lower than those obtained by using observed data with a reduction of Nash–Sutcliffe efficiency (NS) less than 30% when using SM2R_ASC product while TMPA is characterized by a significant deterioration during the validation period 2012–2013. Moreover, the integration between observed and satellite rainfall data is investigated as well. Interestingly, the simple integration procedure here applied allows obtaining more accurate rainfall input datasets with respect to the use of ground observations only, for 3 out 4 basins. Indeed, discharge simulations improve when ground rainfall observations and SM2R_ASC product are integrated, with an increase of NS between 2 and 42% for the 3 basins in Central and Northern Italy. Overall, the study highlights the feasibility of using SRPs in hydrological applications over the Mediterranean region with benefits in discharge simulations also in well gauged areas.     

An inter-comparison of soil moisture data products from satellite remote sensing and a land surface model

Significant advances have been achieved in generating soil moisture (SM) products from satellite remote sensing and/or land surface modeling with reasonably good accuracy in recent years. However, the discrepancies among the different SM data products can be considerably large, which hampers their usage in various applications. The bias of one SM product from another is well recognized in the literature. Bias estimation and spatial correction methods have been documented for assimilating satellite SM product into land surface and hydrologic models. Nevertheless, understanding the characteristics of each of these SM data products is required for many applications where the most accurate data products are desirable. This study inter-compares five SM data products from three different sources with each other, and evaluates them against in situ SM measurements over 14-year period from 2000 to 2013. Specifically, three microwave (MW) satellite based data sets provided by ESA's Climate Change Initiative (CCI) (CCI-merged, -active and -passive products), one thermal infrared (TIR) satellite based product (ALEXI), and the Noah land surface model (LSM) simulations. The in-situ SM measurements are collected from the North American Soil Moisture Database (NASMD), which involves more than 600 ground sites from a variety of networks. They are used to evaluate the accuracies of these five SM data products. In general, each of the five SM products is capable of capturing the dry/wet patterns over the study period. However, the absolute SM values among the five products vary significantly. SM simulations from Noah LSM are more stable relative to the satellite-based products. All TIR and MW satellite based products are relatively noisier than the Noah LSM simulations. Even though MW satellite based SM retrievals have been predominantly used in the past years, SM retrievals of the ALEXI model based on TIR satellite observations demonstrate skills equivalent to all the MW satellite retrievals and even slightly better over certain regions. Compared to the individual active and passive MW products, the merged CCI product exhibits higher anomaly correlation with both Noah LSM simulations and in-situ SM measurements.     

数据驱动的蒸散发遥感反演方法及产品研究进展

蒸散发是水圈、大气圈和生物圈中水分循环和能量交换的纽带。在全球尺度上,蒸散发约占陆地降水总量的60%;作为其能量表达形式,潜热通量约占地表净辐射的80%。随着通量观测技术的发展,全球长期持续的观测数据得以获取和共享,近年来基于数据驱动的蒸散发遥感反演方法取得了较好的研究进展。本文针对数据驱动的蒸散发遥感反演方法和产品,从经验回归、机器学习和数据融合3个方面展开,对现有的研究进展进行了梳理、归纳和总结,并从驱动数据、反演方法、已有产品等方面指出目前仍存在的问题和不足。未来仍需开展数据驱动的高时空分辨率的蒸散发遥感反演方法的研究,有效考虑地表温度和土壤水分等可以指示地表蒸散发短期变化的重要信息,同时加强基于过程驱动的物理模型与数据驱动的模型的结合,使两类模型能互为补充、各自发挥所长,共同推动蒸散发遥感反演研究水平的进步。     

多星融合的土壤湿度滚动式估算模型

全球定位系统干涉反射测量(GPS-IR)是一种新的遥感技术,可用于估算近地表土壤水分含量。考虑到多卫星融合的优势和土壤湿度的时空尺度性,提出一种基于多星融合的土壤湿度最小二乘支持向量机(LS-SVM)滚动式估算模型。首先通过低阶多项式拟合分离GPS卫星直射和反射信号,进而建立反射信号正弦拟合模型,获取相对延迟相位。最后,通过线性回归模型有效分析和选取多卫星相对延迟相位,并建立基于多星融合的最小二乘支持向量机模型进行滚动式估算土壤湿度。以美国板块边界观测计划PBO提供的监测数据为例,对比分析利用单颗、多颗GPS卫星进行土壤湿度滚动式估算的可行性和有效性。经理论分析和两个测站实验表明:该模型充分发挥了LS-SVM的优势,有效综合了各卫星的性能,改善了采用单颗卫星进行土壤湿度估算时,其结果极易出现异常跳变的现象;模型只需较少的建模数据,采用滚动式能实现较长时间的估算,估算误差较为稳定;模型所估算的结果与土壤湿度实测值之间的相关系数R2以及均方根误差分别为0.942和0.962、0.072和0.032,相对于部分单一卫星至少提高了18.18%。因此,土壤湿度问题可作为非线性事件处理,采用多卫星融合估算是可行和有效的。     

风能方向性参量遥感反演

目前可遥感反演的海上风能参量主要为平均风速和平均风功率密度,缺乏对风能方向性参量的反演。本文建立了以风向频率、风能密度方向分布为核心的风能方向性参量体系及相应的反演方法,使用2007年—2016年ASCAT星载散射计观测数据进行了反演实验,并利用海上现场观测数据对反演结果进行比较验证,通过理论分析和模拟实验对反演方法的数据量需求和误差传递进行了分析。结果表明,90%的反演结果通过了所有的同一性检验,验证了其有效性和准确性;风向频率和风能密度方向分布准确反演所需的最小数据量分别为350条和800条;遥感反演的风速风向数据的误差使得最终反演的风能方向性参量趋于离散,真实的风能方向分布越集中,对其影响越敏感。     

顾及多径环境差异和粗差的GNSS-MR土壤湿度反演

针对基于单系统单卫星GNSS-MR（GNSS Multipath Reflectometry）土壤湿度反演的可靠性不高、实际可操作性不强和最小二乘估计不具鲁棒性的缺点,为获取更优的延迟相位估值,并改善GNSS-MR土壤湿度反演的可靠性和实际可操作性,同时简化繁杂的选星过程,提出了一种基于抗差估计的多系统多卫星组合GNSS-MR土壤湿度反演算法。该算法首先顾及多径环境的差异性、多径误差的周期特性等进行信噪比SNR（Signal to Noise Ratio）观测值的筛选,然后采用基于IGGIII（Weight Function III Developed by Institute of Geodesy and Geophysics）权函数的抗差估计解求延迟相位,进而获得表征土壤湿度变化趋势的延迟相位组合。实验结果表明,相较于未采用抗差估计的多系统多卫星组合（方案1）和单卫星组合（方案3）,得益于抗差估计良好的鲁棒性,基于抗差估计的多系统多卫星组合（方案2）和单卫星组合（方案4）获得了较高的建模精度,所得延迟相位与实测土壤湿度间的相关系数分别为0.97和0.95、土壤湿度拟合残差的均方根误差分别为0.010和0.012;同时,方案2和方案4还取得了较高的土壤湿度预报精度,土壤湿度预测值与土壤湿度实测值间的相关系数分别为0.92和0.91、土壤湿度预报残差的均方根误差分别为0.016和0.023;此外,相比于方案4,方案2在采用抗差估计解求延迟相位的基础上,采用多系统多卫星组合进一步提升了延迟相位的估值精度,从而不仅避免了复杂的选星过程,而且还获得了更好的建模效果和更高的土壤湿度预报精度。     

Multi-Index Soil Moisture Estimation from Satellite Earth Observations: Comparative Evaluation of the Topographic Wetness Index (TWI), the Temperature Vegetation Dryness Index (TVDI) and the Improved TVDI (iTVDI)

Soil moisture estimation from satellite earth observation has emerged effectively advantageous due to the high temporal resolution, spatial resolution, coverage, and processing convenience it affords. In this paper, we present a study carried out to estimate soil moisture level at every location within Enugu State Nigeria from satellite earth observation. Comparative analysis of multiple indices for soil moisture estimation was carried out with a view to evaluating the robustness, correlation, appropriateness and accuracy of the indices in estimating the spatial distribution of soil moisture level in Enugu State. Results were correlated and validated with In-Situ soil moisture observations from multi-sample points. To achieve this, the Topographic Wetness Index (TWI), based on digital elevation data, the Temperature Vegetation Dryness Index (TVDI) and an improved TVDI (iTVDI) incorporating air temperature and a Digital Elevation Model (DEM) were calculated from ASTER global DEM and Landsat images. Possible dependencies of the indices on land cover type, topography, and precipitation were explored. In-Situ soil moisture data were used to validate the derived indices. The results showed that there was a positive significant relationship between iTVDI versus TVDI (R = 0.53, P value < 0.05), while in iTVDI versus TWI (R = 0.00, P value > 0.05) and TVDI versus TWI (R = ?0.01, P value > 0.05) no significant relationship existed. There was a strong relationship between iTVDI and topography, land cover type, and precipitation than other indices (TVDI, TWI). In situ measured soil moisture values showed negative significant relationship with TVDI (R = ?0.52, P value < 0.05) and iTVDI (R = ?0.63, P value < 0.05) but not with TWI (R = ?0.10, P value > 0.05). The iTVDI outperformed the other two index; having a stronger relationship with topography, precipitation, land cover classes and soil moisture. It concludes that although iTVDI outperformed other indices (TVDI, TWI) in soil moisture estimation, the decision of which index to apply is dependent on available data, the intent of usage and spatial scale.