基于MODIS数据的黑河流域土壤热惯量反演研究

热惯量法在土壤水分反演中有着广泛的应用。以MODIS数据为基础,选用真实热惯量模型,反演得到了黑河流域的土壤热惯量,为进一步研究流域土壤水分提供可靠的方法和数据。利用地面实测数据对模型参数及反演结果进行了验证,并分析了地表昼夜最大温差、地表反照率及土壤热惯量的季节性变化规律,同时对比了真实热惯量模型与表观热惯量模型反演结果与土壤水分的相关性。结果表明：地表温差、地表反照率及土壤热惯量都具有明显的季节性变化特征;真实热惯量模型相对于表观热惯量模型更有利于土壤水分的反演,且具有广泛的适用性。     

基于MODIS数据反演阜新地区土壤水分的研究

利用MODIS数据计算表观热惯量模型反演阜新地区春季的土壤含水状况分布。通过处理 2009、2010年4月的数据,拟合表观热惯量与土壤含水量的关系模型,得到阜新地区土壤含水状况分布图。通过与37个实测点土壤含水量数据对比验证表明,深度为0~10 cm时,相对误差均值为5.4%;深度为10~20 cm时,相对误差值为10.82%;深度为20~30 cm时,相对误差值为16.50%,相对误差都满足地区实际反演要求,并随土层深度增加相对误差均值增大。     

渍害遥感识别的基本原理与方法

本文论述了渍害遥感识别的主要标志，通过土壤热惯量、土壤含水量及土壤温度等渍害相关因子的讨论，阐明了渍害遥感识别的基本原理及几种主要方法。由于渍害涉及诸多因素，本文给出了以地学相关分析为基础的渍害判别模型。本研究表明，用遥感方法研究渍害是切实可行的。     

热惯量与土壤特性研究

热惯量是岩石和土壤保持自身热量的量度,可以反映地表的组成特点。1978年4月发射并用于观测地表热惯量的热惯量卫星,其上搭载的热惯量成像辐射计(HCMR),具有两个通道:     

半干旱区土壤热通量遥感估算模型的适应性对比分析

提出了一种基于地表温度的土壤热通量遥感估算模型,结合另外两种应用比较广泛的遥感估算模型,分别是Moran(1989)提出的基于归一化植被指数NDVI、净辐射通量的模型和Bastiaanssen(1998)的基于NDVI、地表反照率、地表温度、净辐射通量的模型,利用MODIS遥感数据对这3种土壤热通量的模型进行了试验分析。参照半干旱区退化草地和农田地面站点实测的土壤热通量数据,3种遥感模型的试验结果表明:提出的基于MO-DIS地表温度的模型得到的土壤热通量精度最高;Bastiaanssen(1998)模型也能得到精度相当的土壤热通量,特别是它得到的可利用能量精度最高;Moran(1989)模型反演的土壤热通量误差最大。     

基于MODIS数据的渭河流域土壤水分反演

利用MODIS产品数据MOD11A1、MOD13A2和MOD15A2获取地表温度(TS)、昼夜温差(TSD)、表观热惯量(ATI)、归一化植被指数(NDVI)、增强植被指数(EVI)、叶面积指数(LAI),构建渭河流域2006年8月1日、8月6日的TS-NDVI、TS-EVI、TS-LAI、TSD-NDVI、TSD-EVI、TSD-LAI、ATI-NDVI、ATI-EVI、ATI-LAI特征空间,根据TS-NDVI、TS-EVI、TS-LAI、TSD-NDVI、TSD-EVI、TSD-LAI、AI-NDVI、ATI-EVI、ATI-LAI特征空间建立了温度归一化植被指数型干旱指数(TNDI)、温度增强植被指数型干旱指数(TEDI)、温度叶面积指数型干旱指数(TLDI)、温差归一化植被指数型干旱指数(DTNDI)、温差增强植被指数型干旱指数(DTEDI)、温差叶面积指数型干旱指数(DTLDI)、表观热惯量归一化植被指数型干旱指数(ANDI)、表观热惯量增强植被指数型干旱指数(AEDI)、表观热惯量叶面积指数型干旱指数(ALDI),并以这些干旱指数作为土壤水分监测指标,反演了渭河流域2006年8月1日、8月6日的土壤水分.利用TDR实测10cm土壤水分进行相关分析表明: TEDI、TNDI、TLDI在高植被覆盖的地区、低植被覆盖的地区进行土壤水分反演和干旱监测都能取得较好的效果,其中TEDI效果最好;DTNDI、DTEDI、DTLDI、ANDI、AEDI、ALDI比较适合在低植被覆盖的地区进行土壤水分反演和干旱监测,但在高植被覆盖的地区效果较差,不适合进行土壤水分反演和干旱监测.     

土壤—植被混合地表红外辐射温度场的耦合建模方法*

目前混合地表温度场的数学模型忽略了地表内部各组分间的热通量交互,致使温度场模拟结果不够准确、真实.针对已有独立求解模型的不足,结合土壤-植被混合地表的材质组成及空间分布特点,建立温度场的耦合求解模型,通过在热平衡方程中引入组分间热通量的交互项,对土壤-植被-大气耦合的能量平衡过程进行描述.实验结果表明,利用耦合模型求解的混合地表的温度场分布和热图像特征与自然地表红外辐射特性的真实分布规律具有更好的一致性,从而验证了温度场耦合建模方法的有效性.     

蒸发条件下冬小麦田土壤水热耦合运移模拟

综合考虑了根区土壤水分动态、根系吸水、蒸发蒸腾、地表能量分配和土壤中温度分布5个子系统，建立了冬小麦蒸发条件下土壤水热耦合运移模拟模型，经实测资料检验其精度较高。该模型可用于田间水分动态、根系吸水、蒸发蒸腾、地表能量分配和土壤中温度分布的模拟或预测。     

甘肃黄土高原地区土壤水热特征分析研究

利用甘肃黄土高原代表站地温、土壤含水量及降水资料,运用统计模拟方法,分析了土壤温度的日变化及土中热交换特性,评述了水热耦合效应。地温的日变化特性用谐波分析方法描述,各季典型天气下24小时热通量值由热平衡台站规范方法计算。结果表明,各层次地温的日变化基本表现为一阶谐波,这种正弦的波形尤以晴天最为明显。不同季节典型天气下土中热通量的变化由正值转为负值的时间基本一致出现在16时,阴天提前1时,由负值转为正值的时间基本一致出现在7时,冬季阴天出现在9时。水热交互作用与土壤含水量的变化有显著相关关系。     

基于多源遥感数据融合的土壤水分反演研究

土壤含水量作为地表的重要参量之一,对地球能量循环、水循环、碳循环及生态环境都有十分重要的意义。以南京市金川河流域为研究区,融合哨兵 2 号 L2A 数据和 Landsat 8 遥感数据 2 种数据源,分别采用偏最小二乘法(PLSR)、最小二乘-支持向量机(LS-SVM)、反向传播神经网络(BPNN)和随机森林(RF)4 种建模方法,建立遥感数据与土壤含水量之间的关系,并进行模型的验证与评价。结果表明：1)土壤含水量与哨兵 2 号和 Landsat 8 各波段反射率均呈负相关关系,和海岸带监测波段(波长为 430~450 nm)和近红外波段(波长为 2 100~2 300 nm)相关性最佳;2)融合后的遥感数据相较于单一遥感数据源,预测土壤含水量的能力更佳, 最优模型 R2 达 0.996,均方根误差仅为 0.003 g/g;3)4 种建模方法中,建模效果从好到差依次为 PLSR,RF, LS-SVM,BPNN。融合哨兵 2 号 L2A 和 Landsat 8 数据,结合 PLSR 建模方法可进行土壤含水量的精准反演, 相较于现有研究反演精度大大提升,对研究该地区地表与地下水循环和生态环境治理有一定参考价值。     

A combined passive/active microwave remote sensing approach for surface variable retrieval using Tropical Rainfall Measuring Mission observations

The backscattering and emission measured simultaneously by radar and radiometer show promise for the estimation of surface variables such as near-surface soil moisture and vegetation characteristics. In this paper, the 10.7 GHz Tropical Rainfall Measuring Mission (TRMM) microwave imager (TMI) channel and 13.8 GHz precipitation radar (PR) observations are simultaneously used for the estimation of the near-surface soil moisture and vegetation properties. The Fresnel model for soil and a simple model for vegetation are used to simulate the passive microwave emission at 10.7 GHz. To determine the PR backscatter signal from a land surface, a theoretical approach is used based on the Geometric Optics Model for simulating bare soil and a semi-empirical water-cloud model for vegetation. The model parameters required in specifying the nature of the soil and vegetation are calibrated on the basis of in situ soil moisture data combined with remotely sensed observations. The calibrated model is subsequently used to retrieve near-surface soil moisture and leaf area index for assumed values of surface roughness and temperature. Algorithm assessment using synthetic passive and active microwave data shows a nonlinearity effect in the system inversion, which results in a varying degree of error statistics in soil wetness and vegetation characteristics retrieval. The technique was applied on TRMM radar/radiometer observations from three consecutive years and evaluated against in situ near-surface (5 cm) soil moisture measurements from the Oklahoma Mesonet showing a consistent performance.     

Estimating soil thermal properties from sequences of land surface temperature using hybrid Genetic Algorithm–Finite Difference method

Most models used in land surface hydrology, vadose zone hydrology, and hydro-climatology require an accurate representation of soil thermal properties (soil thermal conductivity and volumetric heat capacity). Various empirical relations have been suggested to estimate soil thermal properties. However, they require many input parameters such as soil texture, mineralogical composition, porosity and water content, which are not always available from laboratory experiments and field measurements. In this paper, to overcome the above challenge, a hybrid numerical method, Genetic Algorithm–Finite Difference (GA–FD), is proposed to estimate soil thermal properties using land surface temperature (LST) as the only input. The genetic algorithm (GA) optimization method coupled with the finite difference (FD) modeling technique is a viable hybrid approach for estimating soil thermal properties. The finite difference method is employed to solve the heat diffusion equation and simulate LST, while a robust optimization technique (GA) is used to retrieve soil thermal properties by minimizing the difference between observed and simulated LST. Furthermore, a generalization of the hybrid model is developed for inhomogeneous soil, in which soil thermal properties are not constant throughout the soil slab. The proposed model is applied to the First International Satellite Land Surface Climatology Project (ISLSCP) Field Experiment (FIFE). The results show that the proposed hybrid numerical method is able to estimate soil thermal properties accurately, and therefore effectively eliminate the need for the unavailable soil parameters which are required by empirical methods for determining the soil thermal conductivity and volumetric heat capacity. Remarkably, the temporal variation of the retrieved soil thermal conductivity is consistent with the volumetric water content, even though no water content information is used in the model.     

Evaluating near-surface soil moisture using heat capacity mapping mission data

Four dates of Heat Capacity Mapping Mission (HCMM) data were analyzed in order to evaluate HCMM thermal data use in estimating near-surface soil moisture in a complex agricultural landscape. Because of large spatial and temporal ground cover variations, HCMM radiometric temperatures alone did not correlate with soil water content. The radiometric temperatures consisted of radiance contributions from different canopies and their respective soil backgrounds. However, when surface soil temperatures were empirically estimated from HCMM temperatures and percent cover of each pixel, a highly significant correlation ($\text{r = 0.74}{}^7$) was obtained between the estimated soil temperatures and near-surface soil water content.     

Combining thermal inertia and a diurnal temperature difference cycle model to estimate thermal inertia from MSG-SEVIRI data

Thermal inertia is an important parameter in geological and agricultural applications. In this study, we present a method that combines models of thermal inertia and the diurnal temperature difference cycle to estimate the thermal inertia from Meteosat Second Generation Spinning Enhanced Visible and Infrared Imager (MSG-SEVIRI) data. This method can directly derive thermal inertia from MSG-SEVIRI brightness temperatures without the need to include the land surface temperature and emissivity. Two important parameters (the time of the maximum temperature and the diurnal temperature difference) that were input into the thermal inertia model were obtained by fitting the diurnal temperature difference cycle model to the diurnal cycle of land surface temperatures. The spatial distribution of thermal inertia shows that high thermal inertia values occur over vegetated areas, whereas low thermal inertia values occur over bare areas. The uncertainty in thermal inertia is investigated in terms of the uncertainties in the surface albedo, the time of the maximum temperature, and the diurnal temperature difference. The results indicate that the uncertainty in thermal inertia over vegetated areas is greater than that over bare areas. The consistency of the thermal inertia model is evaluated by analysing the difference in thermal inertia values on two consecutive days. The root mean square error of the thermal inertia differences under nearly identical surface and atmospheric conditions on two consecutive days is considered to be the error of the thermal inertia model.     

RS、GIS、GPS在西北农业大开发中的应用前景

遥感(RS)、地理信息系统(GIS)和全球定位系统(GPS)作为三大高新技术(“3S”技术),可以
独立地,也可以相互补充地为农业生产和开发提供强大的技术支撑。它们能快速准确地获取农业生
产系统的多维信息,尤其是时间维的信息,能综合性地管理和处理属性数据和空间数据,并能为农
业生产的决策提供相应的技术服务,进而精确地指导农业生产,促进生态环境的良性发展。论述了
“3S”技术在西北地区农业开发中的应用前景,着重于土壤水分的遥感反演以及干旱和荒漠化的动
态监测。     

Passive microwave emission from smooth bare soils: Developing a simple model to predict near surface water content

MICRO-SWEAT is a coupled microwave emission and soilvegetation-atmosphere transfer scheme. It can predict, among other things, the L band (21 cm wavelength) microwave brightness temperature ( T B ) for a smooth bare soil from knowledge of the soil hydraulic properties and local meteorological data. T B is the product of the apparent emissivity ( e app ) and the effective physical temperature of the soil profile ( T eff ). The modelled relationship between e app and average 0-2 cm soil water content was used to develop a simple semi-empirical model, which predicts the near surface water content from knowledge of the particle size distribution and e app . T eff was found to be comparable to the soil temperature at 11 cm depth, and was estimated using a simple sinusoidal function based on thermal diffusivity to extrapolate the temperature at 11 cm depth from knowledge of the diurnal variation in air temperature. The dependence of T B on incidence angle was predicted by MICRO-SWEAT and also incorporated. This simple model was then successfully used to predict the 0-2 cm water content from knowledge of T B, the air temperature regime and the particle size distribution, for data collected at various look angles over three different soils using a truck-based radiometer.     

基于多源遥感数据协同反演森林地表土壤水分研究

土壤水分在土壤监测中是一项重要的指标,对于农业生产、生态环境以及水资源管理有着重要的影响。随着遥感建模与反演理论的不断成熟,其逐渐成为分析土壤指标的重要技术与手段。因此,利用光学影像与雷达影像数据,以大兴安岭地区漠河市为研究区域,分别建立以Landsat 8为数据源的土壤水分反演模型和由Landsat 8影像数据与GF-3卫星数据协同反演的土壤水分反演模型,将反演结果与实际测得数据进行对比验证,并评价所建立的反演模型。结果表明：①对研究区地温进行反演,利用地表温度（Ts）与归一化差异湿度指数NDMI构建Ts-NDMI特征空间,结合实测数据可以发现Ts-NDMI特征空间土壤水分反演模型的反演结果与实测土壤含水量为负相关性;②协同GF-3卫星数据和Landsat 8遥感影像数据所建立的土壤水分反演模型能得到质量较高的反演结果,且在高植被覆盖度地区,利用该协同反演模型得到的反演结果比利用单一光学数据源所建模型得到的反演结果精度高,为今后高植被覆盖度地区土壤湿度的研究提供了新途径。     

吉林西部盐碱地特性微波双频差分无损探测

针对吉林西部盐碱地特性(内地苏打盐类型),应用车载双频段被动微波遥感系统,对不同盐碱状态的4个裸盐碱区进行双极化多角度微波辐射无损探测。基于多角度双频率双极化的观测数据优势,选择逼近式迭代算法来反演其介电常数虚部,Dobson模型反演介电常数实部。在此基础上,应用双频差分法研究了该区域盐碱地介电特性与含水量、含盐量的关系。双频差分结果表明:实部双频差分与盐碱土含水量呈线性关系,相关系数为0.9996;虚部双频差分与含盐量呈线性关系,相关系数为0.9977。这为应用被动微波遥感定量反演盐碱地特性(含水量、含盐量)奠定了理论基础。     

SOILPSI: a potential-driven three-dimensional soil water redistribution model—description and comparative evaluation

ECOPHYS, an individual-based process model for poplar, requires a three-dimensional soil water redistribution model to simulate soil water dynamics, plant uptake, and root growth. SOILPSI is a potential-driven water redistribution model based on the RHIZOS rhizosphere simulator. It expands on RHIZOS by calculating water flux based on water potential, and has a macropore flow mode to allow rapid drainage of the soil. SOILPSI simulates water flux in three dimensions and accounts for slope. SOILPSI was evaluated by comparing model output to soil moisture data collected under bare soil conditions. AMMI analysis of a date×depth matrix of differences between simulated and observed soil moisture content showed that excluding the two shallowest soil layers resulted in a difference matrix that conformed to an additive model. The grand mean predicted values were within 2% of the observed values, and 50 of 56 predicted values were within 5% of the observed values. Better agreements between simulated and observed soil moisture content were observed deeper in the soil profile and later in the season. Agreement between SOILPSI and field conditions was consistently more accurate than RHIZOS. Improving simulation of evaporative flux at the soil surface would improve simulation accuracy in the upper horizons.     

Utilizing calibrated GPS reflected signals to estimate soil reflectivity and dielectric constant: Results from SMEX02

Extensive reflected GPS data was collected using a GPS reflectometer installed on an HC130 aircraft during the Soil Moisture Experiment 2002 (SMEX02) near Ames, Iowa. At the same time, widespread surface truth data was acquired in the form of point soil moisture profiles, areal sampling of near-surface soil moisture, total green biomass and precipitation history, among others. Previously, there have been no reported efforts to calibrate reflected GPS data sets acquired over land. This paper reports the results of two approaches to calibration of the data that yield consistent results. It is shown that estimating the strength of the reflected signals by either (1) assuming an approximately specular surface reflection or (2) inferring the surface slope probability density and associated normalization constants give essentially the same results for the conditions encountered in SMEX02. The corrected data is converted to surface reflectivity and then to dielectric constant as a test of the calibration approaches. Utilizing the extensive in-situ soil moisture related data this paper also presents the results of comparing the GPS-inferred relative dielectric constant with the Wang-Schmugge model frequently used to relate volume moisture content to dielectric constant. It is shown that the calibrated GPS reflectivity estimates follow the expected dependence of permittivity with volume moisture, but with the following qualification: The soil moisture value governing the reflectivity appears to come from only the top 1-2 cm of soil, a result consistent with results found for other microwave techniques operating at L-band. Nevertheless, the experimentally derived dielectric constant is generally lower than predicted. Possible explanations are presented to explain this result.