Moisture Model Research Articles

Spatial Distribution of Soil Moisture in Mongolia Using SMAP and MODIS Satellite Data: A Time Series Model (2010–2025)

Soil moisture is one of the essential variables of the water cycle, and plays a vital role in agriculture, water management, and land (drought) and vegetation cover change as well as climate change studies. The spatial distribution of soil moisture with high-resolution images in Mongolia has long been one of the essential issues in the remote sensing and agricultural community. In this research, we focused on the distribution of soil moisture and compared the monthly precipitation/temperature and crop yield from 2010 to 2020. In the present study, Soil Moisture Active Passive (SMAP) and Moderate Resolution Imaging Spectroradiometer (MODIS) data were used, including the MOD13A2 Normalized Difference Vegetation Index (NDVI), MOD11A2 Land Surface Temperature (LST), and precipitation/temperature monthly data from the Climate Research Unit (CRU) from 2010 to 2020 over Mongolia. Multiple linear regression methods have previously been used for soil moisture estimation, and in this study, the Autoregressive Integrated Moving Arima (ARIMA) model was used for soil moisture forecasting. The results show that the correlation was statistically significant between SM-MOD and soil moisture content (SMC) from the meteorological stations at different depths (p < 0.0001 at 0–20 cm and p < 0.005 at 0–50 cm). The correlation between SM-MOD and temperature, as represented by the correlation coefficient (r), was 0.80 and considered statistically significant (p < 0.0001). However, when SM-MOD was compared with the crop yield for each year (2010–2019), the correlation coefficient (r) was 0.84. The ARIMA (12, 1, 12) model was selected for the soil moisture time series analysis when predicting soil moisture from 2020 to 2025. The forecasting results are shown for the 95 percent confidence interval. The soil moisture estimation approach and model in our study can serve as a valuable tool for confident and convenient observations of agricultural drought for decision-makers and farmers in Mongolia.

Techno-economic uncertainty analysis of wet waste-to-biocrude via hydrothermal liquefaction

Wet waste hydrothermal liquefaction (HTL) has the potential to make economically competitive and environmentally sustainable biocrude. However, there are few studies available on the continuous-flow wet waste HTL process. Therefore, stochastic techno-economic analysis (TEA) is needed to evaluate the economic feasibility and risks of the wet waste HTL process. This work leveraged our previous uncertainty quantification work on algae HTL and in-house wet waste HTL continuous system testing data. A “component additivity” model was developed to predict HTL product yields and qualities from different wet waste compositions. With the established HTL yield model, a process reduced-order model (ROM) coupled with an economic model was built in Microsoft Excel® to replace the rigorous but computationally intensive Aspen Plus® model for uncertainty analysis. The proposed stochastic TEA approach using the ROM reduces the computational time for the analysis by 2000 times, compared to the full Aspen-based model. Monte Carlo simulation was conducted to quantify the uncertainties from feedstock composition, HTL yield model, aqueous-phase product treatment, utility consumption, and equipment sizing and costing. The stochastic TEA indicates that the MBSP for wet waste HTL ranges from $2.65/gge ($0.75/L) to $4.93/gge ($1.41/L) (10th and 90th percentiles) with a median of $3.55/gge ($1.01/L). Feed moisture, HTL reactor model, and capital investment are the main contributors to the economic uncertainty of the wet waste HTL process. Uncertainty in the MBSP could be reduced by roughly 50% if uncertainties in the feed moisture and HTL reaction yield model can be effectively controlled or decreased.

Towards hyper-resolution land-surface modeling of surface and root zone soil moisture

The goal of this work is to estimate surface and root zone soil moisture at resolutions that are useful for decision making and water resources management. A 500-m atmospheric forcing dataset is developed from the 12.5-km NLDAS-2 (North America Land Data Assimilation System) products across Oklahoma, where high-quality observations are available for validation purposes. A land surface model is then forced with three combinations of input variables to simulate surface and root zone soil moisture: 1) NLDAS-2 atmospheric forcings at their original resolution; 2) downscaled NLDAS-2 atmospheric variables (i.e., near-surface air temperature and humidity, wind speed and direction, incident longwave and shortwave radiation, pressure) and original resolution NLDAS-2 precipitation; and 3) downscaled NLDAS-2 atmospheric variables and precipitation. Results show that the third simulation is able to bring modeled standard-normal deviates of both surface and root zone soil moisture closer to in-situ observations, whereas the second simulation only shows slight improvements with respect to one forced with original resolution NLDAS-2 data. This is particularly evident for negative values of standard-normal deviates, which correspond to drier than usual cases, due to the improved ability of the downscaled precipitation to detect missed events and no-rain cases. In summary, finer resolution forcings have the potential to improve simulations of soil moisture and the resolution of precipitation plays a critical role in improving time series of soil moisture standard-normal deviates.

Study on the Freeze‐Thaw Process of the Lining Structures of a Tunnel on Qinghai‐Tibet Plateau with the Consideration of Lining Frost Damage

In seasonally frozen ground, there are many frost problems in highway road tunnel after its excavation due to the heat exchange between the cold air and lining structure inside the tunnel. To mitigate these frost‐related damages, thermal insulation layer is widely used at entrance and exit sections of the tunnel. In this study, a coupled mathematical model of heat, moisture, and stress was built for tunnels in seasonally permafrost regions. Then, based on the field‐observed air temperature inside a roadway tunnel at Altun Mountain on the Qinghai‐Tibet Plateau (QTP), seasonal freeze‐thaw process of the surrounding rocks (SR) and lining structures were numerically investigated with the consideration of insulation methods: without insulation (WTIL) and laying the insulation layer on the inner surface of the second lining structure (STIL). Combined with the principle of Miner damage accumulation, the stress regimes of the lining structures of tunnel were investigated in WTIL and STIL. The results show that there was a significantly thermal disturbance of the SR after the tunnel excavation. In the 5th year of the operation period, the maximum seasonal freeze depth (MSFD) of the SR can reach 1.6 m at the vault of the arch and that at the inverted arch was only 1.0 m due to the pavement inside the tunnel. Then, both the absolute maximum value of the maximum principal stress (MAPS) and minimum principal stress (MIPS) in cold season were bigger than those in warm season comparing the value of the stress filed of the lining structure. In the same way, both the MAPS and MIPS of the lining structure in WTIL are bigger than those in STIL in numerical simulation. The positions of the maximum tensile stress of the primary lining structure in STIL and WTIL were inverted arch. For the lining structures, the greater tensile stress was generally harmful. Thus, the inverted arch of the tunnel should be laid on the insulation layer.

Evaluation of CropSyst model for soil moisture and water productivity of wheat

Evaluation of CropSyst model for soil moisture and water productivity of wheat

Accelerated shelf life testing of mocatilla chip using critical moisture content approach and models of sorption Isotherms

Mocatilla is a tortilla chip made from composite flour consists of modified cassava, corn, and rice flour which is enriched by tempeh. This study aimed to estimate the shelf life of mocatilla chips when packed in polyethylene terephthalate (PET), polypropylene (PP), and aluminum foil (alufo) using the Accelerated Shelf Life Testing (ASLT) method by the critical moisture content approach. The study was conducted by determining the initial, critical, and equilibrium moisture content of the sample by thermogravimetry. Six models of moisture sorption isotherm were fitted to determine the equilibrium moisture content using six salts with relative humidity (RH) values at a range of 0.076-0.903. The result showed that the initial and critical moisture content of mocatilla chips (g H2O/g solid) was 0.0141 and 0.0532, respectively. Meanwhile the equilibrium moisture content at relative humidity 80% and temperature 30°C was 0.1471. The Guggenheim Anderson de Boer (GAB) equation was the most suitable model for describing the sorption isotherm phenomenon of mocatilla chips. Based on Labuza’s equation, the shelf life prediction of mocatilla chips (80% RH, 30°C) packed in PET, PP, and alufo was 3, 8, and 62 months, respectively.

A meso-stochastic research on the chloride transport in unsaturated concrete

Based on the transport model of moisture, this paper establishes a prediction model of chloride transport in unsaturated concrete. For the moisture transport model, different water diffusion coefficients are assigned to the drying and wetting processes respectively. Based on considering the interaction between the transport of moisture and chloride ions in concrete and multi-factors influence on chloride diffusion coefficient, the constitutive equation of chloride diffusion-convection in unsaturated concrete is built. Two groups of tests of concrete specimens under drying and wetting cycles with different time ratios of drying-wetting are carried out and verify the rationality of the prediction model. Furthermore, a two-dimensional concrete model based on finite element method is developed to investigate the chloride transport in unsaturated concrete on meso-level. The simulation results show that the random distribution of aggregates could have a significant influence on the chloride transport. Also, the randomness and inhomogeneity of chloride concentration distribution are well reflected in the meso-scale concrete model under the action of drying and wetting cycles.

Simulation of walnut drying under hot air heating using a nonequilibrium multiphase transfer model

A nonequilibrium multiphase moisture and heat transfer model was developed to simulate the drying of a single walnut under hot air heating. The walnut was modeled as a 3-layer sphere by differentiating the shell, kernel, and an air gap between them. Drying characteristics of walnuts at 43, 55, 65, and 75 °C under 1.4 m/s air velocity were simulated using finite element method. The simulation results agreed well with experimental data, and the model showed good predictability for drying of walnuts with different initial moisture contents and dimension characteristics (R adj 3 > 0.996 for moisture and R adj 3 > 0.983 for temperature). The spatial distributions of liquid moisture, water vapor, and temperature in the walnut were determined through model simulation, which provided important information for understanding the mechanisms of heat and moisture transfer during walnut drying process. Findings from this study established theoretical basis for improving the walnut drying process and determining the suitable drying conditions.

Neural network soil moisture model for irrigation scheduling

Real-time irrigation scheduling systems attempt to eliminate crop water stress and achieve a high yield at harvest through the control of soil moisture. Artificial intelligence algorithms are possibly to learn the soil moisture dynamics in the soil-plant-atmosphere system and then are embedded into a low-cost controller to generate appropriate irrigation schedules. In this study, a neural network (NN) model was proposed to learn from a process-based agricultural systems model, the Root Zone Water Quality Model (RZWQM2) in predicting the root zone soil moisture during the crop growing season. Climatic data, rooting depth and hesternal soil moisture are set as inputs of the NN model to predict intraday soil moisture in different layers. Conditions with and without water supply are modeled separately to achieve a higher accuracy. The NN-based irrigation scheduling method (NN method), triggers irrigation when the predicted soil moisture drops to a level defined by the product of management-allowed depletion multiplied by the depth of available water to plant. Irrigation quantity was set to replenish the root zone soil water content to field capacity. NN method was compared with the reported water stress (WS) method based on RZWQM2. The results reveal that though the constructed NN model well-predicted soil moisture changes during the main crop season with minor errors, but the error was larger at lower soil moisture thereby decreased the scheduling efficiency. An NN ensemble model was tested and shown to improve the precision and robustness of soil moisture prediction as well as the scheduling performance in view of water conservation and yield maintenance. Combined with the NN ensemble model and adjusted lowest soil moisture for triggering irrigations, the NN ensemble-based irrigation scheduling method achieved a performance not better than that of the RZWQM2-WS method but exceeded the method based on evapotranspiration and water balance by up to 20%. The constructed NN ensemble model and NN ensemble-based irrigation scheduling method could act as an alternative to predicting soil moisture and obtaining efficient irrigation scheduling.

Effect of sample presentation on the near infrared spectra of wild harvest Kakadu plum fruits (Terminalia ferdinandiana)

Consumer awareness of the health value of native and tropical fruits and its products, is increasing, where demands for new applications are driving food industry to find novel applications of these fruits as functional ingredients. Kakadu plum fruit (Terminalia ferdinandiana) is endemic to Australia and has been used as a functional food or ingredient. This fruit is sold as whole fruit, puree or powder. Critical to the commercialization of this fruit is the development and use of methods and tools to better monitor and understand the quality of these products throughout the value chain. The aim of this study was to evaluate four scanning positions used to collect near infrared (NIR) spectra of Kakadu plum fruits wild harvested, in order to develop a rapid and high throughput method to monitor fruit chemical composition. A portable NIR instrument (Micro-NIR, Viavi, Milpitas, CA, USA) was used to collect the spectra of whole fruit (four positions) samples from the Kimberley region in Western Australia. The NIR data was analysed using principal component analysis (PCA) and partial least squares (PLS) regression models for moisture and total soluble solids. The results showed that the scanning position affected the performance of the models (standard error of cross validation). It was concluded that NIR spectroscopy is a promising method to assure the integrity of the value chain of these products. However, a more robust protocol must be defined when whole fruit are analysed.

Retrieving Heterogeneous Surface Soil Moisture at 100 m Across the Globe via Fusion of Remote Sensing and Land Surface Parameters

Successful monitoring of soil moisture dynamics at high spatio-temporal resolutions globally is hampered by the heterogeneity of soil hydraulic properties in space and complex interactions between water and the environmental variables that control it. Current soil moisture monitoring schemes via in situ station networks are sparsely distributed while remote sensing satellite soil moisture maps have a very coarse spatial resolution. In this study, an empirical surface soil moisture (SSM) model was established via fusion of in situ continental and regional scale soil moisture networks, remote sensing data (SMAP and Sentinel-1) and high-resolution land surface parameters (e.g. soil texture, terrain) using a quantile random forest (QRF) algorithm. The model had a spatial resolution of 100 m and performed well under cultivated, herbaceous, forest, and shrub soils (overall R2 = 0.524, RMSE = 0.07 m3 m-3). It has a relatively good transferability at the regional scale among different soil moisture networks (mean RMSE = 0.08–0.10 m3 m-3). The global model was applied to map SSM dynamics at 30–100 m across a field-scale soil moisture network (TERENO-Wüstebach) and an 80-ha cultivated cropland in Wisconsin, USA. Without the use of local training data, the model was able to delineate the variations in SSM at the field scale but contained large bias. With the addition of 10% local training datasets (“spiking”), the bias of the model was significantly reduced. The QRF model was relatively insensitive to the resolution of Sentinel-1 data but was affected by the resolution and accuracy of soil maps. It was concluded that the empirical model has the potential to be applied elsewhere across the globe to map SSM at the regional to field scales for research and applications. Future research is required to improve the performance of the model by incorporating more field-scale soil moisture sensor networks and assimilation with process-based models.

Standardization of model for the production of spray dried whey protein-zinc complex and its acceptability in milk

Zinc (Zn) is important in maintaining metabolic condition of human health and its deficiency increases the prevalence of diarrhea especially in children. Novel Zn fortificants such as carbohydrate/protein-Zn chelates, nano-Zn oxides, etc., enhance the solubility of Zn under various processing conditions. In the present study, the conditions for the production of spray dried whey protein concentrate-Zn complex (WP–Zn) were optimized using combinations of variables like inlet air temperature, feed flow rate and total solids and responses like moisture, yield and solubility by adopting Central Composite Response Design (CCRD). The models for moisture, solubility and yield were statistically significant. Optimized conditions comprised of 200 °C inlet temperature, 3 mL/min flow rate and 14.4% total solids. Milk fortified with WP-Zn (@35 ppm Zn) showed similar sensory acceptability with control (unfortified milk). Physicochemical properties (pH, acidity and viscosity) of fortified milk showed non-significant differences with control.

Evaluation of the soil moisture model with data assimilation by the triple collocation method

This paper deals with a nonlinear soil moisture transport problem, solved with addition of satellite observed soil moisture. The satellite data are assimilated into the model using Newtonian nudging method. Evaluation is done by the triple collocation method, which involves three independent data sources: model results, ground stations and ERA5 climatic data. The results testify that model results are nearly as accurate as the ground station measurements.

Prediction and Analysis of Strawberry Moisture Content based on BP Neural Network Model

Background: Moisture content is one of the most important indicators for the quality of fresh strawberries. Currently, several methods are usually employed to detect the moisture content in strawberry. However, these methods are relatively simple and can only be used to detect the moisture content of single samples but not batches of samples. Besides, the integrity of the samples may be destroyed. Therefore, it is important to develop a simple and efficient prediction method for strawberry moisture to facilitate the market circulation of strawberry. Objective: This study aims to establish a novel BP neural network prediction model to predict and analyze strawberry moisture. Methods: Toyonoka and Jingyao strawberries were taken as the research objects. The hyperspectral technology, spectral difference analysis, correlation coefficient method, principal component analysis and artificial neural network technology were combined to predict the moisture content of strawberry. Results: The characteristic wavelengths were highly correlated with the strawberry moisture content. The stability and prediction effect of the BP neural network prediction model based on characteristic wavelengths are superior to those of the prediction model based on principal components, and the correlation coefficients of the calibration set for Toyonaka and Jingyao respectively reached up to 0.9532 and 0.9846 with low levels of standard deviations (0.3204 and 0.3010, respectively). Conclusion: The BP neural network prediction model of strawberry moisture has certain practicability and can provide some reference for the on-line and non-destructive detection of fruits and vegetables.

Remote sensing of the impact of flash drought events on terrestrial carbon dynamics over China

BackgroundFlash drought poses a great threat to terrestrial ecosystems and influences carbon dynamics due to its unusually rapid onset and increasing frequency in a warming climate. Understanding the response of regional terrestrial carbon dynamics to flash drought requires long-term observations of carbon fluxes and soil moisture at a large scale. Here, MODIS satellite observations of ecosystem productivity and ERA5 reanalysis modeling of soil moisture are used to detect the response of ecosystems to flash drought over China.ResultsThe results show that GPP, NPP, and LAI respond to 79–86% of the flash drought events over China, with highest and lowest response frequency for NPP and LAI, respectively. The discrepancies in the response of GPP, NPP, and LAI to flash drought result from vegetation physiological and structural changes. The negative anomalies of GPP, NPP, and LAI occur within 19 days after the start of flash drought, with the fastest response occurring over North China, and slower responses in southern and northeastern China. Water use efficiency (WUE) is increased in most regions of China except for western regions during flash drought, illustrating the resilience of ecosystems to rapid changes in soil moisture conditions.ConclusionsThis study shows the rapid response of ecosystems to flash drought based on remote-sensing observations, especially for northern China with semiarid climates. Besides, NPP is more sensitive than GPP and LAI to flash drought under the influence of vegetation respiration and physiological regulations. Although the mean WUE increases during flash drought over most of China, western China shows less resilience to flash drought with little changes in WUE during the recovery stage. This study highlights the impacts of flash drought on ecosystems and the necessity to monitor rapid drought intensification.

Measurement and zonation of soil surface moisture in arid and semi-arid regions using Landsat 8 images

Monitoring of soil surface moisture is an imperative factor in water and energy cycle. Due to the variability of soil characteristics such as topography, vegetation, and climate dynamics, this important factor varies with respect to time and place. Measuring methods can provide soil moisture information in a wide range of short intervals with reasonable accuracy. In present research, Landsat 8 satellite data with various soil moisture content estimation methods were tested. In order to evaluate the accuracy of each method, the real-field data used 80 samples of volumetric soil moisture content in suburban areas of Semnan city that were collected at the time of satellite passage of the area. Some of the indicators used in this study are normalized vegetation index, NDTI index, NDMI index, PSMI index (use full form of these indices), surface temperature, and SMSWIR index. The SMSWIR index with correlation coefficient was 0.78, and the correlation coefficient of regression model was 0.61, and RMSE was 3.69. The results of the regression model and real data were estimated to be 3.69, which are recommended for assessing surface soil moisture in arid and desert regions. Three indicators of SMSWIR index, NDTI index, and NDMI index with a small difference are not suitable indices for measuring soil moisture content in desert areas with vegetation cover. By employing multivariable regression models, soil moisture model was also prepared by using the studied indices. The findings of this research indicate that the simultaneous correlation model is superior to the surface soil moisture mapping.

On the Radiative Transfer Model for Soil Moisture across Space, Time and Hydro-Climates

A framework is proposed for understanding the efficacy of the microwave radiative transfer model (RTM) of soil moisture with different support scales, seasonality (time), hydroclimates, and aggregation (scaling) methods. In this paper, the sensitivity of brightness temperature TB (H- and V-polarization) to physical variables (soil moisture, soil texture, surface roughness, surface temperature, and vegetation characteristics) is studied. Our results indicate that the sensitivity of brightness temperature (V- or H-polarization) is determined by the upscaling method and heterogeneity observed in the physical variables. Under higher heterogeneity, the TB sensitivity to vegetation and roughness followed a logarithmic function with an increasing support scale, while an exponential function is followed under lower heterogeneity. Surface temperature always followed an exponential function under all conditions. The sensitivity of TB at H- or V- polarization to soil and vegetation characteristics varied with the spatial scale (extent and support) and the amount of biomass observed. Thus, choosing an H- or V-polarization algorithm for soil moisture retrieval is a tradeoff between support scales, and land surface heterogeneity. For largely undisturbed natural environments such as SGP’97 and SMEX04, the sensitivity of TB to variables remains nearly uniform and is not influenced by extent, support scales, or an upscaling method. On the contrary, for anthropogenically-manipulated environments such as SMEX02 and SMAPVEX12, the sensitivity to variables is highly influenced by the distribution of land surface heterogeneity and upscaling methods.

Spatio-temporal soil drying in southeastern South America: the importance of effective sampling frequency and observational errors on drydown time scale estimates

ABSTRACT The study of the spatio-temporal dynamics of surface soil moisture (SSM) drydowns integrates the soil response to climatic conditions, drainage and land cover and is key to advances in our knowledge of the soil–atmosphere water exchange. SSM drydowns have also been employed to compare soil moisture spatio-temporal behaviour between different data sources such as satellite-derived data and land–surface models, difficult to compare with standard methodologies. However, the errors introduced by satellite effective sampling frequencies (SF) and by different methodologies employed to define a drydown period have until now not been properly addressed in the literature. Here, SSM from microwave remote sensing products operating at L, C and X frequency bands are analysed together with SSM from a land–surface model in southeastern South America during 2010–2014, at seasonal and annual scales. We use an SSM-based drydown detection methodology and an exponential model to estimate the drydown time scale. The errors generated by the SF and by using SSM instead of precipitation to define the start of the drydown period are examined using a synthetic soil moisture model. Most of the products can detect the negative correlation between aridity conditions and drydown time scales (faster soil drying in the semiarid west and slower – and noisier – towards the wetter east). The Soil Moisture Ocean Salinity (SMOS) L-band product reproduces the smoothest drydown time scale spatial patterns at the annual and seasonal scales and displays large seasonal contrasts, although its error due to SF is the highest among the three products. The Organizing Carbon Hydrology In Dynamic Ecosystems (ORCHIDEE) land–surface model resampled by the SF of each product shows better agreement with SMOS, followed by the X-band product. The agreement is higher over the southern Pampas Plains, a region with high coverage of satellite-derived data and flat topography. SSM observational errors generate higher relative uncertainties for drydown time scales longer than 8 days, while the SF is more relevant for shorter drydowns. Also, the SF has a larger impact than soil depth, particularly in the dry season, when sparse temporal coverage misses short drydowns. Soil texture influence is captured by SMOS and ORCHIDEE, revealing slower drydowns for finer textures at the annual scale. Our results show that the soil drying behaviour is comparable between microwave remote sensing products and a land–surface model and that the observational errors and the SF are important sources of uncertainty to consider when interpreting drydown results.

Limitation of soil moisture on the response of transpiration to vapor pressure deficit in a subtropical coniferous plantation subjected to seasonal drought

Plant transpiration accounts for >60% of evapotranspiration and is affected by drought in various ways. Seasonal droughts are frequent in humid region, and co-occur with increasing atmospheric vapor pressure deficit (VPD) and decreasing soil moisture. Independent effects of atmospheric water demand and soil water supply on transpiration are still unclear. In this study, plant transpiration and environmental factors were monitored from April 2012 through December 2017 in a subtropical coniferous plantation, where low precipitation and high temperature occurred in summer and autumn. Our results confirmed the nonlinear relationship between atmospheric VPD and soil moisture and transpiration. A multiplicative model of atmospheric VPD and soil moisture on plant transpiration was developed to quantify their relative contributions to plant transpiration. The response of plant transpiration to atmospheric VPD or soil moisture followed an exponential function when they neither factor was limiting. Throughout the study period, atmospheric VPD and soil moisture explained on average 90.8% and 9.2% of the limitation on plant transpiration, respectively. While seasonal drought occurred during July to October, monthly mean contribution of soil moisture limitation to plant transpiration was 17.8 ± 6.7% with a maximum daily contribution of 86.8%. Our results showed that transpiration in this subtropical coniferous plantation would be further limited by soil moisture in conditions of future climate change.

Evaluating the characteristics of soil vis-NIR spectra after the removal of moisture effect using external parameter orthogonalization

Timely and accurate monitoring of specific soil properties is essential. Visible and near-infrared (vis-NIR) reflectance spectroscopy has been suggested as a powerful tool to estimate soil properties, but the accuracy of soil property estimates can be strongly affected by soil moisture. External parameter orthogonalization (EPO) has been proposed to be an effective method to alleviate the effects of soil moisture on soil property estimation. The EPO algorithm decomposes the spectra into the useful (containing soil characteristics) part and the external part (affected by moisture). Most studies have focused on the useful part of spectra which contain soil information. However, it remains unclear whether the EPO process could also remove other useful soil information. Thus, the objectives of this study were to investigate the shape of the external part of the spectra, verify the relationship between soil moisture content and the external part, test whether other soil factors (e.g., soil texture) were removed by the EPO process, and propose a model for soil moisture using the spectra angle (EPO-SA) method. Soil samples with various soil moisture contents were analyzed. The results showed that the spectra of the external part, after the EPO process, were mainly correlated with the soil moisture content, with absolute correlation coefficients (|r|) greater than 0.8. These external spectra were not related to inherent soil properties, such as soil organic matter and clay content, with absolute correlation coefficients of less than 0.2. Furthermore, we proposed the EPO-SA method, which can be used to quantitatively predict the soil moisture content (R2 = 0.77, RMSE = 0.09 g kg−1). This approach clarified that useful soil spectral information was not removed by EPO, and it also proved the effectiveness of the EPO algorithm. Additionally, it is also demonstrated that the external spectra are useful for predicting the external factors since they are highly correlated with soil moisture.