Estimation Of Soil Moisture Content Research Articles

Ensemble Machine-Learning-Based Framework for Estimating Surface Soil Moisture Using Sentinel-1/2 Data: A Case Study of an Arid Oasis in China

Soil moisture (SM) is a critical parameter in Earth’s water cycle, significantly impacting hydrological, agricultural, and meteorological research fields. The challenge of estimating surface soil moisture from synthetic aperture radar (SAR) data is compounded by the influence of vegetation coverage. This study focuses on the Weigan River and Kuche River Delta Oasis in Xinjiang, employing high-resolution Sentinel-1 and Sentinel-2 images in conjunction with a modified Water Cloud Model (WCM) and the grayscale co-occurrence matrix (GLCM) for feature parameter extraction. A soil moisture inversion method based on stacked ensemble learning is proposed, which integrates random forest, CatBoost, and LightGBM. The findings underscore the feasibility of using multi-source remote sensing data for oasis moisture inversion in arid regions. However, soil moisture content estimates tend to be overestimated above 10% and underestimated below 5%. The CatBoost model achieved the highest accuracy (R2 = 0.827, RMSE = 0.014 g/g) using the top 16 feature parameter groups. Additionally, the R2 values for Stacking1 and Stacking2 models saw increases of 0.008 and 0.016, respectively. Thus, integrating multi-source remote sensing data with Stacking models offers valuable support and reference for large-scale estimation of surface soil moisture content in arid oasis areas.

Estimation of the Soil Moisture Content in a Desert Steppe on the Mongolian Plateau Based on Ground-Penetrating Radar

Desert grasslands are a crucial component of terrestrial ecosystems that play vital roles in regional and global hydrological cycling, climate change, and ecosystem balance through variations in their soil moisture content (SMC). Despite this, current research on the SMC of desert grasslands remains insufficient, with many areas remaining underexplored. In this study, we focused on a typical desert grassland located in the northern foothills of the Yinshan Mountains. Ground-penetrating radar (GPR) exploration and soil sampling were used to test existing mixed-media models, and a new mixed-media model was calibrated using cross-validation methods. Among the three general mixed-media models, the Topp and Roth models yielded more accurate SMC estimates for the study area, with root mean square errors of 0.0091 g/cm3 and 0.0054 g/cm3, respectively, and mean absolute percentage errors of 25.86% and 19.01%, respectively, demonstrating their high precision. A comparison of the calibrated and original mixed-media models revealed that the estimation accuracy was significantly improved after parameter calibration. After parameter calibration, the Ferre model achieved an accuracy comparable to that of the Topp model. Parameter-calibrated models can be used to estimate the SMC using GPR data, offering a higher precision than general models and possessing greater suitability for the study area. The soil in the study area is primarily composed of sand particles and is therefore more compatible with the parameters of the Topp model, whereas the Ferre model requires further parameter calibration to achieve effective application.

Soil moisture content estimation of drip-irrigated citrus orchard based on UAV images and machine learning algorithm in Southwest China

Soil moisture content (SMC), as a pivotal component in the energy and matter exchange processes within the soil-plant-atmosphere continuum, plays a crucial role in surface water dynamics, energy fluxes, and carbon cycling within ecosystems. The development of remote sensing technology has offered new perspectives for monitoring soil moisture at regional scales. Unmanned aerial vehicles (UAV) equip with multispectral have distinct advantages for vegetation monitoring, including rapidity and cost-effectiveness, which has superior applicability and practicality. Therefore, in a 5a "Daya" late-maturing citrus orchard, the vegetation index (VI) and texture feature (TF) information of citrus canopy based on UAV multi-spectral images were extracted, and soil and plant analyzer development (SPAD) of citrus was collected. These different data sources were integrated into the framework of the random forest algorithm (RF) and genetic algorithm-optimized random forest (GA-RF) to evaluate the accuracy of surface SMC (SSMC) estimation in citrus orchard. The Biswas model was utilized to simulate the root zone SMC (RSMC). The spatiotemporal variations of SMC in citrus orchard were analyzed, and the potential of low-cost sensor-equipped drones in rapidly acquiring spatial and temporal distribution information of SMC at a large regional scale was explored. The results indicated that the GA-RF models outperformed the RF models in estimating citrus orchard SMC (with R2 ranging from 0.502 to 0.949 and RMSE ranging from 0.552 % to 3.166 % for GA-RF, compared to R2 ranging from 0.430 to 0.936 and RMSE ranging from 0.587 % to 3.449 % for the RF). The GA-RF models using VI+SPAD as inputs exhibited the best performance for SMC at depths of 5 cm, 10 cm, 20 cm and 40 cm (SMC5, SMC10, SMC20 and SMC40) across citrus growth stages (R2 ranging from 0.793 to 0.949 at 5 cm, R2 ranging from 0.702 to 0.938 at 10 cm, R2 ranging from 0.714 to 0.927 at 20 cm). In bud bust to flowering, young fruit and fruit maturation stages (stage Ⅰ, ⅠⅠ and ⅠⅤ), all models demonstrated good accuracy in estimating SMC at depth of 10 cm (R2 ranging from 0.567 to 0.908 in stage Ⅰ, with R2 ranging from 0.681 to 0.916 in stage ⅠⅠ and R2 ranging from 0.579 to 0.938 in stage ⅠⅤ). In fruit expansion stage (stage III), the models performed best in predicting SMC5 (R2 ranging from 0.698 to 0.861). The Biswas model was constructed to simulate SMC40 by utilizing the inverted SMC10 and SMC20, thereby generating spatiotemporal distribution maps of SMC at different depths in citrus orchard. The SSMC was susceptible to environmental factors, exhibiting significant spatiotemporal heterogeneity. In summary, this study illustrated that the integration of multiple data sources into GA-RF enhanced the estimation performance of SMC at different growth stages of late-maturing citrus orchard in the Southwest China. Additionally, it enabled the rapid and efficient monitoring of spatiotemporal variations in SMC, providing an effective method and practical foundation for precision irrigation and improved water use efficiency.

Estimation of Soil Moisture during Different Growth Stages of Summer Maize under Various Water Conditions Using UAV Multispectral Data and Machine Learning

Accurate estimation of soil moisture content (SMC) is vital for effective farmland water management and informed irrigation decision-making. The utilization of unmanned aerial vehicle (UAV)-based remote sensing technology to monitor SMC offers advantages such as mobility, high timeliness, and high spatial resolution, thereby compensating for the limitations of in-situ observations and satellite remote sensing. However, previous research has primarily focused on SMC diagnostics for the entire crop growth period, often neglecting the development of targeted soil moisture modeling paradigms that account for the specific characteristics of the canopy and root zone at different growth stages. Furthermore, the variations in soil moisture status between fields, resulting from the hysteresis of water flow in irrigation channels at different levels, may influence the development of soil moisture modeling schemes, an area that has been seldom explored. In this study, SMC models based on UAV spectral information were constructed using Random Forest (RF) and Particle Swarm Optimization-Support Vector Machine (PSO-SVM) algorithms. The soil moisture modeling paradigms (i.e., input–output mapping) under different growth stages and soil moisture conditions of summer maize were systematically compared and discussed, along with the corresponding physical interpretability. Our results showed that (1) the SMC modeling schemes differ significantly across the various growth stages, with distinct input–output mappings recommended for the early (i.e., jointing, tasselling, and silking stages), middle (i.e., blister and milk stages), and late (i.e., maturing stage) periods. (2) these machine learning-based models performed best at the jointing stage, while subsequently, their accuracy generally exhibited a downward trend as the maize grew. (3) the RF model demonstrates superior robustness in estimating soil moisture status across different fields (moisture conditions), achieving optimal estimation accuracy in fields with overall higher SMC in line with the PSO-SVM model. (4) unlike the RF model’s robustness in spatial SMC diagnostics, the PSO-SVM model more reliably captured the temporal dynamics of SMC across different growth stages of summer maize. This study offers technical references for future modelers in UAV-based SMC modeling across various spatial and temporal conditions, addressing both the types of models as well as their input features.

Study on Soil Moisture Status of Soybean and Corn across the Whole Growth Period Based on UAV Multimodal Remote Sensing

Accurate estimation of soil moisture content (SMC) in the field is a critical aspect of precise irrigation management. The development of unmanned aerial vehicle (UAV) platforms has provided an economically efficient means for field-scale SMC measurements. However, previous studies have mostly focused on single-sensor estimates of SMC. Additionally, the lack of differentiation between various crops and their growth stages has resulted in an unclear understanding of how crop types and growth stages affect the accuracy of SMC estimation at different soil depths. Therefore, the purpose of this paper was to use UAV multimodal remote sensing and a machine learning algorithm to estimate the SMC in agricultural fields and investigate estimation’s effectiveness under different scenarios. The results indicated the following: (1) The multispectral remote sensing method provided higher accuracy in SMC estimation compared to thermal infrared remote sensing. Moreover, the integration of multimodal data improved the accuracy of SMC estimation, enhancing the coefficient of determination (R2) by approximately 14% over that achieved through the use of multispectral data alone and 39% over that of thermal infrared data alone. (2) Across the entire growth period, the optimal soil depths of SMC estimation for soybean were 10 cm and 20 cm (average R2 were 0.81 and 0.82, respectively), while for corn, they were 10 cm, 20 cm, and 40 cm (average R2 were 0.59, 0.60, and 0.55, respectively). (3) The SMC estimation model performed better for both crops during the first three growth stages, with accuracy declining in the maturity stage. These results demonstrate that this approach can provide relatively accurate root zone SMC estimates for different crops throughout their main growth periods. Thus, it can be employed for SMC monitoring and precision irrigation system design.

Monitoring soil moisture content in the root zone of winter wheat with multi-angle multispectral imagery

ABSTRACT Exploring the impact of different monitoring angles from unmanned aerial vehicle (UAV) on the monitoring accuracy of soil moisture content (SMC) is crucial for precision irrigation. To this end, experiment was conducted to monitor the SMC of winter wheat at different growth stages under different irrigation treatments in Yangling, Shaanxi, China. At a solar zenith angle of 45°, multispectral remote sensing data from a UAV were collected at thirteen different monitoring zenith angles. Simultaneously, the SMC in the wheat root zone was measured. From the UAV multispectral images, spectral reflectance was extracted for the construction of vegetation indices. Then the correlation between the vegetation indices and the measured SMC was analyzed. With the vegetation indices as input variables, SMC monitoring models were constructed using Extreme Learning Machine (ELM), Random Forest (RF), and Back Propagation Neural Network (BPNN). The study also examined the effect of specific angles (hotspot and dark spot angles) on the estimation accuracy of the SMC at the nadir angle. The results indicated that different monitoring angles significantly impact the SMC estimation accuracy. Band reflectance and vegetation indices exhibited significant peak values and angular effects at the monitoring zenith angle of 45°. The models achieved the optimal inversion accuracy at the hotspot angle (at a monitoring zenith angle of 45° in the solar principal plane), and the accuracy was ranked as follows: BPNN (R2 = 0.71; RMSE = 1.69) > ELM (R2 = 0.52; RMSE = 1.94) > RF (R2 = 0.48; RMSE = 2.10). By eliminating shadows at the nadir angle through the threshold of dark spot, inversion accuracy similar to the hotspot direction is achieved. This study provides a basis for appropriate selection of UAV flight angles for the monitoring of SMC in the root zone of winter wheat.

Factors Affecting Soil Bulk Density: A Conceptual Model

Soil bulk density is an important aspect of soil properties, and it can affect agroecology as in diversity, synergies, efficiency, recycling, and resilience. Diversity as in soil low in bulk density allows greater penetration of plant roots allow better access of water and nutrients. The synergistic effect of adequate supply of soil moisture content in forming moderately low soil bulk density for deeper plant roots for better access to water and nutrients. Agroecology efficiency reduces the need of external inputs to reduce environmental impact related to the resulted excessive pollution and wastage. Ultimately, an improved soil bulk density may increase the overall agroecosystem resilience and output, thus, improving the efficiency. Recycling in agroecology involved mass recycling and heat circulation as in nutrient, water, and waste reduction. A high soil bulk density will impede the recycling process the soil. Resilience as in drought resistance is for soil not too loosely packed that cannot hold water or too compacted until flooding water occurs at the surface and too little water infiltrates the soil. In addition, soil bulk density can affect the estimation of soil moisture content and temperature distribution. The ability to understand the factors that influence changes in soil bulk density would improve the predictive ability of the current model. The current study conducts a review on factors affecting the soil bulk density that is divided into soil physical, chemical, biological, environmental, and management practices. A cumulative of more than 50 factors are discussed in the review on how it affects the change in soil bulk density. The current review illustrates the conceptual relation of these factors on soil bulk density. Also, the result of the current work can be used to support future endeavour by turning the conceptual relation into quantifiable predictive model.

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.

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.

Estimation of soil moisture and organic matter content in saline alkali farmland by using CARS algorithm combined with covariates

Rapid acquisition of the data of soil moisture content (SMC) and soil organic matter (SOM) content is crucial for the improvement and utilization of saline alkali farmland soil. Based on field measurements of hyperspectral reflectance and soil properties of farmland soil in the Hetao Plain, we used a competitive adaptive reweighted sampling algorithm (CARS) to screen sensitive bands after transforming the original spectral reflectance (Ref) into a standard normal variable (SNV). Strategies Ⅰ, Ⅱ, and Ⅲ were used to model the input variables of Ref, Ref SNV, Ref-SNV+ soil covariate (SC), and digital elevation model (DEM). We constructed SMC and SOM estimation models based on random forest (RF) and light gradient boosting machine (LightGBM), and then verified and compared the accuracy of the models. The results showed that after CARS screening, the sensitive bands of SMC and SOM were compressed to below 3.3% of the entire band, which effectively optimized band selection and reduced redundant spectral information. Compared with the LightGBM model, the RF model had higher accuracy in SMC and SOM estimation, and the input variable strategy Ⅲ was better than Ⅱ and Ⅰ. The introduction of auxiliary variables effectively improved the estimation ability of the model. Based on comprehensive analysis, the coefficient of determination (Rp2), root mean square error (RMSE), and relative analysis error (RPD) of the SMC estimation model validation based on strategy Ⅲ-RF were 0.63, 3.16, and 2.01, respectively. The SOM estimation models based on strategy Ⅲ-RF had Rp2, RMSE, and RPD of 0.93, 1.15, and 3.52, respectively. The strategy Ⅲ-RF model was an effective method for estimating SMC and SOM. Our results could provide a new method for the rapid estimation of soil moisture and organic matter content in saline alkali farmland.

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.