Comparison of vegetation water contents derived from shortwave-infrared and passive-microwave sensors over central Iowa

Retrieval of soil moisture content using the vertical and horizontal polarizations of multiple frequency bands on microwave sensors can provide an estimate of vegetation water content (VWC). Another approach is to use foliar-water indices based on the absorption at shortwave-infrared wavelengths by liquid water in the leaves to determine canopy water content, which is then related to VWC. An example of these indices is the normalized difference infrared index (NDII), which was found to be linearly related to canopy water content using various datasets, including data from the Soil Moisture Experiments 2002 and 2005 in central Iowa. Here we compared independent estimates of VWC from WindSat to Moderate resolution Imaging Spectroradiometer (MODIS) NDII over central Iowa from 2003 to 2005. Results showed that there was a linear relationship between the MODIS and WindSat estimates of VWC, although WindSat-retrieved VWC was greater than MODIS-retrieved VWC. WindSat and MODIS have different satellite overpass times and in most climates we expect VWC to vary over a day due to transpiration and plant water stress. However, a sensitivity analysis indicated that the diurnal variation of VWC should not have a significant effect on retrievals of VWC by either method. The results of this study indicated that soil moisture retrievals from microwave sensors may be improved using VWC from optical sensors determined by foliar-water indices and classifications of land cover type.     

Evaluating remotely sensed live fuel moisture estimations for fire behavior predictions in Georgia, USA

Research has shown that remote sensing techniques can be used for assessing live fuel moisture content (LFMC) from space. The need for dynamic monitoring of the fire risk environment favors the use of fast, site-specific, empirical models for assessing local vegetation moisture status, albeit with some uncertainties. These uncertainties may affect the accuracy of decisions made by fire managers using remote sensing derived LFMC. Consequently, the analysis of these LFMC retrieval uncertainties and their impact on applications, such as fire spread prediction, is needed to ensure the informed use of remote sensing derived LFMC measurements by fire managers. The Okefenokee National Wildlife Refuge, one of the most fire-prone regions in the southeastern United States was chosen as our study area. Our study estimates the uncertainties associated with empirical site specific retrievals using NDWI (Normalized Difference Water Index; (R_0.86 − R_1.24) / (R_0.86 + R_1.24)) and NDII (Normalized Difference Infrared Index; (R_0.86 − R_1.64) / (R_0.86 + R_1.64)) that are simulated by coupled leaf and canopy radiative transfer models. In order to support the findings from those simulations, a second approach estimates uncertainties using actual MODIS derived indices over Georgia Forestry Commission stations that provide NFDRS model estimates of LFMC. Finally, we used the FARSITE surface fire behavior model to examine the sensitivity of fire spread rates to live fuel moisture content for the NFDRS high pocosin and southern rough fuel models found in Okefenokee. This allowed us to evaluate the effectiveness of satellite based LFMC estimations for use in fire behavior predictions. Sensitivity to LFMC (measured as percentage of moisture weight per unit dry weight of fuel) was analyzed in terms of no-wind no-slope spread rates as well as normalized spread rates. Normalized spread rates, defined as the ratio of spread rate at a particular LFMC to the spread rate at LFMC of 125 under similar conditions, were used in order to make the results adaptable to any wind-slope conditions. Our results show that NDWI has a stronger linear relationship to LFMC than NDII, and can consequently estimate LFMC with lesser uncertainty. Uncertainty analysis shows that 66% of NDWI based LFMC retrievals over non-sparsely vegetated regions are expected to have errors less than 32, while 90% of retrievals should be within an error margin of 56. In pocosin fuel models, under low LFMC conditions (< 100), retrieval errors could lead to normalized spread rate errors of 6.5 which may be equivalent to an error of 47 m/h in no-wind no-slope conditions. For southern rough fuel models, when LFMC < 175, LFMC retrieval errors could amount to normalized spread rate errors of 0.6 or an equivalent error of 9.3 m/h in no-wind no-slope conditions. These spread rate error estimates represent approximately the upper bound of errors resulting from uncertainties in empirical retrievals of LFMC over forested regions.     

Remote sensing of vegetation water content from equivalent water thickness using satellite imagery

Vegetation water content (VWC) is one of the most important parameters for the successful retrieval of soil moisture content from microwave data. Normalized Difference Infrared Index (NDII) is a widely-used index to remotely sense Equivalent Water Thickness (EWT) of leaves and canopies; however, the amount of water in the foliage is a small part of total VWC. Sites of corn (Zea mays), soybean (Glycine max), and deciduous hardwood woodlands were sampled to estimate EWT and VWC during the Soil Moisture Experiment 2005 (SMEX05) near Ames, Iowa, USA. Using a time series of Landsat 5 Thematic Mapper, Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) and Advanced Wide Field Sensor (AWiFS) imagery, NDII was related to EWT with R² of 0.85; there were no significant differences among land-cover types. Furthermore, EWT was linearly related to VWC with R² of 0.87 for corn and 0.48 for soybeans, with a significantly larger slope for corn. The 2005 land-cover classification product from the USDA National Agricultural Statistics Service had an overall accuracy of 92% and was used to spatially distribute VWC over the landscape. SMEX05 VWC versus NDII regressions were compared with the regressions from the Soil Moisture Experiment 2002 (SMEX02), which was conducted in the same study area. No significant difference was found between years for corn (P = 0.13), whereas there was a significant difference for soybean (P = 0.04). Allometric relationships relate the size of one part of a plant to the sizes of other parts, and may be the result from the requirements of structural support or material transport. Relationships between NDII and VWC are indirect, NDII is related to canopy EWT, which in turn is allometrically related to VWC.     

Errors associated with estimating vegetation water content from radar for use in passive microwave brightness temperature algorithms

The Soil Moisture Active Passive Validation Experiment 2012 was conducted as a pre-launch validation campaign for the Soil Moisture Active Passive mission over 6 weeks in June and July 2012. During this campaign, the Passive Active L-Band System (PALS) was flown at a low altitude, providing radar and radiometer measurements that were contained within a single agricultural field. The campaign domain consisted of 55 agricultural fields, where soil moisture was measured coincident to the PALS flight times and measurements of vegetation volumetric water content (VWC) and leaf area index (LAI) were measured weekly. The low-altitude flights allowed for the comparison between measured VWC and LAI for 11 fields to radar parameters derived from the radar backscatter. Only the correlation between the HV backscatter and the soybean VWC was considered strong (|r| > 0.7). All other correlations between the radar parameters and the VWC (or LAI) were moderate (0.3 < |r| < 0.7) or weak (|r|< 0.3). The established relationships between radar parameters and VWC were used in a forward radiation transfer model to estimate H-pol brightness temperature. It was found that the RMSE between the brightness temperatures estimated using the measured VWC was lowest when using the relationship between VWC and LAI (3.9 K for soybeans, 6.8 K for spring wheat, and 9.3 K when all crop data are combined). Despite a lower correlation, the RMSE associated with using the radar vegetation index relationship with VWC was less than when HV was used (7.9 K) for soybeans, which would result in an error in soil moisture estimation of just over 4%. The RMSEs for all other VWC and radar parameter relationships were greater than 10 K.     

On the terminology of the spectral vegetation index (NIR − SWIR)/(NIR + SWIR)

The spectral vegetation index (ρ_NIR???ρ_SWIR)/(ρ_NIR?+?ρ_SWIR), where ρ_NIR and ρ_SWIR are the near-infrared (NIR) and shortwave-infrared (SWIR) reflectances, respectively, has been widely used to indicate vegetation moisture condition. This index has multiple names in the literature, including infrared index (II), normalized difference infrared index (NDII), normalized difference water index (NDWI), normalized difference moisture index (NDMI), land surface water index (LSWI) and normalized burn ratio (NBR). After reviewing each term's definition, associated sensors and channel specifications, we found that the index consists of three variants, differing only in the SWIR region (1.2–1.3, 1.55–1.75 or 2.05–2.45 μm). Thus, three terms are sufficient to represent these three SWIR variants; other names are redundant and therefore unnecessary. Considering the spectral representativeness, the term's popularity and the ‘rule of priority’ in scientific nomenclature, NDWI, NDII and NBR, each corresponding to the three SWIR regions, are more preferable terms.     

Relationship between light use efficiency and photochemical reflectance index in soybean leaves as affected by soil water content

Photosynthetic light response curves and reflectance spectra (380–2500 nm) were measured for soybean (Glycine max L. Merr.) leaves with a range of chlorophyll concentrations at various soil water contents. Regression lines for the relationship between the photosynthetic light use efficiency (LUEp) and photochemical reflectance index (PRI) under different soil water content θ almost all passed through a common point (PRI, LUEp) = (?0.04, 0), so that the LUEp could be expressed simply as LUEp = kAPRI using an adjusted PRI [APRI = (ρ531?ρ570)/(ρ531?ρ570)+0.04]. The effect of soil moisture was strong under dry conditions and gradually decreased with increasing θ. There was no effect of θ above 25% (v/v). The effect of θ on the APRI–LUEp relationship was expressed by a simple exponential function. These results should provide a new basis for applications in dynamic diagnosis of photosynthetic functioning of plant leaves and in the prediction of plant productivity. The change in the slope of LUE vs. APRI may provide further ways of assessing volumetric soil water content.     

Global surface soil moisture from the Microwave Radiation Imager onboard the Fengyun-3B satellite

Soil moisture retrievals from China’s recently launched meteorological Fengyun-3B satellite are presented. An established retrieval algorithm – the Land Parameter Retrieval Model (LPRM) – was applied to observations of the Microwave Radiation Imager (MWRI) onboard this satellite. The newly developed soil moisture retrievals from this satellite mission may be incorporated in an existing global microwave-based soil moisture database. To reach consistency with an existing data set of multi-satellite soil moisture retrievals, an intercalibration step was applied to correct brightness temperatures for sensor differences between MWRI and the radiometer of the Tropical Rainfall Measuring Mission’s (TRMM’s) Microwave Imager (TMI), resulting from their individual calibration procedures. The newly derived soil moisture and vegetation optical depth product showed a high degree of consistency with parallel retrievals from both TMI and WindSat, the two satellites that are observing during the same time period and are already part of the LPRM database. High correlation (R > 0.60 at night-time) between the LPRM and official MWRI soil moisture products was shown over the validation networks experiencing semiarid climate conditions. The skills drop below 0.50 over forested regions, with the performance of the LPRM product slightly better than the official MWRI product. To demonstrate the promising use of the MWRI soil moisture in drought monitoring, a case study for a recent and unusually dry East Asian summer Monsoon was conducted. The MWRI soil moisture products are able to effectively delineate the regions that are experiencing a considerable drought, highly in agreement with spatial patterns of precipitation and temperature anomalies. The results in this study give confidence in the soil moisture retrievals from the MWRI onboard Fengyun-3B. The integration of the newly derived products into the existing database will allow a better understanding the diurnal, seasonal and interannual variations, and long-term (35 year) changes of soil moisture at the global scale, consequently enhancing hydrological, meteorological, and climate studies.     

A methodology to evaluate the aerosol effective radius based on MODIS aerosol products applicable to other satellite platforms

A strategy to evaluate the effective radius (r _eff) as a function of aerosol retrievals is provided in this work. This methodology is based on the MODerate resolution Imaging Spectroradiometer (MODIS) aerosol products, using the 0.66 and 0.87 µm bands. These data have been studied from February 2000 to December 2005 in a grid situated at Subtropical North‐east Atlantic region. To reduce the number of MODIS useful variables a Factorial Analysis by Principal Components has been applied, decreasing the aerosol parameters from 18 to five. With these parameters, backscattering ratios and asymmetry factors at 0.66 and 0.87 µm besides the Ångström parameter, a lineal multivariate analysis technique has been applied to find the combination of variables that better evaluate the r _eff. The standard error for the predicted value of r _eff is ±0.09 µm. The expression obtained here has the advantage that it can be applied to other remote sensors like AVHRR/NOAA, HRV/SPOT, TM/LANDSAT, and so on, with long time series.     

Evaluation of the SMOS L-MEB passive microwave soil moisture retrieval algorithm

Soil moisture will be mapped globally by the European Soil Moisture and Ocean Salinity (SMOS) mission to be launched in 2009. The expected soil moisture accuracy will be 4.0 %v/v. The core component of the SMOS soil moisture retrieval algorithm is the L-band Microwave Emission of the Biosphere (L-MEB) model which simulates the microwave emission at L-band from the soil-vegetation layer. The model parameters have been calibrated with data acquired by tower mounted radiometer studies in Europe and the United States, with a typical footprint size of approximately 10 m. In this study, aircraft L-band data acquired during the National Airborne Field Experiment (NAFE) intensive campaign held in South-eastern Australia in 2005 are used to perform the first evaluation of the L-MEB model and its proposed parameterization when applied to coarser footprints (62.5 m). The model could be evaluated across large areas including a wide range of land surface conditions, typical of the Australian environment. Soil moisture was retrieved from the aircraft brightness temperatures using L-MEB and ground measured ancillary data (soil temperature, soil texture, vegetation water content and surface roughness) and subsequently evaluated against ground measurements of soil moisture. The retrieval accuracy when using the L-MEB ‘default’ set of model parameters was found to be better than 4.0 %v/v only over grassland covered sites. Over crops the model was found to underestimate soil moisture by up to 32 %v/v. After site specific calibration of the vegetation and roughness parameters, the retrieval accuracy was found to be equal or better than 4.8 %v/v for crops and grasslands at 62.5-m resolution. It is suggested that the proposed value of roughness parameter H_R for crops is too low, and that variability of H_R with soil moisture must be taken into consideration to obtain accurate retrievals at these scales. The analysis presented here is a crucial step towards validating the application of L-MEB for soil moisture retrieval from satellite observations in an operational context.     

Vegetation water content mapping using Landsat data derived normalized difference water index for corn and soybeans

Information about vegetation water content (VWC) has widespread utility in agriculture, forestry, and hydrology. It is also useful in retrieving soil moisture from microwave remote sensing observations. Providing a VWC estimate allows us to control a degree of freedom in the soil moisture retrieval process. However, these must be available in a timely fashion in order to be of value to routine applications, especially soil moisture retrieval. As part of the Soil Moisture Experiments 2002 (SMEX02), the potential of using satellite spectral reflectance measurements to map and monitor VWC for corn and soybean canopies was evaluated. Landsat Thematic Mapper and Enhanced Thematic Mapper Plus data and ground-based VWC measurements were used to establish relationships based on remotely sensed indices. The two indices studied were the Normalized Difference Vegetation Index (NDVI) and the Normalized Difference Water Index (NDWI). The NDVI saturated during the study period while the NDWI continued to reflect changes in VWC. NDWI was found to be superior based upon a quantitative analysis of bias and standard error. The method developed was used to map daily VWC for the watershed over the 1-month experiment period. It was also extended to a larger regional domain. In order to develop more robust and operational methods, we need to look at how we can utilize the MODIS instruments on the Terra and Aqua platforms, which can provide daily temporal coverage.     

Cloud parameter retrievals from Meteosat and their effects on the shortwave radiation at the surface

A method based on Spinning Enhanced Visible and Infrared Imager (SEVIRI) measured reflectance at 0.6 and 3.9 µm is used to retrieve the cloud optical thickness (COT) and cloud effective radius (r_e) over the Iberian Peninsula. A sensitivity analysis of simulated retrievals to the input parameters demonstrates that the cloud top height is an important factor in satellite retrievals of COT and r_e with uncertainties around 10% for small values of COT and r_e; for water clouds these uncertainties can be greater than 10% for small values of r_e. The uncertainties found related with geometries are around 3%. The COT and r_e are assessed using well-known satellite cloud products, showing that the method used characterize the cloud field with more than 80% (82%) of the absolute differences between COT (r_e) mean values of all clouds (water plus ice clouds) centred in the range from ±10 (±10 µm), with absolute bias lower than 2 (2 μm) for COT (r_e) and root mean square error values lower than 10 (8 μm) for COT (r_e). The cloud water path (CWP), derived from satellite retrievals, and the shortwave cloud radiative effect at the surface (CRE_SW) are related for high fractional sky covers (F_sc >0.8), showing that water clouds produce more negative CRE_SW than ice clouds. The COT retrieved was also related to the cloud modification factor, which exhibits reductions and enhancements of the surface SW radiation of the order of 80% and 30%, respectively, for COT values lower than 10. A selected case study shows, using a ground-based sky camera that some situations classified by the satellite with high F_sc values correspond to situations of broken clouds where the enhancements actually occur. For this case study, a closure between the liquid water path (LWP) obtained from the satellite retrievals and the same cloud quantity obtained from ground-based microwave measurements was performed showing a good agreement between both LWP data set values.     

A MODIS-based perpendicular moisture index to retrieve leaf moisture content of forest canopies

Moisture dictates vegetation susceptibility to fire ignition and propagation. Various spectral indices have been proposed for the estimation of equivalent water thickness (EWT), which is defined as the mass of liquid water per unit of leaf surface. However, fire models use live fuel moisture content (LFMC) as a measure of vegetation moisture. LFMC is defined as the ratio of the mass of the liquid water in a leaf over the mass of dry matter, and traditional spectral indices are not as effective as with EWT in capturing LFMC variability. The aim of this research was to explore the potential of the Moderate Resolution Imaging Spectroradiometer (MODIS) on board Terra and Aqua satellites in retrieving LFMC from top of the canopy reflectance, and to develop a new spectral index sensitive to this parameter. All the analyses were based on synthetic canopy spectra constructed by coupling the PROSPECT (leaf optical properties model) and SAIL (Scattering by Arbitrarily Inclined Leaves) radiative transfer models. Simulated top of the canopy spectra were then convolved to MODIS ‘land’ channels 1–7 spectral response functions. All band pairs were evaluated to determine the subspace of MODIS measurements where the separability of points based on their value of LFMC was the highest. This led to the identification of isolines of LFMC in the plane defined by MODIS reflectance measurements in channels 2 and 5; the isolines are straight and parallel, and ordered from lower to higher values of LFMC. This observation allowed the construction of a novel spectral index that is directly related to LFMC – the perpendicular moisture index (PMI). This index measures the distance of a point in the plane spanned by reflectance measurements in MODIS channels 2 and 5 from a reference line, that of completely dry vegetation. Validation against simulated data showed that PMI exhibits a linear relationship with LFMC. When the vegetation cover is dense, the LFMC explains most of the variability in the PMI (R² = 0.70 when LAI > 2; R² = 0.87 when LAI > 4). When the LAI is lower, the contribution of soil background to the measured reflectance increases, and the index underestimates LFMC. The PMI was also validated against the LOPEX93 (Leaf Optical Properties Experiment 1993) data set of leaf optical and biophysical measurements, scaled to canopy reflectance with SAIL, showing acceptable results (R² = 0.56 when LAI > 2; R² = 0.63 when LAI > 4).     

An in situ study of the effects of surface anisotropy on the remote sensing of burned savannah

This Letter presents field‐based evidence of the perturbing effects of surface anisotropy on the remote sensing of burned savannah. The analysis is based on bidirectional spectral reflectance data collected at different solar illumination angles and convolved to Moderate‐resolution Imaging Spectroradiometer (MODIS) reflective bands. Results from a grass savannah site show that burning reduces the anisotropy of the surface compared to its pre‐burn state. In contrast, at a shrub savannah site, burning reduces or increases surface anisotropy. Spectral indices defined from 1.240 µm and 2.130 µm reflectance, and 1.640 µm and 2.130 µm reflectance, provided stronger diurnal separation between burned and unburned areas than individual reflectance bands but do not eliminate anisotropic effects. The Normalized Difference Vegetation Index (NDVI) provides weak diurnal separation relative to these near‐ and mid‐infrared based indices. Implications of the findings are discussed for burned area mapping.     

Reflectance spectroscopy for the assessment of soil salt content in soils of the Yellow River Delta of China

There has been growing interest in the use of reflectance spectroscopy as a rapid and inexpensive tool for soil characterization. In this study, we collected 95 soil samples from the Yellow River Delta of China to investigate the level of soil salinity in relation to soil spectra. Sample plots were selected based on a field investigation and the corresponding soil salinity classification map to maximize variations of saline characteristics in the soil. Spectral reflectances of air‐dried soil samples were measured using an Analytical Spectral Device (ASD) spectrometer (350–2500 nm) with an artificial light source. In the Yellow River Delta, the dominant chemical in the saline soil was NaCl and MgCl₂. Soil spectra were analysed using two‐thirds of the available samples, with the remaining one‐third withheld for validation purposes. The analysis indicated that with some preprocessing, the reflectance at 1931–2123 nm and 2153–2254 nm was highly correlated with soil salt content (S _SC). In the spectral region of 1931–2123 nm, the correlation R ranged from ?0.80 to ?0.87. In the region of 2153–2254 nm, the S _SC was positively correlated with preprocessed reflectance (0.79–0.88). The preprocessing was done by fitting a convex hull to the reflectance curve and dividing the spectral reflectance by the value of the corresponding convex hull band by band. This process is called continuum removal, and the resulting ratio is called continuum removed reflectance (CR reflectance). However, the S _SC did not have a high correlation with the unprocessed reflectance, and the correlation was always negative in the entire spectrum (350–2500 nm) with the strongest negative correlation at 1981 nm (R = ?0.63). Moreover, we found a strong correlation (R = 0.91) between a soil salinity index (S _SI: constructed using CR reflectance at 2052 nm and 2203 nm) and S _SC. We estimated S _SC as a function of S _SI and S _SI′ (S _SI′: constructed using unprocessed reflectance at 2052 nm and 2203 nm) using univariate regression. Validation of the estimation of S _SC was conducted by comparing the estimated S _SC with the holdout sample points. The comparison produced an estimated root mean squared error (RMSE) of 0.986 (S _SC ranging from 0.06 to 12.30 g kg^?1) and R ² of 0.873 for S _SC with S _SI as independent variable and RMSE of 1.248 and R ² of 0.8 for S _SC with S _SI′ as independent variable. This study showed that a soil salinity index developed for CR reflectance at 2052 nm and 2203 nm on the basis of spectral absorption features of saline soil can be used as a quick and inexpensive method for soil salt‐content estimation.     

Soil moisture retrievals at L-band using a two-step inversion approach (COSMOS/NAFE'05 Experiment)

COSMOS (Campaign for validating the Operation of Soil Moisture and Ocean Salinity), and NAFE (National Airborne Field Experiment) were two airborne campaigns held in the Goulburn River catchment (Australia) at the end of 2005. These airborne measurements are being used as benchmark data sets for validating the SMOS (Soil Moisture and Ocean Salinity) ground segment processor over prairies and crops. This paper presents results of soil moisture inversions and brightness temperature simulations at different resolutions from dual-polarisation and multi-angular L-band (1.4 GHz) measurements obtained from two independent radiometers. The aim of the paper is to provide a method that could overcome the limitations of unknown surface roughness for soil moisture retrievals from L-band data. For that purpose, a two-step approach is proposed for areas with low to moderate vegetation. Firstly, a two-parameter inversion of surface roughness and optical depth is used to obtain a roughness correction dependent on land use only. This step is conducted over small areas with known soil moisture. Such roughness correction is then used in the second step, where soil moisture and optical depth are retrieved over larger areas including mixed pixels. This approach produces soil moisture retrievals with root mean square errors between 0.034 m³ m^− 3 and 0.054 m³ m^− 3 over crops, prairies, and mixtures of these two land uses at different resolutions.     

Comparison of different soil dielectric models for microwave soil moisture retrievals

ABSTRACT

Soil dielectric model is an essential part of the microwave soil moisture retrieval process. This study compared the performance of four widely used dielectric models, the Wang-Schmugge model (WS), Dobson model, generalized refractive mixing dielectric model (GRMDM), and multi-relaxation generalized refractive mixing dielectric model (MRGRMDM), and investigated the effects of the uncertainties of each model on soil moisture retrievals. Furthermore, the simulated soil dielectric constants were evaluated by measured dielectric data at the P/L/C/X bands. The results showed that the uncertainties induced in soil moisture retrievals by an alternative dielectric model exceeded 0.09 m³ m^?3 in the worst case. The Dobson model is sensitive to the sand content. WS, GRMDM, and MRGRMDM model are sensitive to the clay content. The measured dielectric data further verified that the applicability of each dielectric model depends on the soil texture type and soil moisture condition. Compared with Dobson model, WS showed better performance at dry soil. GRMDM and MRGRMDM provided better results under lower clay content soil. Especially, MRGRMDM has better simulation accuracy than GRMDM in the low-frequency range (< 1 GHz).     

Influence of forest cover fraction on L-band soil moisture retrievals from heterogeneous pixels using multi-angular observations

Airborne L-band data from the Australian National Airborne Field Experiment 2005 (NAFE '05) field campaign were used to investigate the influence of fractional forest cover on soil moisture retrievals from heterogeneous (grass/forest) pixels. This study is, to our knowledge, the first to use experimental data on this subject and was done in view of the SMOS mission, in order to contribute to calibration/validation studies and the analysis of heterogeneous surfaces. Because the multi-angle observations were contained in swaths, swaths were used instead of pixels as the basic surface unit in this study. Simultaneous retrievals of soil moisture (SM) and vegetation optical depth (τ_NAD) were undertaken by inversion of the L-MEB zero-order radiative transfer model. This was done for two different retrieval configurations, the first consisting of swath-effective values of SM and τ_NAD and the second consisting of values of SM and τ_NAD for the non-forested (i.e. grass) fraction of the swath, with forest emission known from forward modelling. Model inputs for non-retrieved parameters were either default values taken from the literature or site- and time-specific values obtained from observations of nearby homogeneous swaths gathered during the same flight. The main focus of this study was on retrieval behaviour for various soil moisture conditions and forest fractions. Area-averaged retrieval results were generally very reasonable for both retrieval configurations. When retrieving swath-effective values of SM and τ_NAD, τ_NAD showed an increased overestimation with increased forest fraction. Highest retrieved values of SM were found at intermediate values of forest fraction. The results show the difficulty in flagging upper limits of pixel forest fraction during soil moisture retrievals, besides the fact that erroneous parameter values can lead to high errors in retrieved SM, especially in wet conditions. This study is the first to give a realistic idea of the errors and uncertainties involved in soil moisture retrievals from partly forested swaths, and as such will contribute to a better understanding of SMOS calibration/validation issues.     

Comparison of ground-based and remotely-sensed surface soil moisture estimates over complex terrain during SMEX04

Comparisons to ground-based surface soil moisture estimates are necessary to evaluate the capability of remote sensors to determine soil moisture and its spatiotemporal variability. Soil moisture can be especially variable in regions of complex terrain which exhibit large variations in vegetation, soil properties and hydrologic conditions. The objective of this study is to evaluate the spatiotemporal variability of soil moisture in a mountainous basin in northwestern Mexico. Soil moisture estimates from ground sampling over a topographic transect and high resolution retrievals from the Polarimetric Scanning Radiometer are compared during a two week period in August 2004 as part of the Soil Moisture Experiment 2004. Results indicate that the soil moisture estimates exhibit similar variability with mean water content. Statistical analysis, however, reveals clear differences in soil moisture in the basin, in particular for wet periods and high elevations. Despite these differences, the temporal persistence of soil moisture from the estimates agrees well and indicates locations that capture the basin-averaged conditions. Furthermore, the spatiotemporal soil moisture characteristics from the two products are linked to terrain attributes. As a result, a hypsometric technique is shown to improve comparisons between basin-averaged values derived from ground data and remote sensing, as compared to arithmetic averaging. To our knowledge, this study is the first attempt to evaluate PSR/CX retrievals with respect to ground observations over a region of high topographic and vegetation variability using statistical, time-stability and terrain analysis techniques.     

Estimating the leaf area index in Indian tropical forests using Landsat-8 OLI data

Leaf area index (LAI) is a key vegetation biophysical parameter and is extensively used in modelling of phenology, primary production, light interception, evapotranspiration, carbon, and nitrogen dynamics. In the present study, we attempt to spatially characterize LAI for natural forests of Western Ghats India, using ground based and Landsat-8 Operational Land Imager (OLI) sensor satellite data. For this, 41 ground-based LAI measurements were carried out across a gradient of tropical forest types, viz. dry, moist, and evergreen forests using LAI-2200 plant canopy analyser, during the month of March 2015. Initially, measured LAI values were regressed with 15 spectral variables, including nine spectral vegetation indices (SVIs) and six Landsat-8 surface reflectance (ρ) variables using univariate correlation analysis. Results showed that the red (ρ_red), near-infrared (ρ_NIR), shortwave infrared (ρ_SWIR1, ρ_SWIR2) reflectance bands (R² > 0.6), and all SVIs (R² > 0.7) except simple ratio (SR) have the highest and second highest coefficient of determination with ground-measured LAI. In the second step, to select significant (high R², low root mean square error (RMSE), and p-level < 0.05) SVIs to determine the best representative model, stepwise multiple linear regression (SMLR) was implemented. The results indicate that the SMLR model predicted LAI with better coefficient of determination (R² = 0.83, RMSE = 0.78) using normalized difference vegetation index, enhanced vegetation index, and soil-adjusted vegetation index variables compared to the univariate approach. The predicted SMLR model was used to estimate a spatial map of LAI. It is desirable to evaluate the stability and potentiality of regional LAI models in natural forest ecosystems against the operationally accepted Moderate Resolution Imaging Spectroradiometer (MODIS) global LAI product. To do this, the Landsat-8 pixel-based LAI map was resampled to 1 km resolution and compared with the MODIS derived LAI map. Results suggested that Landsat-8 OLI-based VIs provide significant LAI maps at moderate resolution (30 m) as well as coarse resolution (1 km) for regional climate models.     

Evaluation of MODIS derived perpendicular drought index for estimation of surface dryness over northwestern China

In this paper, drought status of northwestern China is evaluated using the Terra–Moderate Resolution Imaging Spectroradiometer (MODIS) data with a newly developed method called perpendicular drought index (PDI), which is defined as a line segment that is parallel with the soil line and perpendicular to the normal line of soil line intersecting the coordinate origin in the two‐dimensional scatter plot of red against near infrared (NIR) wavelength reflectance. To validate the PDI in macroscale applications, quantitative evaluation of drought conditions in Ningxia, Northwestern China is carried out by comparing the PDI with one of the well‐known drought indexes, namely, temperature‐vegetation index (TVX). Linear regression between ground‐measured soil moisture data and the PDI and the TVX was made. Results show that satellite based PDI and TVX has significant correlation with 0–20 cm averaged soil moisture obtained over the meteorological observing stations across the whole study area. The highest correlation of R ² = 0.48 for the PDI and R ² = 0.40 for the TVX is obtained when compared with average soil moisture from 0 to 20 cm soil depth. According to the drought critical values defined by soil hydrologic parameters including soil moisture, wilting coefficient and field moisture capacity, the PDI based drought guidelines are established, and then the drought status in the study area is evaluated using the PDI. It is evident from the results showing the spatial distribution of drought in northwestern China that the PDI is highly accordant with field drought status.