Soil Moisture Values Research Articles

Spatial downscaling of SMAP soil moisture to high resolution using machine learning over China’s Loess Plateau

Soil moisture (SM) is a critical physical parameter in land surface processes that affects the atmospheric and hydrological cycles. Soil Moisture Active Passive (SMAP) mission produces high-quality global SM data, but its low spatial resolution limits the applications at a regional or local scale. In this study, we developed a novel method to select optimal factors from 34 candidate downscaling factors for each month using three machine learning methods (Back Propagation Neural Network (BPNN), Support Vector Machine (SVM) and Random Forest (RF)) to produce monthly time series SM products at a spatial resolution of 1 km for the entire Loess Plateau in China from 2015 to 2023. 11 in-situ SM measurements distributed in Loess Plateau and precipitation data were used to evaluate the downscaling performances of the three machine learning approaches. The results show that the spatial trends of the three downscaled SM were essentially the same as the SMAP SM, with similar spatial patterns, and all three downscaled SM can provide more spatial information and texture features than SMAP SM. Among the three types of downscaled SM, the RF downscaled SM reached the highest R (0.654) and the smallest unbiased Root Mean Square Error (ubRMSE) (0.044 m3m−3), better than the BPNN and SVM downscaled SM, and most closely matched the in-situ measurements. The dynamics of SM were successfully captured by RF downscaled SM, which exhibited strong temporal consistency with the in-situ SM and responded well to precipitation events, with significant increases in SM values in the month of high precipitation and subsequent months. In conclusion, the RF model has the best downscaling effect and it can be used to provide high spatial resolution SM data for applications at a regional or local scale across the Loess Plateau.

Geographic analysis of soil moisture recorded and estimated in Erbil governorate

Calculating of soil moisture and its monitoring is one of the important processes of water balance and sustainable agriculture. The intended goal of this study is to determine soil moisture values in Erbil Governorate and reveal the correlation coefficient and the degree of influence between its values with the elevation and total annual rainfall. The importance of the study lies In its discussion of the accuracy of extracting soil moisture values using satellite images, by comparing its results with the soil moisture values recorded in the governorate’s climate stations, the study reached a set of conclusions, the most important of which is: The results of using satellite images to estimate soil moisture are inaccurate. It is closer than guessing, and the results of applying the linear regression process indicated that there was a significant statistical relationship between the soil moisture values recorded with the two factors of elevation and the total annual rainfall, where the probability values were less than 0.01 and the values of the degree of influence between them reached 0.583 and 0.491 for each of them, respectively.‏

Combined Drought Index Using High-Resolution Hydrological Models and Explainable Artificial Intelligence Techniques in Türkiye

We developed a combined drought index to better monitor agricultural drought events. To develop the index, different combinations of the temperature condition index, precipitation condition index, vegetation condition index, soil moisture condition index, gross primary productivity, and normalized difference water index were used to obtain a single drought severity index. To obtain more effective results, a mesoscale hydrologic model was used to obtain soil moisture values. The SHapley Additive exPlanations (SHAP) algorithm was used to calculate the weights for the combined index. To provide input to the SHAP model, crop yield was predicted using a machine learning model, with the training set yielding a correlation coefficient (R) of 0.8, while the test set values were calculated to be 0.68. The representativeness of the new index in drought situations was compared with established indices, including the Standardized Precipitation-Evapotranspiration Index (SPEI) and the Self-Calibrated Palmer Drought Severity Index (scPDSI). The index showed the highest correlation with an R-value of 0.82, followed by the SPEI with 0.7 and scPDSI with 0.48. This study contributes a different perspective for effective detection of agricultural drought events. The integration of an increased volume of data from remote sensing systems with technological advances could facilitate the development of significantly more efficient agricultural drought monitoring systems.

Field-scale variability and dynamics of soil moisture in Southwestern Nigeria

This research leverages field-based sensors and Geographic Information Systems (GIS) to elucidate soil moisture dynamics across diverse land uses in Southwestern Nigeria, including Cocoa, Cassava, Oil Palm, and Riparian ecosystems. The study synthesizes soil moisture data and geospatial coordinates from strategically selected land covers with satellite-derived soil moisture records from NASA's Prediction of Worldwide Energy Resources (POWER) and the US Geological Survey. Through a robust analytical framework combining cross-correlation functions, wavelet analysis, and inverse distance weighting interpolation, we revealed distinct soil moisture patterns ranging from 0 to 47.9% in arable farms, 0 to 46.8% in built-up areas, 0 to 49.9% in cocoa farms, 0.2 to 49.7% in oil palm farms, and 5.6 to 94.1% in riparian vegetation. The coefficient of determination values between 0.7 and 0.97 (p ≤ 0.05) underscore the significant influence of periodic land use on soil moisture during the study period. A comparative analysis of ground-based and satellite data revealed a potential overestimation of minimum soil moisture values by 66% and an underestimation of maximum values by 4%. This investigation provides critical insights into the interplay between hydrological variables and agro-meteorological factors, informing the management and assessment of agricultural resources.

Performance of Soil Moisture Sensors at Different Salinity Levels: Comparative Analysis and Calibration.

Soil dielectric sensors have been widely used to obtain real-time soil moisture data, which are important for water resource management. However, soluble salts in the soil significantly affect the accuracy of these sensor measurements. Therefore, it is crucial to select suitable soil dielectric sensors for soil moisture measurements at different salinity levels. Eight mainstream sensors (EC-5, 5TE, Teros12, Hydra-probe II, TDR315L, TDR315H, TDR305H, and CS655) were selected and tested at four different soil salinity levels (EC1:5 = 3.0, 1.5, 1.0, and 0.75 dS·m-1). The measured values using the factory calibration formulas were compared at six soil moisture levels. The results showed that the measured soil moisture values from various sensors exhibited varying degrees of overestimation, which increased with increasing salinity. Only EC-5 did not exhibit distortion at high-salinity levels, with the measured values showing a good linear trend compared to the standard values. Mutational distortion of the measured apparent dielectric permittivity occurred in TDR315L, TDR315H, Hydra-probe II, and 5TE at EC1:5 = 3.0 dS·m-1. Insensitive distortion of the measured apparent dielectric permittivity occurred in Teros12 and TDR305H at EC1:5 = 3.0 dS·m-1 as well as in Teros12, TDR305H, 5TE and Hydra-probe II at EC1:5 = 1.5 dS·m-1. All tested sensors performed reasonably well at EC1:5 ≤ 1.0 dS·m-1. Seven sensors (excluding CS655) were calibrated within the distortion threshold. The soil moisture accuracy using the calibrated formulas could reach ±0.02 cm3·cm-3. At EC1:5 ≤ 1.0 dS·m-1, most sensors in this study could be applied with the factory calibration formulas. TDR series, EC-5, 5TE and Teros12 were recommended after calibration for EC1:5 > 1.0 dS·m-1. For extremely high soil salinity levels, the TDR series and EC-5 may be the best choices.

Smart Farm Control System Using IoT

This paper presents a way to do smart agriculture by using the advances in technology and information today, especially the Internet of Things (IoT) to help control the environment to change according to the needs of plants automatically. Which this research used the method of making a closed house or Greenhouse size 6m x 9m x 4m in number of 2 houses and using various types of sensor equipment. To detect parameters that affect the environment such as: Temperature sensor, Humidity sensor, Soil moisture sensor, pH sensor and Light sensor. To bring those values ​​forward to the microcontroller to process and order the electrical equipment to work, such as solenoid valves or water pumps; As well as send the parameter value to display the results to the application on the smartphone and the website through the wireless Internet system (WiFi) using the Blynk platform or Blynk cloud. From the results of the design and experiment, it was found that the system can control electrical equipment in the farm system, such as solenoid valves, water pump, and read various values ​​according to the set goals and objectives. We can order control Turn on and off the water system or the fog system anywhere at any time according to the conditions of the internet connection or direct control through the control box at any place or choose to have the system work automatically such as watering and misting according to the conditions we want and the system can also monitor or check the temperature, touch moisture, soil moisture, light intensity and pH value of the water at any time through the platform of the brand that can be used on both smartphones and computers.

Machine Learning Modelling for Soil Moisture Retrieval from Simulated NASA-ISRO SAR (NISAR) L-Band Data

Soil moisture is a critical factor that supports plant growth, improves crop yields, and reduces erosion. Therefore, obtaining accurate and timely information about soil moisture across large regions is crucial. Remote sensing techniques, such as microwave remote sensing, have emerged as powerful tools for monitoring and mapping soil moisture. Synthetic aperture radar (SAR) is beneficial for estimating soil moisture at both global and local levels. This study aimed to assess soil moisture and dielectric constant retrieval over agricultural land using machine learning (ML) algorithms and decomposition techniques. Three polarimetric decomposition models were used to extract features from simulated NASA-ISRO SAR (NISAR) L-Band radar images. Machine learning techniques such as random forest regression, decision tree regression, stochastic gradient descent (SGD), XGBoost, K-nearest neighbors (KNN) regression, neural network regression, and multilinear regression were used to retrieve soil moisture from three different crop fields: wheat, soybean, and corn. The study found that the random forest regression technique produced the most precise soil moisture estimations for soybean fields, with an R2 of 0.89 and RMSE of 0.050 without considering vegetation effects and an R2 of 0.92 and RMSE of 0.042 considering vegetation effects. The results for real dielectric constant retrieval for the soybean field were an R2 of 0.89 and RMSE of 6.79 without considering vegetation effects and an R2 of 0.89 and RMSE of 6.78 with considering vegetation effects. These findings suggest that machine learning algorithms and decomposition techniques, along with a semi-empirical technique like Water Cloud Model (WCM), can be effective tools for estimating soil moisture and dielectric constant values precisely. The methodology applied in the current research contributes essential insights that could benefit upcoming missions, such as the Radar Observing System for Europe in L-band (ROSE-L) and the collaborative NASA-ISRO SAR (NISAR) mission, for future data analysis in soil moisture applications.

The specifics оf тhe development оf soil flushing during spring snowmelt in the arable land of the northern zone of the Krasnoyarsk forest-steppe

The results of studies of soil flushing and accumulation in the period 2009–2017 in the northern part of the Krasnoyarsk forest-steppe in areas with high intensity of erosive and accumulative processes are presented. A wide range of erosion values from meltwater has been established – from 0.4 to 25 mm for the spring period. The results of the application of a refined Regional model for calculating potential flushing for the northern part of the Krasnoyarsk forest-steppe are presented. The analysis of meteorological factors and conditions of the underlying surface affecting the distribution of values of melt runoff, flushing and accumulation of soils on arable massifs located on inclined watershed surfaces is carried out: the specificity of the distribution of meltwater flows determines the formation of zones of extreme erosion along the perimeter of arable land in the lower parts of the slope – up to 15–20 mm of soil layer. In conditions of low slope steepness, the effect of relief on erosion is largely determined by the high slope lengths and melt water flow lines. The distribution of flushing values over the area of arable land shows a close correlation with the values of snow accumulation and soil moisture. The spatial and temporal patterns of the influence of the previous autumn soil moisture on the intensity of erosion have been established: the values of flushing increase with increasing soil moisture, the average values of erosion correlate with the values of autumn soil moisture in dynamics over the years of field observations. Special soil protection measures have been proposed for sites with the most intensive flushing in the research area.

High soil moisture promotes the emergence of ground beetles and spiders from soils in wheat fields

Promoting arthropods in agricultural landscapes can contribute substantially to stop their decline and enhance pest control. Higher soil moisture and the presence of field margins can increase the abundance of arthropods in agricultural landscapes and influence their distribution within crop fields. However, little is known about the influence of soil moisture and distance from field margins on the overwintering of arthropods in arable fields. We investigated the influence of soil moisture and distance from a field margin on the numbers of arthropods, ground beetles and spiders emerging from soil in winter wheat fields. We established transects in winter wheat fields away from two different types of field margins: (i) around small standing water bodies (kettle holes) to capture a wide range of soil moisture values and (ii) other semi-natural landscape elements. At three distances (1 m, 20 m, 50 m), we sampled arthropods with emergence traps and measured soil moisture between March and June. We found that soil moisture had a positive effect on the emergence numbers of arthropods in general and ground beetles and spiders in particular. Distance from field margins generally had negative effects on the emergence numbers of ground beetles, but positive effects on the emergence numbers of spiders. Emergence numbers and soil moisture content did not differ significantly between the two types of field margins. The high emergence numbers inside the fields indicate that arable fields are important overwintering habitats for beneficial arthropods. Proper management of arable soils to promote soil water holding capacity and soil moisture content may have the added benefit of promoting the production of beneficial natural enemies from local soils.

Plant Monitoring System Using Gray Level Co-occurrence Matrix and Weight Based Artificial Neural Network Algorithm over Internet of Things

The potential deployment of the plant monitoring system in cutting-edge technologies is currently generating a great deal of interest. A new IoT-based technology is used to track both the development and wellness of the plant. Setting up cross-device connectivity over the internet is the idea underlying IoT devices. It is a sizable network that links people and various interconnected elements in order to gather and transmit data. This research article aims to generate a manufacturing Internet of Things (IoT) based plant monitoring system that uses IoT sensors to detect environmental conditions. The IoT term is used to link objects to the internet and make it easier for consumers to obtain data. The technology can accurately perceive the surroundings in agriculture and convey the data to users. The system keeps track of several factors like soil moisture, temperature, and light intensity. The method begins with the collection of plant images and noise reduction pre-processing. Once the images have been segmented, they are done so utilizing the Region based K-Means Clustering (RKMC) technique. Following that, Gray Level Co-occurrence Matrix (GLCM) is utilized to extract features, with a focus on extracting the more informative characteristics like color, texture, and shape features. The classifying process is completed by utilizing Weight based Artificial Neural Network (WANN) algorithm for improving the plant monitoring system performance significantly. It offers IoT-based solutions to categorize plant illnesses and observe variables including soil moisture, air temperature, and pH values. Te findings of the suggested GLCM-WANN algorithm show better performers than existing methods for obtained values of computation complexity, accuracy, Mean Absolute Error (MAE), and Root Mean Square Error (RMSE).

Optimizing Plant Watering Efficiency via IoT: Fuzzy Sugeno Method with ESP8266 Microcontroller

This research explores automatic plant sprinklers utilizing fuzzy logic with the fuzzy Sugeno method to overcome optimal watering schedules. Developed with ESP8266 microcontroller hardware and Arduino IDE software, the WSN prototype focuses on spinach plants. Watering in the nursery was done twice a day for seedling growth, then reduced to once a day. Soil moisture levels were assessed using a soil analyzer, as well as temperature variations with the HTC-1 device. Python facilitates data analysis, generating fuzzy inference graphs on temperature sensors (10-40°C) and soil moisture values (1-1024 RH). This innovative approach, which integrates fuzzy logic and advanced hardware, offers a promising solution for effective plant care.

Effects Of Some Climatic Factors On Soil Moisture In Scots Pine Stands With Different Crown Closure

In this study, the effects of climate factors on soil moisture in Scots Pine stands with different crown closure were investigated. As a result of the study, it was revealed whether soil moisture values differ in Scots Pine stands in the same aspect, depth and slope group. The effects of some climate elements (air temperature, air humidity and rainfall) on the soil moisture values in the areas where the Scots Pine in different crown closures were investigated. We took 6 different forest plots in the same depth, slope and aspect group. The average soil moisture values was found 23.91% in Open forest plot (POF), 26.84% in P1 plot (Low crown closure scots pine stand), 16.72% in P2 (Medium crown closure scots pine stand), 16.00% in P3 (Full crown closure scots pine stand), 30.79% in Simulation plot (PS) and 28.62% in Erosion plot (PE). It was determined that the lowest average soil moisture values were found in the P2 and P3 plots and the highest soil moisture values were in the PS and PE plots.

Vulnerability assessment of aerosol and climate variability for rice and maize yield using EO datasets for sustainable agriculture over India.

Human-generated aerosol pollution gradually modifies the atmospheric chemical and physical attributes, resulting in significant changes in weather patterns and detrimental effects on agricultural yields. The current study assesses the loss in agricultural productivity due to weather and anthropogenic aerosol variations for rice and maize crops through the analysis of time series data of India spanning from 1998 to 2019. The average values of meteorological variables like maximum temperature (TMAX), minimum temperature (TMIN), rainfall, and relative humidity, as well as aerosol optical depth (AOD), have also shown an increasing tendency, while the average values of soil moisture and fraction of absorbed photosynthetically active radiation (FAPAR) have followed a decreasing trend over that period. This study's primary finding is that unusual variations in weather variables like maximum and minimum temperature, rainfall, relative humidity, soil moisture, and FAPAR resulted in a reduction in rice and maize yield of approximately (2.55%, 2.92%, 2.778%, 4.84%, 2.90%, and 2.82%) and (5.12%, 6.57%, 6.93%, 6.54%, 4.97%, and 5.84%), respectively. However, the increase in aerosol pollution is also responsible for the reduction of rice and maize yield by 7.9% and 8.8%, respectively. In summary, the study presents definitive proof of the detrimental effect of weather, FAPAR, and AOD variability on the yield of rice and maize in India during the study period. Meanwhile, a time series analysis of rice and maize yields revealed an increasing trend, with rates of 0.888 million tons/year and 0.561 million tons/year, respectively, due to the adoption of increasingly advanced agricultural techniques, the best fertilizer and irrigation, climate-resilient varieties, and other factors. Looking ahead, the ongoing challenge is to devise effective long-term strategies to combat air pollution caused by aerosols and to address its adverse effects on agricultural production and food security.

Contributions of Soil Moisture and Vegetation on Surface-Air Temperature Difference during the Rapid Warming Period

The difference (DIF) between land surface temperature (Ts) and near surface air temperature (Ta) is the key indicator of the energy budget of the land surface, which has a more complex process than the individual Ts or Ta. However, the spatiotemporal variations and influencing factors of DIF remain incomplete. The contribution of vegetation and soil moisture (SM) as key driving factors to DIF is not yet clear. Here, we analyzed the spatiotemporal variation patterns of DIF in China from 2011 to 2023 using in situ Ts and Ta data. A convergent cross-mapping method was employed to explore the causal relationship between SM, NDVI and DIF, and subsequently calculated the contribution of NDVI and SM variations to DIF under different climatic backgrounds. The results indicate that during the study period, DIF values were all above 0 °C and showed a significant increasing trend with a national mean slope of 0.02 °C/a. In general, vegetation and SM have a driving effect on DIF, with vegetation contributing more to DIF (0.11) than SM (0.08) under different surface properties. The background values of SM and temperature have a significant effect on the spatial and temporal distribution of DIF, as well as the correlation of vegetation and soil moisture to DIF. The study outcomes contribute to a better understanding of the coupling relationship between the land surface and atmosphere, which are also crucial for addressing climate change and ecological environmental management.

Wpływ zastosowania uzdatniaczów gleby na temperaturę i wilgotność środowiska glebowego

In cities, environmental and social impacts are increased every year due to high temperatures due to the heat island of the city. An effective struggle against the heat island of the city is the green infrastructure, where woody plants such as trees and shrubs play an irreplaceable role. Wood in the city is affected by a number of stress factors, especially high temperatures and lack of precipitation. In order for them to perform all their important functions and help reduce the negative impacts of the city's heat island, all requirements for their successful development and growth must be fulfilled. Soil conditions are an important factor affecting the condition of trees. Unsuitable soil conditions which include lack of water, compacted soil, result in insufficient development of the root system which directly affects the quality of the above-ground part of the plants. A possible treatment to improve the soil environment in the root zone of trees is the use of soil conditioners applied in the form of soil injection. The paper deals with the effect of soil conditioners (Hydrogel®, mycorrhizal mix Endomyk PROF + Trichoderma, and their combination) in the form of injection on the temperature and humidity conditions of the soil environment in a young plants of trees (species) Acer campestre L. in the city of Znojmo (South Moravia region). Plants without application of any preparation served as a Control variant. Before the actual application of conditioners MINILOG data loggers with temperature sensors and VIRRIB moisture sensors were placed in the soil which recorded changes in soil temperature and volumetric soil moisture in six-hour intervals every day in the period from February 2021 till December 2022. These changes were recorded in two profiles 0.1 - 0.4 and 0.4 - 0.7 meters. From the recorded results, it can be said that the application of soil conditioners had a significant effect on the temperature and humidity conditions of the soil environment. The variant with the soil conditioner Hydrogel® and the variant with the soil conditioner Hydrogel® in combination with the mycorrhizal mix in most cases show higher values of soil moisture and temperature compared to the Control variant. This trend is most evident in the soil profile of 0.4 - 0.7 m in the growing season.

The longest multiannual drought in Northeastern Brazil

The Northeastern region of Brazil has long history of drought with impacts on water supply for cities and agriculture. However, the last drought that occurred in the region had unprecedented magnitude and periodicity, making it the longest multiannual drought in Northeastern Brazil. Thus, understanding and characterizing the behavior of this phenomenon is necessary, considering that another episodes may occur. Thus, the objective of this study was to characterize the drought trend in the Northeastern region of Brazil, with the application of the Palmer Drought Severity Index (PDSI). The application of PDSI was carried out with the monthly calculation of the water balance coefficients referring to the soil water balance between the years 2000 and 2019, depending on the relative soil moisture values. The results obtained were submitted to the Mann-Kendall test to identify monthly trends in the index. The annual drought variability in Northeastern Brazil showed moderate to extreme intensity, with the worst drought being observed between the years 2012 and 2019. The monthly drought average shows the Mid-Northern and “Sertão” regions as the most critical, with monthly trend of significant drought severity. The positive trend was observed in the Eastern, Southern and Western coast regions of NEB.

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

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

Estimation of soil moisture of a high Andean wetland ecosystem (Bofedal) with geo-radar data and In-Situ measurements, Ayacucho - Peru

High Andean ecosystems within microbasins serve as crucial areas for water recharge, containing both surface and subsurface moisture. However, these ecosystems are currently under threat due to overgrazing, degradation, and the impacts of climate change. The objective is to validate the subsoil moisture of bofedal estimated using ground-penetrating radar (GPR) data in comparison to in-situ measurements obtained with a soil moisture meter (SMM) in the Apacheta microbasin of the Ayacucho region. The validation method involves comparing soil moisture values obtained with the SMM, with the estimated dielectric permittivity (DP) values from GPR surveys along four transects (T) in a bofedal. Reflected wave amplitude data are converted to DP values to identify water pockets (70<DP<81) and saturated soil moisture (10<DP<40). An analysis of the determination coefficient R2 and the Kappa index (κ) was conducted between both groups of bofedal subsoil moisture data along the four surveyed transects at depths ranging from 0 to 24 cm that contain water and saturated moisture. T1 contains a volume of 1,16m3 (47.85 %), T2 has 0.98m3 (46.6 %), T3 lacks water (40.8 %), and T4 holds 0.63m3 (31.45 %). The correlation of DP data with SMM for T1 (R2=0.801), T2 (R2=0.949), T3 (R2=0.837) y T4 (R2=0.842) implies that the SMM measurements significantly explain the estimated DP. Moreover, the kappa test demonstrated good agreement reliability between both observations made with GPR and SMM, with κ=0.763;[95%CI:0.471−1.055], indicating that the GPR method for measuring subsoil moisture is acceptable with an 87.5% confidence level.

IoT Based Smart Agriculture Using Machine Learning

Agriculture is one of the crucial sources of economic growth in the country. Agriculture plays a major role in increasing the overall economy of any country. Many countries are having tremendous growth in the production of crops as the demand for food grains and supply increases. India is one of the leading countries in producing a variety of different crops. However, most parts of India are still using the traditional methods for implementation and cultivation of crops, due to which farmers are facing a loss in their production due to inadequate supply of fertilizers and uncertain climatic conditions. To overcome this, there is a need to develop a system that will look for these soil, temperature, and climatic conditions. IoT-based Smart Agriculture using Machine Learning talks about the use of IoT and Machine Learning techniques that will not only look for sensor data but also recommend the farmers the suitable fertilizers and crops to be grown. Smart sensors will be used to get the values related to soil moisture, temperature, humidity, and pH values. These sensors will check for suitable soil conditions, and how wet or dry the soil is, and will also look for temperature values. The real-time data will be fetched and displayed on the live monitoring webpage and Android applications. We can get timely information about the soil moisture value and suitable temperature and humidity value. With the help of Data Analysis techniques, we can figure out which data to take for further exploration. Historical datasets that are publicly available can be used for training and building Machine Learning models. Farmers can access information about their field data, and machine learning algorithms will help the farmers in making informed decisions about what crops to grow in the specific environment. Keywords— Internet of Things, Sensors, Soil moisture, Temperature, Humidity, Microcontroller, Smart Agriculture, Recommendation Model, Crop Prediction, Fertilizers Prediction, Machine Learning Model.

The Verification and Fusion Analysis of Passive Microwave Soil Moisture Products in the Three Northeastern Provinces of China

The utilization of remote sensing soil moisture products in agricultural and hydrological studies is on the rise. Conducting a regional applicability analysis of these soil moisture products is essential as a preliminary step for their effective utilization. The triple collocation (TC) method enables the estimation of the standard deviation of errors in products when true soil moisture values are unavailable. It assesses data uncertainty and mitigates the influence of product errors on fusion, thereby enhancing product accuracy significantly. In this study, the TC uncertainty error analysis was employed to integrate Soil Moisture Active Passive (SMAP), the Advanced Microwave Scanning Radiometer 2 (AMSR-2), and the European Space Agency Climate Change Initiative (ESA CCI) active (ESA CCI A) and passive (ESA CCI P) products, with ground-based measurements serving as a reference. Traditional evaluation metrics, such as the correlation coefficient (R), bias, root mean square error (RMSE), and unin situed root mean square error (ubRMSE), were employed to evaluate the accuracy of the product. The findings indicate that SMAP and ESA CCI P products demonstrate strong spatiotemporal continuity within the research area and exhibit low uncertainty across various land types. The products derived from the Advanced Microwave Scanning Radiometer 2 (AMSR-2) exhibit a high level of temporal and spatial continuity; however, there is a requirement for enhancing their accuracy. The products of ESA CCI A exhibit notable spatiotemporal disjunction, contributing significantly to their elevated level of uncertainty. After fusion with TC analysis, the correlation coefficient (R = 0.7) of the TC-2 product derived from the fusion of SMAP, AMSR-2, and ESA CCI P products is significantly higher than the correlation coefficient of the TC-1 product (R = 0.65) obtained from the fusion of SMAP, AMSR-2, and ESA CCI A products at a 95% confidence level. The integration of data can efficiently mitigate the challenges associated with spatiotemporal gaps and inaccuracies in products, offering a dependable foundation for the subsequent utilization of remote sensing products.