Estimation Of Soil Moisture Content Research Articles

Spatial mapping of soil moisture content using very-high resolution UAV-based multispectral image analytics

Assessing soil moisture content (SMC) is necessary for managing water at a spatial scale. Remote sensing technologies provide a robust approach for detecting the spatial-temporal fluctuations of SMC. The aim of this study was to estimate SMC at different soil depths using very high-resolution unmanned aerial vehicle (UAV)-based multispectral (MS) images and machine learning algorithms and generate spatial maps of SMC using the best-performed machine learning (ML) algorithm. The UAV-based multispectral images of bare soil were captured at 40 m altitude with a very high spatial resolution (2.89 cm) during the rabi 2021/22 season. At the same time, the soil samples were collected from different soil depths, and the gravimetric SMC was measured. Five machine-learning algorithms (Linear Regression (LR), K-Nearest Neighbors (KNN), Random Forest (RF), Decision Tree (DT), and Support Vector Regression (SVR)) were used to train the model between SMC and MS data (MS band reflectance and vegetation indices). The soil with high SMC has low spectral reflectance and soil with low SMC shows high spectral reflectance. For the prediction of surface SMC, the linear regression (R2 = 0.89; RMSE = 2.80 %) and 5 cm depth SMC, the SVR (R2 = 0.64; RMSE = 3.03 %) were performed well compared to other ML algorithms. For surface SMC, blue band reflectance, and 5 cm depth SMC, the Ratio Vegetation Index (RVI) correlated well compared to others. All models failed to predict the SMC at the deeper soil depths. The spatial SMC mapping described the visual color variations in SMC within the field. Crop irrigation scheduling can be significantly improved through the insights this spatial SMC estimation approach provides, making it a valuable tool for farmers and irrigation planners.

Estimating soil moisture content in citrus orchards using multi-temporal sentinel-1A data-based LSTM and PSO-LSTM models

Soil moisture content is a vital variable in agricultural, hydrological, ecological and climatological processes. However, susceptible to soil type, soil structure, topography, vegetation and human activities, soil moisture content exhibits strong spatial heterogeneity in spatial distribution, which makes it difficult to accurately estimate the soil moisture content distribution information at a large scale using conventional methods. To solve the problem, this study proposed a novel hybrid model (PSO-LSTM) based on the particle swarm optimization (PSO) and long short-term memory (LSTM) network model to accurately predict soil moisture content at a large scale. Five different input combinations were constructed based on the vertical polarization (VV) and cross-polarization (VH) of multi-phase Sentinel-1A data, and the soil moisture content at depths of 5 cm, 10 cm, 20 cm and 40 cm in citrus orchards were estimated using the standalone LSTM and hybrid PSO-LSTM models. The results showed that the estimation accuracy of the hybrid PSO-LSTM model was greater than that of the standalone LSTM model at different depths, with the normalized root mean square error (NRMSE) of 4.568–11.023 % and 18.056–30.156 %, respectively. With the VV polarization as the only inputs, the PSO-LSTM model obtained high prediction accuracy, with the normalized root mean square error (NRMSE) of 5.458–10.125 %, respectively. Therefore, the PSO-LSTM model with VV polarization input was recommended to estimate the soil moisture content at different depths in citrus orchards, which provides important data for decision-making on distributed precision irrigation at a large scale.

Optimization of multi-dimensional indices for kiwifruit orchard soil moisture content estimation using UAV and ground multi-sensors

Accurately estimating of soil moisture content (SMC) is essential for effective irrigation water management and optimizing plant water productivity. Recent advancements in multi-sensor platforms and ensemble learning (EL) algorithms such as the use of UAV-based multispectral (MS) and thermal infrared (TIR) images, have enabled precise mapping of SMC at the field level. The purpose of this study is to optimize the multi-dimensional (n-D) index set and EL model to achieve accurate inversion mapping of SMC in the 0–60 cm root zone at a kiwifruit orchard during the fruit growth period (May-Sep 2022). The n-D index set was computed using kiwifruit canopy MS and TIR data from UAV along with in-field temperature data (air temperature [Ta], kiwifruit canopy temperature [Tc], soil temperature [Ts], and Ta-Tc, Ta-Ts, Tc-Ts). The primary findings of this study are as follows: (1) The Three-Dimensional Drought Index (TDDI), Temperature Vegetation Drought Index (TVDI), and Crop Water Stress Index (CWSI) demonstrated superior performance in capturing temporal variations of root zone SMC compared to Normalized Difference Vegetation Index (NDVI) and Enhance Vegetation Index (EVI). (2) The optimal Categorical Boosting (Catboost) model with two optimal TDDIs (TDDI22: (Tc-Ts)-EVI10,6,2-CWSI space and TDDI7: Tc-NDVI7,3-CWSI space), exhibited exceptional performance for SMC estimation, with determination coefficient (R2) and root-mean-square error (RMSE) of 0.966 ± 0.010 and 0.046 ± 0.003%, respectively. (3) The planted-by-planted mapping performed well in estimating root zone SMC at different growth stages and under different irrigation treatments, with a correlation coefficient (R) of 0.936 ± 0.074 (p < 0.001). This study demonstrated that the integration of multi-sensor indicators and EL models can improve the accuracy of SMC estimation due to its robust adaptability to various field conditions. Furthermore, the planted-grid-based SMC mapping framework can be considered as a valuable approach for monitoring dynamic SMC, facilitating informed irrigation decisions at the individual planting grid.

Estimation of Soil Moisture Content Based on Fractional Differential and Optimal Spectral Index

Applying hyperspectral remote sensing technology to the prediction of soil moisture content (SMC) during the growth stage of soybean emerges as an effective approach, imperative for advancing the development of modern precision agriculture. This investigation focuses on SMC during the flowering stage under varying nitrogen application levels and film mulching treatments. The soybean canopy’s original hyperspectral data, acquired at the flowering stage, underwent 0–2-order differential transformation (with a step size of 0.5). Five spectral indices exhibiting the highest correlation with SMC were identified as optimal inputs. Three machine learning methods, namely support vector machine (SVM), random forest (RF), and back propagation neural network (BPNN), were employed to formulate the SMC prediction model. The results indicate the following: (1) The correlation between the optimal spectral index of each order, obtained after fractional differential transformation, and SMC significantly improved compared to the original hyperspectral reflectance data. The average correlation coefficient between each spectral index and SMC under the 1.5-order treatment was 0.380% higher than that of the original spectral index, with mNDI showing the highest correlation coefficient at 0.766. (2) In instances of utilizing the same modeling method with different input variables, the SMC prediction model’s accuracy follows the order: 1.5 order &gt; 2.0 order &gt; 1.0 order &gt; 0.5 order &gt; original order. Conversely, with consistent input variables and a change in the modeling method, the accuracy order becomes RF &gt; SVM &gt; BPNN. When comprehensively assessing model evaluation indicators, the 1.5-order differential method and RF method emerge as the preferred order differential method and model construction method, respectively. The R2 for the optimal SMC estimation model in the modeling set and validation set were 0.912 and 0.792, RMSEs were 0.005 and 0.004, and MREs were 2.390% and 2.380%, respectively. This study lays the groundwork for future applications of hyperspectral remote sensing technology in developing soil moisture content estimation models for various crop growth stages and sparks discussions on enhancing the accuracy of these different soil moisture content estimation models.

Estimation of Soil Moisture Content of Farmlands Based on AEA Method of GPR

We used ground penetrating radar (GPR) to estimate the soil moisture content and its spatial distribution of farmland under different tillage modes in this paper. GPR data are collected on the farmlands with no-tillage (NT) and deep ploughing (DP), respectively. The average envelope amplitude (AEA) of GPR signal is used to estimate the soil moisture content which is constrained by time domain reflectometry (TDR) measurement. The findings of the field test indicate that the error of the moisture content obtained by AEA method is in a reasonable range. In the shallow layer of farmland, the AEA method of GPR for detecting the soil moisture content is feasible and different tillage modes can affect the change of soil moisture content.

Research on robust inversion model of soil moisture content based on GF-1 satellite remote sensing

This study aims to improve the accuracy and applicability of traditional linear regression and machine learning algorithms for monitoring soil moisture content by satellite remote sensing. Shahaoqu Experimental Station within Jiefangzha Irrigation District of Hetao Irrigation Area is selected as the study area. The dataset includes GF-1 satellite remote sensing images and measured soil moisture content. The study employs the best subset selection to determine the combination of sensitive variables for soil moisture content at different soil depths. Additionally, robust regression theory is introduced to traditional linear regression and machine learning algorithms to construct soil moisture content inversion models at different soil depths. The results show that all three improved robust algorithms effectively reduce the influence of outliers, improve the accuracy and stability of these inversion models. The improved robust nonlinear algorithm outperforms the improved robust linear algorithm. Compared with the unprocessed algorithm, at the soil depth of 0–20 cm, Robust Extreme Learning Machine (RELM) algorithm is the most optimal, with the validation set's R2adj increasing from 0.555 to 0.696 (approximately 25.33%) and the RMSE decreasing from 0.025 to 0.018 (approximately 26.54%). Robust Least Squares Support Vector Machine (RLSSVM) algorithm is the second best, with the validation set's R2adj increasing from 0.502 to 0.641 (approximately 27.75%) and the RMSE decreasing from 0.026 to 0.020 (approximately 24.59%). Robust Linear Regression (LMS-RLS) algorithm is relatively the worst, with the validation set's R2adj increasing from 0.491 to 0.659 (approximately 34.05%) and the RMSE decreasing from 0.026 to 0.019 (approximately 27.21%). The sensitivity of the five soil depths to soil moisture content follows the order of 0–20 cm, 20–40 cm, 0–60 cm, 0–40 cm, and 40–60 cm. The improved robust regression algorithms can improve the accuracy and applicability of soil moisture content estimation and provide a reference for monitoring soil moisture content using GF-1 satellite.

Research of soil surface image occlusion removal and inpainting based on GAN used for estimation of farmland soil moisture content

In smart agriculture, soil images are frequently used to estimate soil moisture content (SMC). The soil image can’t fully reflect the real soil surface condition due to the interference of weeds and other obstructions. To solve the occlusion problem, a new occlusion removal idea is proposed: identifying the occluded object, generating corresponding masks to cover it, and restoring the area. Therefore, it involves three parts: image inpainting network research, occlusion recognition and removal system, and practical application in farmland. Soil Surface Occlusion Image Inpainting Network (SOI NET) was proposed for image inpainting based on Generative Adversarial Networks (GAN). A two-stage generation network was designed: Rough Network and Refinement Network which uses the coherent semantic attention (CSA) module to improve the network's context information utilization capability. A soil surface image occlusion removal system was designed, including vehicle-mounted detection system, occlusion target recognition, mask generation and occlusion removal. Indoor and field models for estimating SMC using image color parameters were established. To verify the performance of SOI NET, experiments were carried out to compare with the traditional method and other deep learning methods. The actual farmland occlusion removal experiment was carried out with weeds as the removal object which are the most common occlusion. The results revealed that the system can effectively remove the occlusion from image surface images. In order to verify the improvement effect of occlusion removal image on the detection accuracy of vehicle-mounted terminals, further farmland experiments were conducted to estimate SMC. R2 increased from 0.637 to 0.667 and Root Mean Square Error (RMSE) decreased from 1.916 % to 1.822 % after occlusion removal. SOI NET and soil occlusion removal system can make the processed images reflect the real soil conditions and provide help for soil image analysis.

Estimation of Soil Moisture Content in Oredo Local Government Area of Edo-State using Remote Sensing Technique: 2018 Study

Estimation of Soil Moisture Content in Oredo Local Government Area of Edo-State using Remote Sensing Technique: 2018 Study

Estimation of Soil Moisture Content in Oredo Local Government Area of Edo-State Using Remote Sensing Technique: 2017 Study

Estimation of Soil Moisture Content in Oredo Local Government Area of Edo-State Using Remote Sensing Technique: 2017 Study

Soil moisture content estimation using active infrared thermography technique: An exploratory laboratory study

Small scale laboratory testing was conducted to estimate the moisture content of sandy soils using a non-destructive and non-contact infrared thermography (IRT) technique. Tests were carried out by applying an external halogen lamp light (400 ​W). In this study, the aim was to explore the feasibility of using IRT to assess the moisture content of sandy soil. The laboratory tests conducted included evaluating soil physical characteristics such as grain size distribution. Soil samples were prepared with five different moisture content percentages. The soil moisture content was then evaluated using two different techniques. The first is the gravimetric oven dry method as per (ASTM D2216, 2019). The second is the IRT Technique. During the second procedure the samples were photographed with an ICI IR-Pad 640 ​P Series camera, which had an image resolution of 640 ​× ​480 pixels and a capture rate of 6 frames/minute. The emissivity correction used was 0.98. The soil surface temperature was captured in both the heating and the cooling phase, with the temperature taken from a specific thermal image area called the region of interest (ROI). Moisture content measurements were taken for each sample using the oven-drying method at the initial stage, and at the end of the heating and cooling phases. The procedure produced a good correlation between moisture content and surface temperature in the heating phase. The soil moisture content and surface temperatures were inversely related and formed a linear line with a high degree of accuracy (r2 ​= ​0.9138). A validation check is done at the end for a sample with random moisture content using the IRT technique where the moisture content was found to be 6.81%, this value was confirmed with the oven dry procedure. It was found that IRT successfully estimated soil moisture content.

Shadow removal method of soil surface image based on GAN used for estimation of farmland soil moisture content

It is important to obtain soil moisture content (SMC) in farmland, and soil surface images can be used to rapidly estimate SMC. The objective of this study was to propose a shadow removal algorithm to eliminate the effect of shadows in soil surface images, so as to improve the accuracy of SMC estimation. The structure of the proposed soil shadow generative adversarial networks (SS GAN) was a circulating network, which is an unsupervised method and does not require paired shadow image sets for network training. Four loss functions were defined for the network to effectively remove shadows and ensure texture detail and color consistency. This method is compared with traditional methods, supervised and unsupervised deep learning techniques by comparative experiments. Evaluations were made from visual and quantitative comparisons. Visually, the best shadow removal method was proved, it almost has no shadow boundaries or shadow areas visible for samples. The peak signal to noise ratio (PSNR) and structural similarity (SSIM) were used to quantitatively compare shadow removal images with real non-shadow images. The PSNR and SSIM of SS GAN were 28.46 and 0.95 respectively, which are superior to other methods, indicating that the images processed by SS GAN were closer to the real non-shadow images. Field experiments results shown that SS GAN has excellent shadow removal performance in the self-developed vehicle-mounted detection system. In order to verify the improvement effect of shadow removal image on SMC estimation accuracy, further field test was conducted to estimate SMC. Compared with SMC estimation results before and after shadow removal, R 2 increased from 0.69 to 0.76, and root mean square error decreased from 1.39 to 0.94%. The results show that the proposed method can effectively remove the shadow of soil image and improve the accuracy of SMC estimation in farmland.

Satellite-Based Estimation of Soil Moisture Content in Croplands: A Case Study in Golestan Province, North of Iran

Soil moisture content (SMC) plays a critical role in soil science via its influences on agriculture, water resources management, and climate conditions. There is broad interest in finding relationships between groundwater recharge, soil characteristics, and plant properties for the quantification of SMC. The objective of this study was to assess the potential of optical satellite imagery for estimating the SMC over cropland areas. For this purpose, we collected 394 soil samples as targets in Gonbad-e Kavus in the Golestan province in the north of Iran, where a variety of crop types are cultivated. As input data, we first computed several spectral indices from Sentinel 2 (S2) and Landsat 8 (L8) images, such as the Normalized Difference Water Index (NDWI), Modified Normalized Difference Water Index (MNDWI), and Normalized Difference Salinity Index (NDSI), and then analyzed their relationships with surveyed SMC using four machine learning regression algorithms: random forests (RFs), XGBoost, extra tree decision (EDT), and support vector machine (SVM). Results revealed a high and rather similar correlation between the spectral indices and measured SMC values for both S2 and L8 data. The EDT regression algorithm yielded the highest accuracy, with an R2 = 0.82, MAE = 3.74, and RMSE = 1.08 for S2 and R2 = 0.88, RMSE = 2.42, and MAE = 1.08 for L8 images. Results also revealed that MNDWI, NDWI, and NDSI responded most sensitively to SMC estimation.

Evaluating soil moisture content under maize coverage using UAV multimodal data by machine learning algorithms

Timely and accurate estimation of soil moisture content (SMC) is essential for precise irrigation management at the farm scale. Unmanned aerial vehicle (UAV) remote sensing with a high spatiotemporal resolution has become a promising method for SMC monitoring. Many existing SMC models have only been tested at a specific crop growth stage using a single type of sensor, and the effects of growth stage and irrigation variation on SMC estimation accuracy remain unclear. To address these limitations, this study used UAV-based multimodal data to quantify SMC in a maize field under various levels of irrigation over two years using three machine learning algorithms (MLA): partial least squares regression (PLSR), K nearest neighbor (KNN), and random forest regression (RFR). The results demonstrated that multimodal data fusion improves the SMC estimation accuracy regardless of the MLA, especially the joint use of thermal and multispectral data. Among three SMC regression models, the RFR model produced the most accurate SMC estimation for the two growing seasons regardless of sensor combinations. The RFR model using all three types of data generated the most accurate and robust SMC estimation at the vegetative stage with R2 of 0.68 and 0.78, and rRMSE of 20.82% and 19.36% for 10- and 20-cm soil depths, respectively; it also produced the best SMC estimation accuracy under well-watered and mild to modest deficit irrigation treatments for both soil depths. The study shows that the high spatial–temporal maps of SMC using UAV-based multimodal data has promising potential for supporting decision-making in irrigation scheduling at the farmland scale.

Evaluation of new methods for drought estimation in the Canadian Forest Fire Danger Rating System

Background Canadian fire management agencies track drought conditions using the Drought Code (DC) in the Canadian Forest Fire Danger Rating System. The DC represents deep organic layer moisture.Aims To determine if electronic soil moisture probes and land surface model estimates of soil moisture content can be used to supplement and/or improve our understanding of drought in fire danger rating.Methods We carried out field studies in the provinces of Alberta and Ontario. We installed in situ soil moisture probes at two different depths in seven forest plots, from the surface through the organic layers, and in some cases into the mineral soil.Results Our results indicated that the simple DC model predicted the moisture content of the deeper organic layers (10–18 cm depths) well, even compared with the more sophisticated land surface model.Conclusions Electronic moisture probes can be used to supplement the DC. Land surface model estimates of moisture content consistently underpredicted organic layer moisture content.Implications Calibration and validation of the land surface model to organic soils in addition to mineral soils is necessary for future use in fire danger prediction.

Assessing the accuracy indicators of the ground-based spectral estimation of soil moisture

Contact point field measurements of soil moisture are characterized by low productivity. High efficiency of determining and monitoring soil moisture can be achieved remote sensing. The known microwave methods for remote determination of soil moisture have low spatial resolution, are expensive and hence unsuitable for the use in agriculture. For estimating soil moisture content, the article proposes the ground-based on-board complex, which includes an on-board passive meter of the optical signal reflected from the soil and a ground contact meter. Operation of the latter can be performed either by an automatic measuring network or by an operator conducting contact measurements. The authors formulated and solved the problem related to assessing the accuracy of validation of the on-board measurement of soil moisture content. As an on-board meter, a passive radiation spectroradiometer of the soil illuminated by the Sun is used, and as a ground meter, a contact meter is used for estimating soil moisture at different spatial increments. The accuracy indicators of ground measurements and their relationship with the estimated values were analyzed. Comparison of the results obtained by the ground and on-board measurements allowed to determine the relationship between the moisture content measured from the board and the error of ground measurements as a validation error.

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.

The Utilization of Sentinel-1 Soil Moisture Satellite Imagery for Runoff Coefficient Analysis

Soil moisture content can provide information about the relationship between runoff coefficient and water availability to increase agricultural productivity and measure potential erosion rates. Observation of soil moisture content sometimes cannot be directly measured. Therefore, the study was conducted to develop practical soil moisture monitoring techniques using Sentinel-1 satellite images. Moisture content measurements obtained from the Sentinel-1 satellite image are pre-processed through the Sentinel Application Platform (SNAP), which is then processed using Dubois model to get the soil permittivity value from the backscatter value (σ◦). The moisture content was obtained using Topp’s model. The gravimetric methods in situ soil tests were used to validate the soil moisture content estimation obtained from Sentinel-1 image processing. Based on this research, the Sentinel-1 satellite provides a solution for monitoring soil moisture. The relationship between moisture content and runoff coefficient shows a greater the soil moisture value, the higher the runoff coefficient will obtain. This study proves that the developed land and valley condition has a high runoff coefficient. A high runoff coefficient value has susceptibility to high erosion rates. Sites with similar characteristics need to consider the appropriate land cover to ensure the sustainability of land conservation while still benefiting from agricultural activities.

An angular normalization method for temperature vegetation dryness index (TVDI) in monitoring agricultural drought

Characterizing drought is mandatory for a thorough monitoring of crop growth. Such information can be inferred from radiometric measurements in optical and thermal spectral ranges. The Temperature Vegetation Dryness Index (TVDI) presents the advantage to mix spectral information since it associates land surface temperature (LST) with normalized difference vegetation index (NDVI). NDVI is known to be weakly impact by directional effects, which is not the case of TVDI as the accuracy assessment of LST can be severely hampered directional anisotropy (DA). In this paper, DA feature of TVDI is analyzed using the kernel-driven model Vinnikov-Li (VinLi) that serves to perform through simulations and remove DA. The study uses both airborne and satellite datasets. TVDI was found to be angularly dependent, with a possible uncertainty larger than 15%. Compared against TVDI without angular correction, the normalized results displayed a better correlation with soil moisture content, and the root mean squared error (RMSE) of estimated soil moisture content decreased obviously, from 0.050 m3/m3 to 0.045 m3/m3 for airborne data over maize canopies, from approximately 0.036 m3/m3 to 0.030 m3/m3 for satellite data over cropland canopies, and from approximately 0.032 m3/m3 to 0.028 m3/m3 for satellite data over grassland canopies. The evaluation was carried out using the complied synthetic datasets based on the Soil Canopy Observation, Photochemistry and Energy Fluxes (SCOPE) model. These also confirmed the occurrence of significant DAs of TVDIs and good performance of VinLi in reducing DA of TVDI by >50%. Based on the VinLi method, an angular-independent TVDI appears necessary to reduce uncertainties originated from illuminating and viewing geometries for the purpose of agriculture monitoring with possible drought episodes. Although further evaluation is still needed using surface measurements, the outcomes support a confident use of TVDI for observations from satellite and airborne/unmanned aerial vehicle.

Hyperspectral Estimates of Soil Moisture Content Incorporating Harmonic Indicators and Machine Learning

Soil is one of the most significant natural resources in the world, and its health is closely related to food security, ecological security, and water security. It is the basic task of soil environmental quality assessment to monitor the temporal and spatial variation of soil properties scientifically and reasonably. Soil moisture content (SMC) is an important soil property, which plays an important role in agricultural practice, hydrological process, and ecological balance. In this paper, a hyperspectral SMC estimation method for mixed soil types was proposed combining some spectral processing technologies and principal component analysis (PCA). The original spectra were processed by wavelet packet transform (WPT), first-order differential (FOD), and harmonic decomposition (HD) successively, and then PCA dimensionality reduction was used to obtain two groups of characteristic variables: WPT-FOD-PCA (WFP) and WPT-FOD-HD-PCA (WFHP). On this basis, three regression models of principal component regression (PCR), partial least squares regression (PLSR), and back propagation (BP) neural network were applied to compare the SMC predictive ability of different parameters. Meanwhile, we also compared the results with the estimates of conventional spectral indices. The results indicate that the estimation results based on spectral indices have significant errors. Moreover, the BP models (WFP-BP and WFHP-BP) show more accurate results when the same variables are selected. For the same regression model, the choice of variables is more important. The three models based on WFHP (WFHP-PCR, WFHP-PLSR, and WFHP-BP) all show high accuracy and maintain good consistency in the prediction of high and low SMC values. The optimal model was determined to be WFHP-BP with an R2 of 0.932 and a prediction error below 2%. This study can provide information on farm entropy before planting crops on arable land as well as a technical reference for estimating SMC from hyperspectral images (satellite and UAV, etc.).

Machine Learning Models for Enhanced Estimation of Soil Moisture Using Wideband Radar Sensor.

In this paper, machine learning models for an effective estimation of soil moisture content using a microwave short-range and wideband radar sensor are proposed. The soil moisture is measured as the volumetric water content using a short-range off-the-shelf radar sensor operating at 3–10 GHz. The radar captures the reflected signals that are post processed to determine the soil moisture which is mapped to the input features extracted from the reflected signals for the training of the machine learning models. In addition, the results are compared and analyzed with a contact-based Vernier soil sensor. Different machine learning models trained using neural network, support vector machine, linear regression and k-nearest neighbor are evaluated and presented in this work. The efficiency of the model is computed using root mean square error, co-efficient of determination and mean absolute error. The RMSE and MAE values of KNN, SVM and Linear Regression are 11.51 and 9.27, 15.20 and 12.74, 3.94 and 3.54, respectively. It is observed that the neural network gives the best results with an R2 value of 0.9894. This research work has been carried out with an intention to develop cost-effective solutions for common users such as agriculturists to monitor the soil moisture conditions with improved accuracy.