Normalized Soil Moisture Research Articles

Evaluation of root zone soil moisture products over the Huai River basin

Abstract. Root zone soil moisture (RZSM) is critical for water resource management, drought monitoring and sub-seasonal flood climate prediction. While RZSM is not directly observable from space, several RZSM products are available and widely used at global and continental scales. This study conducts a comprehensive and quantitative evaluation of eight RZSM products using observations from 58 in situ soil moisture stations over the Huai River basin (HRB) in China. Attention is drawn to the potential factors that contribute to the uncertainties of model-based RZSM, including the errors in atmospheric forcing, vegetation parameterizations, soil properties and spatial scale mismatch. The results show that the Global Land Data Assimilation System Catchment Land Surface Model (GLDAS_CLSM) outperforms the other RZSM products with the highest correlation coefficient (R= 0.69) and the lowest unbiased root mean square error (ubRMSE = 0.018 m3 m−3), while SMOS Level 4 (L4) RZSM shows the worst performance among eight RZSM products. The RZSM products based on land surface models generally perform better in the wet season than in the dry season due to the enhanced ability to capture of the temporal dynamics of in situ observations in the wet season and the inertia of remaining high soil moisture values even in the dry season, while the SMOS L4 RZSM product, derived from SMOS L3 surface moisture (SSM) combined with an exponential filter method, performs better in the dry season due to the attenuated ground microwave radiation signal caused by the increased water vapour absorption and scattering in the wet season. The underestimated SMOS L3 SSM triggers the underestimation of RZSM in SMOS L4. The overestimated RZSM products based on land surface models could be associated with the overestimated precipitation amounts and frequency, the underestimated air temperature, and the underestimated ratio of transpiration to the total terrestrial evapotranspiration. In addition, the biased soil properties and flawed vegetation parameterizations affect the hydrothermal transport processes represented in different land surface models (LSMs) and lead to inaccurate soil moisture simulation. The scale mismatch between point and footprint also introduces representative errors. The comparison of frequency of normalized soil moisture between RZSM products and in situ observations indicates that the LSMs should focus on reducing the frequency of wet soil moisture, increasing the frequency of dry soil moisture and the ability to capture the frequency peak of soil moisture. The study provides some insights into how to improve the ability of land surface models to simulate the land surface states and fluxes by taking into account the issues mentioned above. Finally, these results can be extrapolated to other regions located in similar climate zones, as they share similar precipitation patterns that dominate the terrestrial water cycle.

Evaluation of GLDAS soil moisture product over Kermanshah province, Iran

Abstract Land surface modelling and data assimilation are advanced techniques for generating optimal fields of land surface states and fluxes. In this study, the Global Land Data Assimilation System (GLDAS) data were utilized to investigate the soil moisture variations and droughts in Kermanshah province, northwest Iran. The GLDAS soil moisture data were employed in various depths and compared with observed monthly soil moisture. The monthly and annual moisture data were processed in the Geographic Information System (GIS) environment. To compute the Standardized Precipitation Index, SPI, precipitation data from 2000 to 2014 were used, and the relationship between drought and soil moisture variation was studied. The moisture data from GLDAS had a significant correlation with the most severe wet and dry seasons. The minimum and maximum values of the SPI were determined as −2.077 and 0.931 in 2004 and 2009, respectively, which corresponded to the highest and lowest normalized soil moisture of −1.93 and 1.41. The results showed that GLDAS data can be used to reconstruct spatial and temporal moisture data series.

Sensitivity of Modeled Soil NOx Emissions to Soil Moisture

AbstractAs emissions of nitrogen oxides (NOx) from fossil fuel combustion decrease, the relative contribution of NOx emissions from managed and unmanaged soils (SNOx) is increasing. Modeling SNOx presents a challenge as it requires proper characterization of emission dynamics in response to environmental conditions. SNOx is often represented using the Berkeley Dalhousie Soil NOx Parameterization (BDSNP), which relies upon static relationships between soil moisture and SNOx for arid and non‐arid lands. However, soil chamber and atmospheric studies have shown that emission characteristics are more dynamic, with peak emissions often occurring at higher soil moisture content. Here, to better capture observational studies, we update BDSNP by creating a dynamic SNOx response to soil moisture based on a normalized soil moisture index. We compare the standard and updated parameterizations over the contiguous United States (U.S.) for 2011–2020 using input soil moisture data from ERA5‐Land, MERRA‐2 and NLDAS2‐Mosaic and evaluate SNOx across these different input drivers as well as between the standard and updated parameterizations. The standard parametrization exhibits strong sensitivity to different input soil moisture products, with annual U.S. SNOx differences of up to 0.28 Tg N yr−1. In contrast, the updated parameterization provides a robust representation of SNOx with reduced sensitivity to input soil moisture product with differences of at most 0.03 Tg N yr−1. The updated parameterization simulates a broad increase in SNOx in non‐arid regions, including much of the Eastern U.S., indicating that this region may be more sensitive to climatically‐driven SNOx as anthropogenic NOx emissions continue to decline.

Soil Moisture Sensor Measurement and Vegetation-Soil-Water Related Indices – A Case Study in Mango Plantation, Nakhorn Ratchasima Province, Thailand

This study conducted the soil moisture estimation using remote sensing data and ground soil moisture sensors in the mango plantation. The applied methodology is the spatial and statistical analysis to determine the relationship between the measured soil moisture using ground sensors and the remote sensing indices generated from Sentinel-2A satellite images. The sensors measured the soil moisture ground data from Nov. 2019 to Feb. 2020. However, we used only the data on seven dates. This is because the cloud-free satellite images are available only on these dates to generate the remote sensing indices. The used indices are NDVI, Normalized Water Moisture Index (NDWI) for vegetation water content monitoring, Normalized Soil Moisture Index (NSMI) for data visualization and analysis. In the implementation, we first visualized the soil moisture trend compared with the remote sensing indices value at the image pixels of sensor location on each observation day. Next, we statistically analyzed the spatial data to establish the relationship between the soil moisture from all the ground sensors and the remote sensing indices. However, the output R2 is very low; then, it brings us to have an idea to apply in-depth analysis based on the ground sensor performance. This method shows an interesting result. We found that only the NDWI for monitoring vegetation water content has a similar trend with the soil moisture. Secondly, we performed the linear regression correlation between soil moisture and remote sensing indices values of each sensor as time-series analysis. The result show that the correlation between soil moisture and NDWI, NSMI and NDVI are classified into 3 groups, which are 0.7 < R2 < 0.9, 0.6 < R2 < 0.7, and R2 < 0.5, where their corresponding p-value ranges are 0.001 < p-value < 0.02, 0.01 < p-value < 0.03, and 0.08 < p-value < 0.9, respectively. Lastly, we investigated the reason that causes the very high correlation between the soil moisture value of the first group of sensors and NDWI and NSMI. The result shows that these sensors are in a sparse vegetation cover area, where NDVI ≤ 0.3. Therefore, according to this, we can conclude that remote sensing indices NDWI and NSMI can be applied for soil moisture estimation in a sparsely vegetated study area, where the NDVI value should be less than or equal to 0.3.

Mapping Irrigated Area at Field Scale Based on the OPtical TRApezoid Model (OPTRAM) Using Landsat Images and Google Earth Engine

Irrigation is critical to agricultural production in arid and semiarid regions, and it is imperative to map high-resolution irrigated area to improve water productivity. This study proposes a field-scale (30-m resolution) irrigated area mapping method based on soil moisture change detection using remote sensing data only. First, normalized soil moisture is obtained using the optical trapezoid model (OPTRAM) and then converted to soil water content. Next, individual irrigation events are identified in the time series of soil water content using threshold detection. Finally, irrigation events are accumulated over the time series, and then, the irrigated area map can be obtained. This method was tested using Google Earth Engine (GEE) to analyze remote sensing images and map irrigated areas in a typical arid and semiarid region called Hexi Corridor in northwestern China in the past 30 years. <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">In situ</i> validation shows that this method has an accuracy close to 100%. The shortcoming of low recall is also overcome by long-term observations. An application of the proposed method shows that the irrigated cropland of Hexi Corridor has increased by 4840 km <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> (42.2%) over a 31-year time period (1990–2020). This field-scale irrigated area mapping method can improve the management of water resources.

Remote Detection of Cyanobacterial Blooms and Chlorophyll-a Analysis in a Eutrophic Reservoir Using Sentinel-2

Harmful cyanobacterial blooms have been one of the most challenging ecological problems faced by freshwater bodies for more than a century. The use of satellite images as a tool to analyze these blooms is an innovative technology that will facilitate water governance and help develop measures to guarantee water security. To assess the viability of Sentinel-2 for identifying cyanobacterial blooms and chlorophyl-a, different bands of the Sentinel-2 satellite were considered, and those most consistent with cyanobacteria analysis were analyzed. This analysis was supplemented by an assessment of different indices and their respective correlations with the field data. The indices assessed were the following: Normalized Difference Water Index (NDWI), Normalized Differences Vegetation Index (NDVI), green Normalized Difference Vegetation Index (gNDVI), Normalized Soil Moisture Index (NSMI), and Toming’s Index. The green band (B3) obtained the best correlating results for both chlorophyll (R2 = 0.678) and cyanobacteria (R2 = 0.931). The study by bands of cyanobacteria composition can be a powerful tool for assessing the physiology of strains. NDWI gave an R2 value of 0.849 for the downstream point with the concentration of cyanobacteria. Toming’s Index obtained a high R2 of 0.859 with chlorophyll-a and 0.721 for the concentration of cyanobacteria. Notable differences in correlation for the upstream and downstream points were obtained with the indices. These results show that Sentinel-2 will be a valuable tool for lake monitoring and research, especially considering that the data will be routinely available for many years and the images will be frequent and free.

Maximizing Temporal Correlations in Long-Term Global Satellite Soil Moisture Data-Merging

In this study, an existing combination approach that maximizes temporal correlations is used to combine six passive microwave satellite soil moisture products from 1998 to 2015 to assess its added value in long-term applications. Five of the products used are included in existing merging schemes such as the European Space Agency’s essential climate variable soil moisture (ECV) program. These include the Special Sensor Microwave Imagers (SSM/I), the Tropical Rainfall Measuring Mission (TRMM/TMI), the Advanced Microwave Scanning Radiometer-Earth Observing System (AMSR-E) sensor on the National Aeronautics and Space Administration’s (NASA) Aqua satellite, the WindSAT radiometer, onboard the Coriolis satellite and the soil moisture retrievals from the Advanced Microwave Scanning Radiometer 2 (AMSR2) sensor onboard the Global Change Observation Mission on Water (GCOM-W). The sixth, the microwave radiometer imager (MWRI) onboard China’s Fengyun-3B (FY3B) satellite, is absent in the ECV scheme. Here, the normalized soil moisture products are merged based on their availability within the study period. Evaluation of the merged product demonstrated that the correlations and unbiased root mean square differences were improved over the whole period. Compared to ECV, the merged product from this scheme performed better over dense and sparsely vegetated regions. Additionally, the trends in the parent inputs are preserved in the merged data. Further analysis of FY3B’s contribution to the merging scheme showed that it is as dependable as the widely used AMSR2, as it contributed significantly to the improvements in the merged product.

Development of soil moisture indices from differences in water absorption between shortwave-infrared bands

Soil moisture (SM) controls the exchange of water and heat energy between the land surface and the atmosphere through evaporation and plant transpiration, and timely and accurate estimates of soil moisture are crucial for many studies. Although remote sensing provides many algorithms to obtain bare-soil moisture on large scales (e.g., Soil Moisture and Ocean Salinity (SMOS) and Soil Moisture Active and Passive (SMAP) products), these algorithms are restricted to studies with low ground resolution. In the present study, we propose and evaluate three normalized shortwave-infrared (SWIR) difference bare soil moisture indices [NSDSI1 = (BSWIR1-BSWIR2)/BSWIR1, NSDSI2 = (BSWIR1 − BSWIR2)/BSWIR2, NSDSI3 = (BSWIR1 − BSWIR2)/(BSWIR1 + BSWIR2), where, SWIR1 = 1550–1750 nm, SWIR2 = 2100–2300 nm] to estimate the bare-soil moisture content and, based on the water absorption difference between shortwave-infrared bands, use them to map bare-soil moisture with high ground resolution Sentinel-2 MSI images. The three proposed bare-soil moisture indices are obtained by using the different water absorption in shortwave-infrared bands. Four traditional hyperspectral-based bare-soil moisture indices (such as the water index SOIL, or WISOIL, the normalized soil moisture index, or NSMI, etc.) were used as benchmark. The results show that (i) the differences in water absorption between shortwave-infrared bands is linear in soil moisture content; (ii) the combined use of two shortwave-infrared bands from four soils provides more accurate bare-soil moisture estimates than do single shortwave-infrared (SWIR) bands, and (iii) our proposed bare-soil moisture indices can be applied on broadband remote-sensing images (such as Landsat, Sentinel-2 MSI). The bare-soil moisture estimates obtained by using the proposed bare-soil moisture indices may help to obtain bare-soil moisture maps with high ground resolution.

Land Surface Parameter and State Perturbations in the Global Ensemble Forecast System

AbstractThe National Centers for Environmental Prediction (NCEP) Global Ensemble Forecast System (GEFS) is underdispersive near the surface, a common characteristic of ensemble prediction systems. Here, several methods for increasing the spread are tested, including perturbing soil initial conditions, soil tendencies, and surface parameters, with physically based perturbations. Perturbations are applied to the soil initial conditions based on empirical orthogonal functions (EOFs) of differences between normalized soil moisture states from two land surface models (LSMs). Perturbations to roughness lengths for heat and momentum, soil hydraulic conductivity, stomatal resistance, vegetation fraction, and albedo are applied, with the amplitude and perturbation scales based on previous research. Soil moisture and temperature tendencies are also perturbed using a stochastic perturbation scheme. The results show that surface perturbations, through their impact on 2-m temperature spread, have a modest positive impact on the skill of short-range ensemble forecasts. However, adjusting the forecasts using an estimate of the systematic bias shows that bias correction has a greater impact on the forecast reliability than surface perturbations, indicating that systematic bias in the model needs to be addressed as well.

Prediction of Soil Organic Matter by VIS–NIR Spectroscopy Using Normalized Soil Moisture Index as a Proxy of Soil Moisture

Soil organic matter (SOM) is an important parameter of soil fertility, and visible and near-infrared (VIS–NIR) spectroscopy combined with multivariate modeling techniques have provided new possibilities to estimate SOM. However, the spectral signal is strongly influenced by soil moisture (SM) in the field. Interest in using spectral classification to predict soils in the moist conditions to minimize the influence of SM is growing. The objective of this study was to investigate the transferability of two approaches, SM–based cluster method with known SM (classifying the VIS–NIR spectra into different SM clusters to develop models separately), the normalized soil moisture index (NSMI)–based cluster method with unknown SM (utilizing NSMI to indicate the SM and establish models separately), to predict SOM directly in moist soil spectra. One hundred and twenty one soil samples were collected from Central China, and eight SM levels were obtained for each sample through rewetting experiments. Their reflectance spectra and SOM concentrations were measured in the laboratory. Partial least square-support vector machine (PLS-SVM) was employed to construct SOM prediction models. Specifically, prediction models were developed for NSMI–based clusters with unknown SM data. The models were assessed through three statistics in the processes of calibration and validation: the coefficient of determination (R2), root mean square error (RMSE) and the ratio of the performance to deviation (RPD). Results showed that the variable SM led to reduced VIS–NIR reflectance nonlinearly across the entire spectral range. NSMI was an effective spectral index to indicate the SM. Classifying the VIS–NIR spectra into different SM clusters in known SM states could improve the performance of PLS-SVM models to acceptable prediction accuracies (R2cv = 0.69–0.77, RPD = 1.79–2.08). The estimation of SOM, when using the NSMI–based cluster method with unknown SM (RPD = 1.95–2.04), was similar to the use of the SM–based cluster method with known SM (RPD = 1.79–2.08). The predictive results (RPD = 1.87–2.06) demonstrated that the NSMI-–based cluster method has potential for application outside the laboratory for SOM prediction without knowing the SM explicitly, and this method is also easy to carry out and only requires spectral information.

NIR-Red Spectra-Based Disaggregation of SMAP Soil Moisture to 250 m Resolution Based on OzNet in Southeastern Australia

To meet the demand of regional hydrological and agricultural applications, a new method named near infrared-red (NIR-red) spectra-based disaggregation (NRSD) was proposed to perform a disaggregation of Soil Moisture Active Passive (SMAP) products from 36 km to 250 m resolution. The NRSD combined proposed normalized soil moisture index (NSMI) with SMAP data to obtain 250 m resolution soil moisture mapping. This NRSD method was validated with the data from in situ OzNet network in May and September 2015. Results showed that NRSD performed a decent downscaling (root-mean-square error (RMSE) = 0.04 m3/m3 and 0.12 m3/m3 in May and September, respectively). Based on the validation, it was found that the proposed NSMI was a new alternative indicator for denoting the heterogeneity of soil moisture at sub-kilometer scales. Attributed to the excellent performance of the NSMI, NRSD has a higher overall accuracy, finer spatial representation within SMAP pixels and wider applicable scope on usability tests for land cover, vegetation density and drought condition than the disaggregation based on physical and theoretical scale change (DISPATCH) has at 250 m resolution. This revealed that the NRSD method is expected to provide soil moisture mapping at 250-resolution for large-scale hydrological and agricultural studies.

Prediction of water content and normalized evaporation from oil sands soft tailings surface using hyperspectral observations

The paper addresses the challenge of measuring water content and evaporative fluxes from oil sands soft tailings surfaces using hyperspectral observations. Hyperspectral time-series laboratory observations were collected from four different mature fine tailings (MFT) samples displaying variations in swelling potential and bitumen concentration. The samples were allowed to evaporate from an initial state of water saturation to an air-dried state. From these data, several spectral features were evaluated to predict water content and normalized evaporation rate from the optically sensed portion of the tailings surface (less than a few hundred micrometres). For the samples tested, the best estimate of moisture content was achieved using the normalized soil moisture index (NSMI) index (coefficient of determination R2 = 0.97). The absolute reflectance at 1920 nm was found to be the best spectral estimator of normalized evaporation (R2 = 0.97), with the NSMI index also being valuable (R2 = 0.95). In both instances, the NSMI index may be of value for estimations attempted in the field. Remote estimation of moisture content and evaporation could help tailings managers assess the drying process to determine when the deposit has stopped drying at the surface and decide when the next lift should be deposited. In future efforts, the models obtained from this laboratory investigation will be assessed for their applicability in field settings and validated using concurrent sampling.

Analysis of soil moisture dynamics beneath olive trees

AbstractOlive cultivation is a widespread land use in Mediterranean climates. The proper implementation of soil and water conservation practices in groves requires detailed knowledge of the governing hydrological processes. In this work topsoil moisture dynamics under wet and dry conditions and across a small catchment was investigated in the inter row (IR) and directly under the olive tree canopies (UC). We do this using a sensor network (11 stations) and a simple bucket model which was calibrated (June, 2011–2012) and validated (June, 2012–2013). During most of the year the normalized soil moisture contents (s) were greater in the IR than under UC, with an average normalized soil moisture difference of 0.12. The difference between UC and IR normalized soil moisture followed a seasonal pattern, reaching a maximum near 0.30 during spring. An analysis of the normalized soil moisture probability density functions (pdfs) was bimodal, showing characteristic dominant wet and dry soil moisture states, with the highest probability densities for the dry state. Overall the spatial variability of soil moisture was lower UC than in the IR. This was a result of the soil moisture buffering capacity of the canopy with respect to rainfall and evaporation, in addition to observed differences in soil properties. Hourly soil moisture data were successfully modelled (R2 &gt; 0.85), both UC and in the IR, yet with the inclusion of a simple formulation for canopy interception for the former. The results provide insight into how olive trees change hydrological processes in their neighbourhood. Copyright © 2016 John Wiley &amp; Sons, Ltd.

Hyperspectral Surface Reflectance Data Detect Low Moisture Status of Pecan Orchards during Flood Irrigation

For large fields, remote sensing might permit plant low moisture status to be detected early, and this may improve drought detection and monitoring. The objective of this study was to determine whether canopy and soil surface reflectance data derived from a handheld spectroradiometer can detect moisture status assessed using midday stem water potential (ψsmd) in pecan (Carya illinoinensis) during cyclic flood irrigations. We conducted the study simultaneously on two mature pecan orchards, one in a sandy loam (La Mancha) and the other in a clay loam (Leyendecker) soil. We were particularly interested in detecting moisture status in the −0.90 to −1.5 MPa ψsmd range because our previous studies indicated this was the critical range for irrigating pecan. Midday stem water potential, photosynthesis (A) and canopy and soil surface reflectance measurements were taken over the course of irrigation dry-down cycles at ψsmd levels of −0.40 to −0.85 MPa (well watered) and −0.9 to −1.5 MPa (water deficit). The decline in A averaged 34% in La Mancha and 25% in Leyendecker orchard when ψsmd ranged from −0.9 to −1.5 MPa. Average canopy surface reflectance of well-watered trees (ψsmd −0.4 to −0.85 MPa) was significantly higher than the same trees experiencing water deficits (ψsmd −0.9 to −1.5 MPa) within the 350- to 2500-nm bands range. Conversely, soil surface reflectance of well-watered trees was lower than water deficit trees over all bands. At both orchards, coefficient of determinations between ψsmd and all soil and canopy bands and surface reflectance indices were less than 0.62. But discriminant analysis models derived from combining soil and canopy reflectance data of well-watered and water-deficit trees had high classification accuracy (overall and cross-validation classification accuracy &gt;80%). A discriminant model that included triangular vegetation index (TVI), photochemical reflectance index (PRI), and normalized soil moisture index (NSMI) had 85% overall accuracy and 82% cross-validation accuracy at La Mancha orchard. At Leyendecker, either a discriminant model weighted with two soil bands (690 and 2430 nm) or a discriminant model that used PRI and soil band 2430 nm had an overall classification and cross-validation accuracy of 99%. In summary, the results presented here suggest that canopy and soil hyperspectral data derived from a handheld spectroradiometer hold promise for discerning the ψsmd of pecan orchards subjected to flood irrigation.

Validation of a practical normalized soil moisture model with in situ measurements in humid and semi-arid regions

Soil moisture is an important parameter that influences the exchange of water and energy fluxes between the land surface and the atmosphere. Through the simulation by a Soil–Vegetation–Atmosphere Transfer model, Carlson proposed the universal spatial information-based method to determine soil moisture that is insensitive to the initial atmospheric and surface conditions, net radiation, and atmospheric correction. In this study, a practical normalized soil moisture model is established to describe the relationship among the normalized soil moisture (M), the normalized land surface temperature (T*), and the fractional vegetation cover. The dry and wet points are determined using the surface energy balance principle, which has a robust physical basis. This method is applied to retrieve soil moisture for the Soil Moisture-Atmosphere Coupling Experiment campaign in the Walnut Creek watershed, which has a humid climate, and at the Linzestation, which has a semi-arid climate. The validation data are obtained on days of year (DOYs) 182 and 189 in 2002 in the humid region and on DOYs 148 and 180 in 2008 for the semi-arid region; these data collection days are coincident with the overpass of the Landsat Thematic Mapper/Enhanced Thematic Mapper Plus. When the estimates are compared with the in situ measurements of soil water content, the root mean square error is approximately 0.10 m3 m−3 with a bias of 0.05 m3 m−3 for the humid region and 0.08 m3 m−3 with a bias of 0.03 m3 m−3 for the semi-arid region. These results demonstrate that the practical normalized soil moisture model is applicable in both humid and semi-arid regions.

Estimation of soil moisture content from the spectral reflectance of bare soils in the 0.4-2.5 µm domain.

This work aims to compare the performance of new methods to estimate the Soil Moisture Content (SMC) of bare soils from their spectral signatures in the reflective domain (0.4–2.5 μm) in comparison with widely used spectral indices like Normalized Soil Moisture Index (NSMI) and Water Index SOIL (WISOIL). Indeed, these reference spectral indices use wavelengths located in the water vapour absorption bands and their performance are thus very sensitive to the quality of the atmospheric compensation. To reduce these limitations, two new spectral indices are proposed which wavelengths are defined using the determination matrix tool by taking into account the atmospheric transmission: Normalized Index of Nswir domain for Smc estimatiOn from Linear correlation (NINSOL) and Normalized Index of Nswir domain for Smc estimatiOn from Non linear correlation (NINSON). These spectral indices are completed by two new methods based on the global shape of the soil spectral signatures. These methods are the Inverse Soil semi-Empirical Reflectance model (ISER), using the inversion of an existing empirical soil model simulating the soil spectral reflectance according to soil moisture content for a given soil class, and the convex envelope model, linking the area between the envelope and the spectral signature to the SMC. All these methods are compared using a reference database built with 32 soil samples and composed of 190 spectral signatures with five or six soil moisture contents. Half of the database is used for the calibration stage and the remaining to evaluate the performance of the SMC estimation methods. The results show that the four new methods lead to similar or better performance than the one obtained by the reference indices. The RMSE is ranging from 3.8% to 6.2% and the coefficient of determination R2 varies between 0.74 and 0.91 with the best performance obtained with the ISER model. In a second step, simulated spectral radiances at the sensor level are used to analyse the sensitivity of these methods to the sensor spectral resolution and the water vapour content knowledge. The spectral signatures of the database are then used to simulate the signal at the top of atmosphere with a radiative transfer model and to compute the integrated incident signal representing the spectral radiance measurements of the HYMAP airborne hyperspectral instrument. The sensor radiances are then corrected from the atmosphere by an atmospheric compensation tool to retrieve the surface reflectances. The SMC estimation methods are then applied on the retrieve spectral reflectances. The adaptation of the spectral index wavelengths to the HyMap sensor spectral bands and the application of the convex envelope and ISER models to boarder spectral bands lead to an error on the SMC estimation. The best performance is then obtained with the ISER model (RMSE of 2.9% and R2 of 0.96) while the four other methods lead to quite similar RMSE (from 6.4% to 7.8%) and R2 (between 0.79 and 0.83) values. In the atmosphere compensation processing, an error on the water vapour content is introduced. The most robust methods to water vapour content variations are WISOIL, NINSON, NINSOL and ISER model. The convex envelope model and NSMI index require an accurate estimation of the water vapour content in the atmosphere.

Explicit Approximate Analytical Solution of the Horizontal Diffusion Equation

The solution of unsaturated flow is a never-ending quest for many scientists. Many methods exist with their corresponding advantages and disadvantages, such as semianalytic methods, finite difference and finite element methods, finite control volume method, and flux concentration method. This article produces an improved approximate analytical solution for nonlinear diffusion, in terms of the Boltzmann similarity variable, that has the advantages of being explicit, accurate, and relatively simple to evaluate. It is assumed that the diffusivity can be described with an exponential function, the profiles of soil water content are of finite extent, the concentration at the boundaries is constant, and the reduced flux of Philip (1973) is of the form of Vauclin and Haverkamp (1985). The proposed explicit approximate analytical solution has the Boltzmann transformation as the dependent variable and the soil water moisture as the independent variable. The solution is presented in normalized form as a function of normalized diffusivity and normalized soil moisture. It is tested with 12 soils and shows an excellent agreement with Philip’s method.

Prediction of soil organic carbon for different levels of soil moisture using Vis-NIR spectroscopy

Visible and near infrared diffuse reflectance spectroscopy has produced promising results to infer soil organic carbon (SOC) content in the laboratory. However, using soil spectra measured directly in the field or with airborne imaging spectrometers remains challenging due to uncontrolled variations in surface soil conditions, like vegetation cover, soil moisture and roughness. In particular, soil moisture may dramatically degrade predictions of SOC content when using an empirical approach. This study aims to quantify the effect of soil moisture on the accuracy of SOC predictions, and propose a method to determine SOC content for moist samples with unknown moisture content. More than 100 soil samples were collected along a transect, in the Grand-Duchy of Luxembourg. The soil samples were air-dried, moistened in steps of 0.05g water g soil−1 until saturation, and scanned in the laboratory with a visible and near infrared diffuse reflectance spectrometer. We computed the normalized soil moisture index (NSMI) to estimate the soil moisture content of the samples (R2=0.74), and used it to spectrally classify the samples according to their moisture content. SOC content was predicted using separate partial least square regressions developed on groups of samples with similar NSMI values. The root mean square error of prediction (RMSE) after validation was always below 5g C kg−1, with a ratio of prediction to deviation (RPD) greater than 2. The SOC content prediction models with a-priori knowledge of soil moisture gave similar RMSE as the ones after the NSMI classification. Hence, the NSMI might be used as a proxy of moisture content to improve SOC content prediction for spectral data acquired outside the laboratory since the method is simple and does not need other data than a simple band ratio of the spectra.

Towards an improved and more flexible representation of water stress in coupled photosynthesis–stomatal conductance models

Coupled photosynthesis–stomatal conductance ( A– g s) models are commonly used in ecosystem models to represent the exchange rate of CO 2 and H 2O between vegetation and the atmosphere. The ways these models account for water stress differ greatly among modelling schemes. This study provides insight into the impact of contrasting model configurations of water stress on the simulated leaf-level values of net photosynthesis ( A), stomatal conductance ( g s), the functional relationship among them and their ratio, the intrinsic water use efficiency ( A/ g s), as soil dries. A simple, yet versatile, normalized soil moisture dependent function was used to account for the effects of water stress on g s, on mesophyll conductance ( g m) and on the biochemical capacity. Model output was compared to leaf-level values obtained from the literature. The sensitivity analyses emphasized the necessity to combine both stomatal and non-stomatal limitations of A in coupled A– g s models to accurately capture the observed functional relationships A vs. g s and A/ g s vs. g s in response to drought. Accounting for water stress in coupled A– g s models by imposing either stomatal or biochemical limitations of A, as commonly practiced in most ecosystem models, failed to reproduce the observed functional relationship between key leaf gas exchange attributes. A quantitative limitation analysis revealed that the general pattern of C 3 photosynthetic response to water stress may be well represented in coupled A– g s models by imposing the highest limitation strength to g m, then to g s and finally to the biochemical capacity.

Surface soil moisture quantification and validation based on hyperspectral data and field measurements

Surface soil moisture information is needed for monitoring and modeling surface processes at various spatial scales. While many reflectance based soil moisture quantification models have been developed and validated in laboratories, only few were applied from remote sensing platforms and thoroughly validated in the field. This paper addresses the issues of a) quantifying surface soil moisture with very high resolution spectral measurements from remote sensors in a landscape with sandy substrates and low vegetation cover as well as b) comprehen- sively validating these results in the field. For this purpose, the recently developed Normalized Soil Moisture Index (NSMI) has been analyzed for its applicability to airborne hyperspectral remote sensing data. Three HyMap scenes from 2004 and 2005 were collected from a lignite mining area in southern Brandenburg, Germany. An NSMI model was calibrated (R 2 =0.92) and surface soil moisture maps were calculated based on this model. An in-situ surface soil mois- ture map based on a combination of Frequency Domain Reflectometry (FDR) and gravimetric data allowed for validating each image pixel (R 2 =0.82). In addition, a qualitative multitemporal comparison between two consecutive NSMI datasets from 2004 was performed and validated, showing an increase in estimated surface soil moisture corresponding with field measurements and precipitation data. The study shows that the NSMI is appropriate for modeling surface soil moisture from high spectral-resolution remote sensing data. The index leads to valid estimations of soil moisture values below field capacity in an area with sandy substrates and low vegetation cover (NDVI < 0.3). Further studies will analyze the validity of the NSMI for surface soil moisture estimation from spaceborne hyperspectral sensors like the Environmental Mapping and Analysis Program (EnMap) in different landscapes.