Soil Moisture Estimation Model Research Articles

Deep automatic soil roughness estimation from digital images

ABSTRACT Soil roughness, defined as the irregularities of the soil surface, yields significant information about soil water storage, infiltration and overland flow and, thus, is a key factor in characterizing the quality of the terrain; it is often used as input in many synthetic general agricultural models and in particular in soil moisture estimation models. In this paper, we propose a framework that combines a specific setup for data acquisition with deep convolutional networks for actual estimation. The former relies on projecting a line red laser beam on the analysed soil surface followed by digital color image acquisition. The later, involves two convolutional models that are trained in a supervised manner to predict the soil roughness. The data set was produced in the laboratory both on synthetic and real soil samples. The labels used in the training process are the soil roughness values measured by using a pinboard. The detailed evaluation showed that the error of the automatic precision lies in the range of ground truth deviation, thus validating the proposed procedure.

Watershed scale soil moisture estimation model using machine learning and remote sensing in a data-scarce context

Soil moisture content can be used to predict drought impact on agricultural yield better than precipitation. Remote sensing is viable source of soil moisture data in instrument-scarce areas. However, space-based soil moisture estimates lack suitability for daily and high-resolution agricultural, hydrological, and environmental applications. This study aimed to assess the potential of the random forest machine learning technique to enhance the spatial resolution of remote soil moisture products from the SMAP satellite. Models were built using random forest for spatial downscaling of SMAP-L3-E, then visually and statistically evaluated for disaggregation quality. The impact of topography, soil properties, and precipitation on the downscaled soil moisture was examined. The relationship between downscaled soil moisture and in-situ soil moisture was analyzed. The results indicate that the proposed method demonstrated spatial and hydrological coherence, along with a satisfactory downscaling quality. Statistical validation indicated suitable generalization error for scientific and practical use (RMSE < 0.05 cm3 cm-3). Random forest effectively achieved spatial downscaling of SMAP-L3-E in the study area. Principal component and spatial analysis revealed dependence of downscaled soil moisture on elevation, soil organic carbon content, clay content, and saturated hydraulic conductivity, mainly under near-saturation conditions. Regarding validation against in-situ data, downscaled soil moisture explained in-situ soil moisture well under low soil water content ( = 0.624). Downscaling performance deteriorates for water contents between 0.40 to 0.50 cm3 cm-3, suggesting inadequacy under near saturation conditions at a daily temporal frequency. However, coarser temporal aggregations (7 to 10 days) yielded an average 0.98 correlation coefficient, regardless of saturation conditions. These results could potentially be applied in irrigation planning, soil physics studies and hydrology monitoring, to forecasting the occurrence of droughts, leaching of contaminants, surface runoff modeling, carbon cycle studies, soil's capacity to store and provide nutrients.

A Forward Model and Inversion Algorithm for Near-Surface Soil Moisture Estimation With GNSS Refraction Pattern Technique

A Forward Model and Inversion Algorithm for Near-Surface Soil Moisture Estimation With GNSS Refraction Pattern Technique

Estimation of Soil Moisture for Different Crops Using SAR Polarimetric Data

Soil moisture is an essential factor that influences agricultural productivity and hydrological processes. Soil moisture estimation using field detection methods takes time and is challenging. However, using Remote Sensing (RS) and Geographic Information System (GIS) technology, soil moisture parameters become easier to detect. In microwave remote sensing, synthetic aperture radar (SAR) data helps to retrieve soil moisture from more considerable depths because of its high penetration capability and the illumination power of its light source. This study aims to process the SAR Sentinel-1A data and estimate soil moisture using the Water Cloud Model (WCM). Many physical and empirical models have been developed to determine soil moisture from microwave remote sensing platforms. However, the Water Cloud Model gives more accurate results. In this study, the WCM model is used for mixed crop types. The experimental soil moisture was determined from in-situ soil samples collected from various agricultural areas. The soil backscattering values corresponding to the different soil sampling locations were derived from Sentinel SAR data. Using linear regression analysis, the laboratory's soil moisture results and soil backscattering values were correlated to arrive at a model. The model was validated using a secondary set of in-situ moisture content values taken during the same period. The R2 and RMSE of the model were observed to be 0.825 and 0.0274, respectively, proving a strong correlation between the experimental soil moisture and satellite-derived soil moisture for mixed crop field types. This paper explains the methodology for arriving at a model for soil moisture estimation. This model helps to recommend suitable crop types in large, complex areas based on predicted moisture content. Doi: 10.28991/CEJ-2023-09-06-08 Full Text: PDF

Field-scale evaluation of remote sensing soil moisture retrievals using a multi-satellite approach

ABSTRACT Nine remote sensing-based surface soil moisture (SSM) estimation models using images from Landsat 8, Sentinel-2 and Sentinel-1 satellites were compared. To evaluate these models, we measured SSM at 179 locations in a 50-ha sunflower field . The result showed that the Water Cloud-based model, a semi-empirical regression model, which used the synergy of Landsat 8 and Sentinel-1 data, was the best model, with an R 2 of 0.73 and RMSE of 0.053 m3/m3. In sum, with the integration of images from multiple satellites, soil moisture maps with suitable spatial resolutions were retrieved that may be used for irrigation planning.

Soil-Moisture Estimation Based on Multiple-Source Remote-Sensing Images

Soil moisture plays a significant role in the global hydrological cycle, which is an important component of soil parameterization. Remote sensing is one of the most important methods used to estimate soil moisture. In this study, we developed a new nonlinear Erf-BP neural network method to establish a soil-moisture-content-estimation model with integrated multiple-resource remote-sensing data from high-resolution, hyperspectral and microwave sensors. Next, we compared the result with the single-resource remote-sensing data for SMC (soil-moisture content) estimation models by using the linear-fitting method. The results showed that the soil-moisture estimation model offers better accuracy by using multiple-resource remote-sensing data. Furthermore, the SMC predicted the results by using the new Erf-BP neural network with multiple-resource remote-sensing data and a good overall correlation coefficient of 0.6838. Compared with the linear model’s estimation results, the accuracy of the SMC estimation using the Erf-BP method was increased, and the RMSE decreased from 0.017 g/g to 0.0146 g/g, a decrease of 16.44%. These results also indicate that the improved algorithm of the Erf-BP artificial neural network has better fitting results and precision. This research provides a reference for multiple-resource remote-sensing data for soil-moisture estimation.

Daily Spatial Complete Soil Moisture Mapping Over Southeast China Using CYGNSS and MODIS Data.

Daily spatial complete soil moisture (SM) mapping is important for climatic, hydrological, and agricultural applications. The Cyclone Global Navigation Satellite System (CYGNSS) is the first constellation that utilizes the L band signal transmitted by the Global Navigation Satellite System (GNSS) satellites to measure SM. Since the CYGNSS points are discontinuously distributed with a relativity low density, limiting it to map continuous SM distributions with high accuracy. The Moderate-Resolution Imaging Spectroradiometer (MODIS) product (i.e., vegetation index [VI] and land surface temperature [LST]) provides more surface SM information than other optical remote sensing data with a relatively high spatial resolution. This study proposes a point-surface fusion method to fuse the CYGNSS and MODIS data for daily spatial complete SM retrieval. First, for CYGNSS data, the surface reflectivity (SR) is proposed as a proxy to evaluate its ability to estimate daily SM. Second, the LST output from the China Meteorological Administration Land Data Assimilation System (CLDAS, 0.0625° × 0.0625°) and MODIS LST (1 × 1 km) are fused to generate spatial complete and temporally continuous LST maps. An Enhanced Normalized Vegetation Supply Water Index (E-NVSWI) model is proposed to estimate SM derived from MODIS data at high spatial resolution. Finally, the final SM estimation model is constructed from the back-propagation artificial neural network (BP-ANN) fusing the CYGNSS point, E-NWSVI data, and ancillary data, and applied to get the daily continuous SM result over southeast China. The results show that the estimation SM are comparable and promising (R = 0.723, root mean squared error [RMSE] = 0.062 m3 m−3, and MAE = 0.040 m3 m−3 vs. in situ, R = 0.714, RMSE = 0.057 m3 m−3, and MAE = 0.039 m3 m−3 vs. CLDAS). The proposed algorithm contributes from two aspects: (1) validates the CYGNSS derived SM by taking advantage of the dense in situ networks over Southeast China; (2) provides a point-surface fusion model to combine the usage of CYGNSS and MODIS to generate the temporal and spatial complete SM. The proposed approach reveals significant potential to map daily spatial complete SM using CYGNSS and MODIS data at a regional scale.

Soil Moisture Estimation for Winter-Wheat Waterlogging Monitoring by Assimilating Remote Sensing Inversion Data into the Distributed Hydrology Soil Vegetation Model

Waterlogging crop disasters are caused by continuous and excessive soil water in the upper layer of soil. In order to enable waterlogging monitoring, it is important to collect continuous and accurate soil moisture data. The distributed hydrology soil vegetation model (DHSVM) is selected as the basic hydrological model for soil moisture estimation and winter-wheat waterlogging monitoring. To handle the error accumulation of the DHSVM and the poor continuity of remote sensing (RS) inversion data, an agro-hydrological model that assimilates RS inversion data into the DHSVM is used for winter-wheat waterlogging monitoring. The soil moisture content maps retrieved from satellite images are assimilated into the DHSVM by the successive correction method. Moreover, in order to reduce the modeling error accumulation, monthly and real-time RS inversion maps that truly reflect local soil moisture distributions are regularly assimilated into the agro-hydrological modeling process each month. The results show that the root mean square errors (RMSEs) of the simulated soil moisture value at two in situ experiment points were 0.02077 and 0.02383, respectively, which were 9.96% and 12.02% of the measured value. From the accurate and continuous soil moisture results based on the agro-hydrological assimilation model, the waterlogging-damaged ratio and grade distribution information for winter-wheat waterlogging were extracted. The results indicate that there were almost no high-damaged-ratio and severe waterlogging damage areas in Lixin County, which was consistent with the local field investigation.

Fusion of Reflected GPS Signals With Multispectral Imagery to Estimate Soil Moisture at Subfield Scale From Small UAS Platforms

This study proposes a low-cost and &#x201C;proof-of-concept&#x201D; methodology to obtain high spatial resolution soil moisture (SM) via processing reflected Global Positioning System (GPS) and a multispectral camera data acquired by small Unmanned Aircraft System (UAS) platforms. An SM estimation model is developed using a random forest (RF) machine-learning (ML) algorithm by combining features obtained from reflected GPS signals (collected by smartphones and commercial off the shelf receivers) in conjunction with ancillary vegetation indices from the multispectral camera data. The proposed ML algorithm uses <i>in-situ</i> SM measurements acquired via SM probes as labels. A preliminary field experiment was conducted on 210&#x00A0;m by 110&#x00A0;m (2.31 ha) crop fields (corn and cotton) in 2020 (from January to November, including crop planting through senescence time period) at Mississippi State University (MSU)&#x0027;s the heavily instrumented North Farm to acquire data needed for the ML model to train and test. Our results showed that both fields could be covered by GPS reflectometry measurements with about 13 minutes of flight time at a 15-m altitude, and SM can be mapped with 5m&#x00A0;&#x00D7;&#x00A0;5m spatial resolution (corresponding to the elongated first Fresnel zone). The model is trained with and validated against eight <i>in-situ</i> SM station datasets via 10-fold and leave-one-probe-out cross-validation techniques. Overall root-mean-square errors (RMSE) of 0.013 m <inline-formula><tex-math notation="LaTeX">$^{3}$</tex-math></inline-formula> m<inline-formula><tex-math notation="LaTeX">$^{-3}$</tex-math></inline-formula> volumetric SM and R-value of 0.95 [-] are obtained for 10-fold cross-validation. The proposed model reached an RMSE of 0.033 m <inline-formula><tex-math notation="LaTeX">$^{3}$</tex-math></inline-formula> m<inline-formula><tex-math notation="LaTeX">$^{-3}$</tex-math></inline-formula> and an R-value of 0.5 [-] in leave-one-probe-out cross-validation. While having limited data, the results indicate that high resolution SM measurement can be achieved with a low-cost GPS reflectometry system onboard a small UAS platform for use in precision agriculture (PA) applications.

Comparisons of distributed and lumped rainfall-runoff model for soil moisture estimation

Soil moisture is an important parameter in landslides because of increased pore pressure and decreased shear strength. This research aims to derive soil moisture indicators from two hydrological models: the physically-based distributed hydrological model and the lumped model. Rainfall-Runoff-Inundation (RRI) Model is used to simulate the hydrological response of catchments to the rainfall-induced landslide in a distributed manner. Tank Model as a lumped hydrological model is also used in this study to simulate the dynamic of soil moisture. The study area is the upper Brantas River Basin, prone to landslides due to heavy rainfall and steep slope. Calibration of the model is conducted by tuning the model according to the river discharge data. The simulation indicates that acceptable performance is confirmed. Tank Model can provide the dynamic of the soil moisture. However, by using this approach, the spatial variation of the soil moisture cannot be presented. Regarding the quantitative amount of soil water content, RRI Model could make a reasonable simulation though the temporal variation is not adequately reproduced. Validation of this method with satellite soil moisture as well as ground measurement is also presented. The challenges of using these approaches to develop landslide hazard assessment are discussed.

Development of an empirical model for sub-surface soil moisture estimation and variability assessment in a lesser Himalayan watershed

Development of an empirical model for sub-surface soil moisture estimation and variability assessment in a lesser Himalayan watershed

Methodology for determining the physical parameters of ground plane by the results of the optical and radar data fusion

The methodology of multispectral optical and dual-polarized radar satellite data fusion for soils physical parameters estimation is developed. In particular, the methodology comprises relative permittivity estimation based on the Integral Equation Model (IEM) by using high resolution Sentinel-1 GRDH radar data. The calibration of ε was provided based on the compensation of soil acidity and temperature destabilizing effects. High-resolution multispectral images PlanetScope were used for vegetation indices and thermal emissivity estimation. Both, low-resolution MODIS and medium resolution Landsat-7/8 ETM+/TIRS thermal infrared images were processed in order to estimate ground plane thermodynamic temperature. An investigated approach for the radar signal depolarization compensation is based on local signal deviations and surface roughness estimation. The relief heterogeneity is restored based on the medium-resolution digital terrain elevation model ALOS AWD3D. Aiming to evaluate the accuracy of a soil moisture estimation model designed based on the presented methodology, ground truth measurements were carried out. Specifically, they included soil samples retrieving for the gravimetric soil moisture. In addition, the soil acidity and temperature were measured by applying the GM1312 differential thermometer and WALCOM multifunction device. The estimated parameters and ground truth data were used in order to retrieve the soil moisture based on the multivatiative regression dependence. Root mean square error of soil moisture retrieving was estimated as 4,73 %. Such accuracy is completely acceptable for the soil moisture monitoring of natural-reserved fund territories

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 &lt; 0.0001 at 0–20 cm and p &lt; 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 &lt; 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.

Estimation of Surface Moisture Content using Sentinel-1 C-band SAR Data Through Machine Learning Models

Monitoring the spatio-temporal variation in soil moisture content (SMC) of the surface soil layer is essential for agriculture and water resource management activities, especially in regions where the socio-economic condition and livelihood depend upon agriculture and allied sectors. In the present study, we have compared different machine learning (ML) and linear regression models to estimate the SMC integrating field observed soil moisture and Sentinel-1 SAR data. Total 56 soil samples were collected from the surface soil layer (~ 5 cm) in correspondence with the passing date of the Sentinel-1 sensor over the study area. The surface SMC was estimated for bare soil areas, which was extracted by applying the threshold values on vegetation and water index maps derived from the Sentinel-2 multispectral data. The univariate linear regression with the co-polarized VV band provided higher accuracy compared to the cross-polarized VH band. However, the multiple linear regression with VV and VH bands indicated similar accuracy as obtained by the VV band alone. The random forest model was observed as the best performing ML model for soil moisture estimation (R2 = 0.87 and 0.93 during modeling and validation, respectively; RMSE: ~ 0.03). The obtained results indicate well accurate surface soil moisture verified with in-situ information collected during the dry rabi crop season (January to March 2019). The maximum SMC was observed for March, followed by February and January, that corroborated with the total monthly precipitation and irrigation activities. The study highlights the potentiality of ML models and Sentinel-1 SAR data for soil moisture estimation, which is useful for policy-level implications and decision making in agriculture and water resource management activities.

Evaluation of the Performance of the Integration of Remote Sensing and Noah Hydrologic Model for Soil Moisture Estimation in Hetao Irrigation Region of Inner Mongolia

As an important parameter in Land surface system research, surface soil moisture (SSM) links the surface water and groundwater that plays a key role in water resources, agricultural management and global warming studies. Remote sensing techniques provide a direct and convenient means to estimate SSM on a regional scale. In this study, the performance of the normalized land surface temperature-vegetation index (LST-VI) model was evaluated using the in situ soil moisture measurements at Hetao irrigation region of Inner Mongolia that is a representative semi-arid area with relatively uniform underlying surface. The model was used to estimate soil moisture from HJ-1B and Landsat 8 images on clear days in 2014–2017. The overall SSM estimation accuracy was high, and the average RMSE was approximately 0.04 m3/m3. Moreover, a systematic sensitivity analysis was conducted for the input parameters and other impact factors. The results demonstrated that the model could credibly monitor the regional surface soil water content.

Comparison of two satellite-based soil moisture reconstruction algorithms: A case study in the state of Oklahoma, USA

Soil moisture plays a critical role in regional water cycles and is one of the key indicators for agricultural droughts. Satellite-based microwave radiometers have been the main instrument for mapping surface soil moisture at regional to global scales. However, known issues still exist that lead to data gaps in microwave-based remotely sensed soil moisture products. Multiple gap-filling approaches have been recently developed to generate spatially continuous satellite-based soil moisture. However, there are few inter-comparisons of such approaches despite their importance. In this study, we present a comparison between a triangular feature space-based (Tri) model and a machine learning (ML) based random forest (RF) model for seamless reconstruction in the European Space Agency’s Essential Climate Variables Soil Moisture product (ECV SM) over Oklahoma, USA. Five variables are implemented in the models: the normalized difference vegetation index (NDVI), daytime land surface temperature (LST), nighttime LST, daily average LST, and diurnal LST (ΔLST), and different combinations of these five variables are examined. The reconstructed soil moisture is validated against the original ECV SM and local in-situ measurements. The RF model achieved precise and outstanding performance (R2 = 0.95, RMSE = 0.02 m3/m3, bias = 0.07%) in comparison to the original ECV SM. Comparatively, the Tri and RF models revealed equivalent performance in fitting the in-situ measurements, and both effectively alleviated the bias from the ECV SM. Specifically, Tri, which used daytime LST and NDVI to establish a soil moisture estimation model, displayed the highest correlation coefficient as well as the smallest error (R = 0.620, RMSE = 0.080 m3/m3, ubRMSE = 0.038 m3/m3) for the in-situ measurements. Moreover, the assessments for sub-regions proved the consistency and robustness of both the Tri and RF models. Missing sub-regional training samples are unlikely to degrade performance of the models as long as there are sufficient valid samples in the whole region. The results of this study highlight the promising potential of Tri and RF models in efficiently filling the gaps in the ECV SM and can provide a reference for future studies focused on satellite-based soil moisture gap-filling algorithm selections.

Combined Use of Sentinel-1 SAR and Landsat Sensors Products for Residual Soil Moisture Retrieval over Agricultural Fields in the Upper Blue Nile Basin, Ethiopia.

The objective of this paper is to investigate the potential of sentinel-1 SAR sensor products and the contribution of soil roughness parameters to estimate volumetric residual soil moisture (RSM) in the Upper Blue Nile (UBN) basin, Ethiopia. The backscatter contribution of crop residue water content was estimated using Landsat sensor product and the water cloud model (WCM). The surface roughness parameters were estimated from the Oh and Baghdadi models. A feed-forward artificial neural network (ANN) method was tested for its potential to translate SAR backscattering and surface roughness input variables to RSM values. The model was trained for three inversion configurations: (i) SAR backscattering from vertical transmit and vertical receive (SAR VV) polarization only; (ii) using SAR VV and the standard deviation of surface heights (), and (iii) SAR VV, , and optimal surface correlation length (). Field-measured volumetric RSM data were used to train and validate the method. The results showed that the ANN soil moisture estimation model performed reasonably well for the estimation of RSM using the single input variable of SAR VV data only. The ANN prediction accuracy was slightly improved when SAR VV and the surface roughness parameters ( and ) were incorporated into the prediction model. Consequently, the ANN’s prediction accuracy with root mean square error (RMSE) = 0.035 cm3/cm3, mean absolute error (MAE) = 0.026 cm3/cm3, and r = 0.73 was achieved using the third inversion configuration. The result implies the potential of Sentinel-1 SAR data to accurately retrieve RSM content over an agricultural site covered by stubbles. The soil roughness parameters are also potentially an important variable to soil moisture estimation using SAR data although their contribution to the accuracy of RSM prediction is slight in this study. In addition, the result highlights the importance of combining Sentinel-1 SAR and Landsat images based on an ANN approach for improving RSM content estimations over crop residue areas.

ESTIMATION OF SOIL MOISTURE AND EARTH’S SURFACE TEMPERATURE USING LANDSAT-8 SATELLITE DATA

Abstract. Soil moisture estimation is essential for optimal water and soil resources management. Surface soil moisture is an important variable in the natural water cycle, which plays an important role in the global equilibrium of water and energy due to its impact on hydrological, ecological and meteorological processes. Soil moisture changes due to the variability of soil characteristics, topography and vegetation in time and place. Soil moisture measurements are performed directly using in situ methods and indirect, by means of transfer functions or remote sensing. Since in-site measurements are usually costly and time-consuming in large areas, we can use methods such as remote sensing to estimate soil moisture at very large scales. The purpose of this study is to estimate soil moisture using surface temperature and vegetation indices for large areas. In this paper, ground temperature was calculated using Landsat-8 thermal band for Mashhad city and was used to estimate the soil moisture content of the study area. The results showed that urban areas had the highest temperature and less humidity at the time of imaging. For this purpose, using the LANDSAT 8 images, the indices were extracted and validated with soil moisture data. In this research, the study area was described and then, using the extracted indices, the estimated model was obtained. The results showed that there is a good correlation between surface soil moisture content with LST and NDVI indices (95%). The results of the verification of the soil moisture estimation model also showed that this model with a mean error of less than 0.001 can predict the surface moisture content, this small amount of error indicates the precision of the proposed model for estimating surface moisture.

A modified physical-based water-retention model for continuous soil moisture estimation during infiltration: experiments on saline and non-saline soils

ABSTRACT Water infiltration is an issue of vital concern to farmland irrigation. Besides, a soil-water evaluation during infiltration benefits irrigation determination and salt leaching, which has still not been well revealed through a physical-based model regarding continuous soil-water estimation. We conducted one-dimensional infiltration on non-saline loam soil and saline sandy loam soil to propose a modified water-retention model which was expected to estimate continuous soil-water changes during infiltration. Results indicated that for the non-saline soil and the saline soil with different salinities, the modified water-retention model can be established based on the vertical one-dimensional algebraic model and the Kostiakov model. But different from the traditional vertical one-dimensional algebraic model, the new established model can be adopted for calculating and predicting continuous water content during infiltration at any duration, with higher accuracy than using HYDRUS, due to direct parameter acquisition from the observed infiltration data rather than indirect acquisition from soil water characteristic curve. The established potential relationship between soil moisture and cumulative infiltration promotes the development of precise agriculture.

A model for soil moisture dynamics estimation based on artificial neural network

Research on soil moisture estimation models can effectively improve the growth environment of crops. In this paper, the author studied the artificial neural network and variation pattern of soil moisture. Then, application of the model for water diversion estimation was explored based on artificial neural network. On this basis, an optimization algorithm was presented to simulate water diversion. Furthermore, a model for remote sensing of soil moisture dynamics was applied to artificial neural network. It has been proven that the research can optimize the application of the proposed model, laying a solid foundation for future study.