Use In Precision Agriculture Research Articles

Progress and Potential Drawbacks of Modern Agricultural Technologies: A Literature Review

The production of agriculture has undergone new modifications as a result of agricultural technologies. These not only boost agricultural output but may also significantly raise the caliber of produced food, cut labor expenses, boost farmers' incomes, and achieve agricultural modernization. The use of precision agriculture (PA) is expanding due to the rapid socioeconomic changes that are occurring in certain developing nations. There are enormous ramifications for urbanization, energy consumption, and economic growth in certain developing nations when fundamental changes occur. The research status and current agricultural technology achievements are carefully summarized in this study. In-depth discussions of thirteen significant agricultural technologies are provided in this article. All significant technologies from developed countries are discussed so that under-developed and lower-developed countries will benefit from this paper. Finally, some fresh concepts for each technology are offered, and potential issues in establishing such sophisticated technologies are identified. The main objective of this review is to increase knowledge of modern agriculture and the development process in the agricultural field.

Enhancement of Comparative Assessment Approaches for Synthetic Aperture Radar (SAR) Vegetation Indices for Crop Monitoring and Identification—Khabarovsk Territory (Russia) Case Study

Crop identification at the field level using remote sensing data is a very important task. However, the use of multispectral data for the construction of vegetation indices is sometimes impossible or limited. For such situations, solutions based on the use of time series of synthetic aperture radar (SAR) indices are promising, eliminating the problems associated with cloudiness and providing an assessment of crop development characteristics during the growing season. We evaluated the use of time series of synthetic aperture radar (SAR) indices to characterize crop development during the growing season. The use of SAR imagery for crop identification addresses issues related to cloudiness. Therefore, it is important to choose the SAR index that is the most stable and has the lowest spatial variability throughout the growing season while being comparable to the normalized difference vegetation index (NDVI). The presented work is devoted to the study of these issues. In this study, the spatial variabilities of different SAR indices time series were compared for a single region for the first time to identify the most stable index for use in precision agriculture, including the in-field heterogeneity of crop sites, crop rotation control, mapping, and other tasks in various agricultural areas. Seventeen Sentinel-1B images of the southern part of the Khabarovsk Territory in the Russian Far East at a spatial resolution of 20 m and temporal resolution of 12 days for the period between 14 April 2021 and 1 November 2021 were obtained and processed to generate vertical–horizontal/vertical–vertical polarization (VH/VV), radar vegetation index (RVI), and dual polarimetric radar vegetation index (DpRVI) time series. NDVI time series were constructed from multispectral Sentinel-2 images using a cloud cover mask. The characteristics of time series maximums were calculated for different types of crops: soybean, oat, buckwheat, and timothy grass. The DpRVI index exhibited the highest stability, with coefficients of variation of the time series that were significantly lower than those for RVI and VH/VV. The main characteristics of the SAR and NDVI time series—the maximum values, the dates of the maximum values, and the variability of these indices—were compared. The variabilities of the maximum values and dates of maximum values for DpRVI were lower than for RVI and VH/VV, whereas the variabilities of the maximum values and the dates of maximum values were comparable for DpRVI and NDVI. On the basis of the DpRVI index, classifications were carried out using seven machine learning methods (fine tree, quadratic discriminant, Gaussian naïve Bayes, fine k nearest neighbors or KNN, random under-sampling boosting or RUSBoost, random forest, and support vector machine) for experimental sites covering a total area of 1009.8 ha. The quadratic discriminant method yielded the best results, with a pixel classification accuracy of approximately 82% and a kappa value of 0.67. Overall, 90% of soybean, 74.1% of oat, 68.9% of buckwheat, and 57.6% of timothy grass pixels were correctly classified. At the field level, 94% of the fields included in the test dataset were correctly classified. The paper results show that the DpRVI can be used in cases where the NDVI is limited, allowing for the monitoring of phenological development and crop mapping. The research results can be used in the south of Khabarovsk Territory and in neighboring territories.

A review of SLAM techniques and applications in unmanned aerial vehicles

Abstract Simultaneous Localisation and Mapping (SLAM) is a foundational idea in the field of robotics. It involves the processing of sensor signals and the optimisation of pose-graphs. SLAM has found several applications in various domains, including but not limited to courier services, agriculture, environmental monitoring, and military operations, particularly with the use of Unmanned Aerial Vehicles (UAVs). There exist several applications. This work aims to provide a comprehensive analysis of three Simultaneous Localization and Mapping (SLAM) algorithms, namely CNN-SLAM, Linearized Kalman Filter (LKF), and Extended Kalman Filter (EKF). Additionally, it will explore the utilisation of SLAM algorithms in Unmanned Aerial Vehicles (UAVs) by examining its use in precision agriculture, geological surveys, and Emergency Scenarios. This section will outline certain issues that SLAM algorithms may encounter in relation to wide area applications, real-time processing and efficiency, robustness, and dynamic objects within the environment. Ultimately, this study will undertake a comparative analysis of the merits and drawbacks associated with the three algorithms, while also putting up potential remedies for the aforementioned issues.

Prospects for the introduction of a green economy in the agricultural sector of Ukraine for the next 10 years

The relevance of the study is due to the deterioration of the environment and the low level of application of green technologies in the agricultural sector of Ukraine. The aim was to outline the current problems for farmers in the use of green farming methods and to find solutions. The study assessed the level of use of certain green technologies in agricultural production in Ukraine. The main results obtained in this work are: determining Ukraine’s place in the Green Future Index ranking in various areas during 2021-2023 and comparing it with Iceland, Finland, and Norway; identifying areas of green technologies that need to be developed in Ukraine during martial law and after the war (precision and organic farming, the introduction of biotechnology to create genetically modified organisms and the transition to alternative energy sources). The study of the dependence of farmers’ propensity to use green technologies on the size of farming confirms its undeniable presence. The analysis of foreign experience in precision agriculture allowed formulating recommendations for foreign companies entering the Ukrainian market to promote the use of precision agriculture in Ukraine. The findings on the growing electricity shortage helped to justify the further use of alternative energy sources in the context of the conflict. A study of the negative impact of the hostilities on Ukrainian land allowed classifying such types of impact and developing measures to restore the soil. The assessment of the use of technologies in agriculture helped to identify areas for the introduction of a green economy in the agricultural sector in Ukraine, which will help to increase agricultural yields without harming the environment and increase its competitiveness. The results and conclusions have practical implications for the Government in developing agricultural policy and for farmers in managing their farms

UAV-Based Vegetation Indices to Evaluate Coffee Crop Response after Transplanting Seedlings Grown in Different Containers

Brazil stands out among coffee-growing countries worldwide. The use of precision agriculture to monitor coffee plants after transplantation has become an important step in the coffee production chain. The objective of this study was to assess how coffee plants respond after transplanting seedlings grown in different containers, based on multispectral images acquired by Unmanned Aerial Vehicles (UAV). The study was conducted in Santo Antônio do Amparo, Minas Gerais, Brazil. The coffee plants were imaged by UAV, and their height, crown diameter, and chlorophyll content were measured in the field. The vegetation indices were compared to the field measurements through graphical and correlation analysis. According to the results, no significant differences were found between the studied variables. However, the area transplanted with seedlings grown in perforated bags showed a lower percentage of mortality than the treatment with root trainers (6.4% vs. 11.7%). Additionally, the vegetation indices, including normalized difference red-edge, normalized difference vegetation index, and canopy planar area calculated by vectorization (cm2), were strongly correlated with biophysical parameters. Linear models were successfully developed to predict biophysical parameters, such as the leaf area index. Moreover, UAV proved to be an effective tool for monitoring coffee using this approach.

XAI‐driven model for crop recommender system for use in precision agriculture

AbstractAgriculture serves as the predominant driver of a country's economy, constituting the largest share of the nation's manpower. Most farmers are facing a problem in choosing the most appropriate crop that can yield better based on the environmental conditions and make profits for them. As a consequence of this, there will be a notable decline in their overall productivity. Precision agriculture has effectively resolved the issues encountered by farmers. Today's farmers may benefit from what's known as precision agriculture. This method takes into account local climate, soil type, and past crop yields to determine which varieties will provide the best results. The explainable artificial intelligence (XAI) technique is used with radial basis functions neural network and spider monkey optimization to classify suitable crops based on the underlying soil and environmental conditions. The XAI technology would provide assets in better transparency of the prediction model on deciding the most suitable crops for their farms, taking into account a variety of geographical and operational criteria. The proposed model is assessed using standard metrics like precision, recall, accuracy, and F1‐score. In contrast to other cutting‐edge approaches discussed in this study, the model has shown fair performance with approximately 12% better accuracy than the other models considered in the current study. Similarly, precision has improvised by 10%, recall by 11%, and F1‐score by 10%.

Evaluation of multispectral data for recent manure application: A case study in northern Spain

The use of manure in agricultural fields during the wet season can lead to environmental pollution by releasing nitrates into nearby water sources. To address this issue, authorities may impose closed periods during which manure application is prohibited. However, ensuring compliance with these regulations can be challenging, as it is difficult to monitor all fields in a country. To tackle this problem, a solution has been proposed that involves employing machine learning techniques in conjunction with satellite imagery to automatically identify freshly manured fields. This paper investigates the relationship and effectiveness of the Sentinel-2 satellite bands and 51 frequently utilized multispectral indices in the context of precision agriculture, by exploring different feature selection methods. The proposed method achieves nearly 90% F1-Score and detects all test plots of the northern Spanish region, showing its potential for large-scale use in precision agriculture and environmental monitoring. This method incorporates temporal data, resulting in an 8% improvement in the detection F1-Score. Despite their lower spatial resolution, infrared bands have proven to be more effective than visible bands, enhancing the F1-Score by 4%. Furthermore, the use of over 80 features contributes to a 12% increase in the F1-Score compared to using fewer than 10 features. For further research and future studies, a dataset of recently manured plots, verified on-site, has been developed and made publicly available.

The disposition of data from precision agricultural technologies: What do young agriculturalists think?

The absence of a legal and regulatory framework protecting a farmer's right to control data generated from their use of precision agriculture may impede adoption of the technology. Consideration of preferences about the disposition of this data is therefore critical to the broader discussion about the growth of precision agricultural technologies and its impact on society. Using a choice experiment, we show that factors contributing to willingness to enroll in a data management contract with a precision agriculture service provider include discount received, whether data ownership rights are retained, data privacy guarantees, and whether the data is transferred manually or automatically between systems.

Enhancing Precision Agriculture and Environmental Monitoring Using Proximal Remote Sensing

Proximal remote sensing is a cutting-edge technology that has emerged as a powerful tool in precision agriculture and environmental monitoring. By capturing high-resolution data from a close range, it provides valuable insights into crop health, soil conditions, and ecosystem dynamics. This paper explores the applications, advantages, and limitations of proximal remote sensing, focusing on its use in precision agriculture and environmental management. The applications of proximal remote sensing in precision agriculture include crop monitoring, disease detection, and resource optimization. In environmental management, it aids in habitat mapping, biodiversity assessment, and environmental impact analysis. The advantages of proximal remote sensing lie in its high spatial resolution, real-time data acquisition, and flexibility in sensor selection. However, limitations such as limited coverage area and skill requirements need to be considered. The future perspectives of proximal remote sensing encompass advancements in sensor technology, automation, integration with other technologies, and enhanced data storage and analysis. By leveraging these advancements, proximal remote sensing can contribute to more sustainable practices and informed decision-making for a better and resilient future.

ВИЗНАЧЕННЯ ПОТОКОВОЇ ЩІЛЬНОСТІ ҐРУНТУ

The article is focused on the determination of the nonlinear relationships between soil compaction, density, and water content. It was found that these properties can be described by the second-order models and used for improving devices for the on-stream soil density measuring. The models for determining the density of loamy soil (at a water content of 20%) in the range from 0.9 to 1.6 g/cm3 with an extremum of 1.35 g/cm3 were improved. A device for the on-stream soil density measuring is proposed. The device operates within the soil compaction range from 0.3 to 1.2 MPa and water content from 10 to 30% at the angle of inclination of the kinematic link  from 15 to 40 degrees. The obtained results can be used in the adaptation of the proposed device for use in precision agriculture.

Exploring the relationship between information and communication technology (ICT) and productivity: Evidence from Australian farms

AbstractThe relationship between information and communication technology (ICT) and farm productivity remains unresolved and often debated with limited evidence. While ICT is generally accepted by many to be a positive driver of productivity, others question it. Realistically, truth is likely somewhere in between. Certain ICT investments are likely to facilitate productivity improvement, whereas others may offer some other benefits such as improved safety or reduced emissions. It is also undeniable that some ICT investments may fail or offer little more than a temporary novelty. Using a sample of Australian farm‐level data, analysis in this paper finds a positive relationship between ICT investment and productivity. Specifically, the use of precision agriculture and machinery infused with ICT (such as GPS autosteering tractors) is found to be statistically significant. Moreover, digital internet access or access to the National Broadband Network (NBN) is found to be beneficial—and conversely, farms that reported mobile and internet connectivity problems tended to achieve lower productivity.

A Review on Advances in Agriculture and Agroforestry with GPS and GIS

Various researchers, agronomists, scientists, and engineers utilize a variety of technologies every year to boost agricultural productivity at a low cost, but this has a negative influence on the environment. Precision agriculture is the study of the use of technology to enhance agricultural operations in comparison to conventional agricultural methods and lessen negative environmental impacts. Precision agriculture depends heavily on remote sensing technology, and this technology's use in precision agriculture opens up new possibilities for raising agricultural standards. The global positioning system (GPS) enables the geographic Latitude and Longitude data of field data (slope, aspect, nutrients, and yield). Since it has the ability to continuously determine and record the right position, it can build a bigger database for the user. A geographic information system (GIS) that can handle and store these data is needed for further investigation. Despite agroforestry's limited spatial extent, isolation, and higher functional and structural complexity, recent advancements in geospatial technologies, as well as the free accessibility of spatial information and software, can provide additional insight into assessing tools, making the decisions, and developing policies. This review has covered the current uses of geospatial technology, along with their restrictions and limits, as well as prospective future uses for agroforestry. This review discusses GPS, GIS, and remote sensing technology and explains how they might be used in precision agriculture and agroforestry.

Automation irrigation system using arduino for smart crop field productivity

Agriculture is essential to the prosperity of agricultural countries like India. Thus, the suggested strategy is to use automation and internet of thing (IoT) technology to make agriculture smart. Applications enabled by the IoTs include irrigation decision assistance, crop growth monitoring and selection, and more. an Arduino-powered technology that boosts agricultural productivity. This study's main goal is to find the least quantity of water necessary to grow crops. Most farmers squander a lot of time on the fields rather than concentrating on the water that plants have access to at the right moment. The suggested system determines the required amount of water based on the data obtained from the sensors. Two sensors provide data on the soil's temperature, humidity, amount of sunlight each day, and soil temperature to the base station. The suggested systems must determine the amount of water required for irrigation based on these criteria. The system's main benefit is the use of precision agriculture (PA) in conjunction with cloud computing, which will maximise the use of water fertilisers while maximising crop yields and also assist in determining field weather conditions.

Retrieval of Harmonized LAI Product of Agricultural Crops from Landsat OLI and Sentinel-2 MSI Time Series

In this study, an approach for the harmonized calculation of the Leaf Area Indices (LAIs) for agronomic crops from Sentinel-2 MSI and Landsat OLI multispectral satellite data is proposed in order to obtain a dense seasonal trajectory. It was developed and tested on dominant crops grown in the Czech Republic, including winter wheat, spring barley, winter rapeseed, alfalfa, sugar beet, and corn. The two-step procedure harmonizing Sentinel-2 MSI and Landsat OLI spectral data began with deriving NDVI, MSAVI, and NDWI_1610 vegetation indices (VIs) as proxy indicators of green biomass and foliage water content, the parameters contributing most to a stand’s spectral response. Second, a simple linear transformation was applied to the resulting VI values. The regression model itself was built on an artificial neural network, then trained on PROSAIL simulations data. The LAI estimates were validated using an extensive dataset of in situ measurements collected during 2017 and 2018 in the lowlands of the Central Bohemia Region. Very strong agreement was observed between LAI estimates from both Sentinel-2 MSI and Landsat OLI data and independent ground-based measurements (r between 0.7 and 0.98). Very good results were also achieved in the mutual comparison of Sentinel-2 and Landsat-based LAI datasets (rRMSE < 20%, r between 0.75 and 0.99). Using data from all currently available Sentinel-2 (A/B) and Landsat (8/9) satellites, a dense harmonized LAI time series can be created with high potential for use in precision agriculture.

Protecting farmers' data privacy and confidentiality: Recommendations and considerations

With the increasing use of precision agriculture and technological development, the agricultural sector has been majorly transformed. Precision agriculture uses technological innovations such as sensors, drones, and data analysis tools to improve the productivity of resources and management decisions on the farm. Since these technologies collect a large amount of data related to the farm, the farmers are concerned about the privacy of their data. The farmers are worried about unauthorized access, collection, and sharing of their data with third parties by the agricultural technology providers (ATPs). Furthermore, the ambiguity of agreements and legal frameworks around data collection, processing, and sharing may result in uncertainty in data privacy practices. Furthermore, this situation is aggravated by a lack of adoption of best practices and standards for farm data protection. Violation of privacy can cause reluctance among farmers to adopt new technologies which can negatively impact various stakeholders, government, and public. Protecting farmers' privacy and respecting their rights related to the collected data should be addressed collectively by the actors in the farming ecosystem, including farmers, agricultural technology providers, governments, and supply chain stakeholders. This paper aims at providing recommendations on how to minimize privacy risks and concerns for farmers and reviews some of the data governance best practices for data protection.

Extraction of reflectance spectra features for estimation of surface, subsurface, and profile soil properties

• Accuracy of reflectance spectroscopy estimates of soil properties varied by depth. • Number and location of important wavebands varied by depth and by soil property. • Some calibrations could be applied successfully across multiple depth intervals. • Results could guide development of discrete-waveband soil property sensors. Diffuse reflectance spectroscopy in the visible and near-infrared wavelength ranges has potential to provide high-resolution, pollution-free, and nondestructive estimation of soil chemical and physical properties for use in precision agriculture. Practical implementation of this approach would be facilitated if soil property sensors using a limited number of reflectance bands could maintain accuracy similar to more expensive and complex full-spectrum sensors. Studies identifying such bands are limited, especially for subsurface soils. Thus, in this study, an existing spectral database of 697 soil samples was used to compare results for three soil categories (profile, surface, and subsurface) and multiple waveband selection methods. Soil cores were obtained to approximately 1.2 m depth from ten fields, two each in Missouri, Illinois, Michigan, South Dakota, and Iowa, USA, then sieved and air-dried. Laboratory soil spectra were obtained from 350 to 2500 nm using a commercial spectrometer and soil properties (total nitrogen, soil organic carbon, total carbon, magnesium, calcium, potassium, soil texture (clay, silt, and sand) fractions, cation exchange capacity, and pH) were measured using standard laboratory analyses. The ability of ten spectral preprocessing techniques to improve analysis results was investigated. Backward interval partial least squares was used to identify those spectral regions most predictive of soil properties. Alternatively, specific characteristic wavelengths were identified by a combination genetic algorithm (GA)-back propagation neural network (BPNN) approach. Results were compared for three soil property estimation methods: (1) partial least squares regression (PLSR) models based on the full spectrum, (2) PLSR models based on sensitive regions, and (3) BPNN models based on characteristic wavelengths. The best results for profile and subsurface soils were obtained with absorbance preprocessing, but for the surface soils, the standard normal variate transformation was best. For some soil properties, the prediction R 2 of the PLSR models based on sensitive regions was better than that of the PLSR models based on the full spectrum, demonstrating that retaining only sensitive wavebands could improve estimates. However, in some cases, the reduction in wavebands decreased accuracy. Differences in prediction accuracy across all calibration models over profile and subsurface soils were relatively small but were larger for surface soils. Furthermore, application of characteristic wavelength calibrations to other soil datasets resulted in a lower accuracy than with the full spectrum calibration developed for that dataset. In general, this study shows that there are measurable differences in prediction accuracy across all calibration models over the three soil depth categories. The experimental results of this study illustrate the potential for a set of wavelengths optimized for one depth category to still provide acceptable estimates for other depth categories. Overall, these results provide important guidance for the development of DRS soil sensors based on discrete wavebands to reduce cost and increase the speed of in-field data collection.

Trajectory Tracking of Autonomous Driving Tricycle Robot with Fuzzy Control

Autonomous-driving mobile robots are currently the subject of extensive studies. Trajectory tracking, one of the most critical tasks in mobile robotics, has found many applications in industry, transportation, and other fields. The present work focuses on application of autonomous driving of tricycle robots for use in Precision Agriculture (PA). Employment of autonomous robots in agriculture helps to optimize inputs and increase cropping management efficiency while protecting resources and environment. This paper aims to develop a fuzzy control method for guidance of a tricycle robot according to a trajectory prescription. Firstly, a kinematic model of a tricycle robot was designed and implemented using a fuzzy logic algorithm for control of the robot navigation. Finally, the model has been tested in MATLAB Simulink to show its performance in terms of accuracy and efficiency. Results showed that is possible to maintain the tracking error at 0.2 m by carrying out a straight-line displacement of the robot.

Land Clearing, Preparation and Drone Monitoring using Red-Green-Blue (RGB) and Thermal Imagery for Smart Durian Orchard Management Project

Malaysia's agricultural activity is progressing toward the use of precision agriculture and Internet of Things (IoT) technology as the country moves forward with the adoption of Industrial Revolution 4.0. As a result, a novel approach based on spatial and thermal analysis is proposed and presented, along with a low-cost thermal and photogrammetric modelling workflow. As a result, the purpose of this project is to monitor durian orchards using drones equipped with thermal and red-green-blue (RGB) digital cameras. Thermal maps are presented to demonstrate the relationship between vegetation and soil temperature, and digital imagery from an early stage of preparation in the orchard is analysed for overall management system improvement. The findings indicate that the uneven ground surface of the orchard may have an effect on the irrigation system and crop yield production. Thermal imaging also reveals variations in soil temperature that are related to soil humidity. A well-managed orchard is essential for ensuring that the crop grows in a healthy state. The data acquisition and processing part of this workflow are being tested in Alor Gajah Smart Durian Orchard Management Project, Melaka, Malaysia.

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

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

Environmental Effects on Brightness Temperature Observation From an L-Band Radiometer

Measuring the microwave brightness temperature (TB) using a radiometer is important for estimating soil moisture (SM). However, no study has demonstrated the effect of environmental conditions on these measurements. With this technology being explored for use in precision agriculture, indoor application and/or utilization in environments close to buildings becomes a certain reality. Therefore, an experiment was conducted in a warehouse of concrete and steel construction to investigate this issue. An L-band microwave radiometer known as ELBARA III was used to measure the surface TB over a soil box at different incidence angles and moisture contents. An environmental correction equation was applied to offset the effect of the indoor environment on the TB observed by the sensor. Accordingly, the effect of the TB environment on the TB observations at different incidence angles and moisture contents were analyzed. The environment correction equation provided a substantial improvement in estimating the direct TB emitted from the soil relative to model estimates with a reduction in root-mean-squared error (RMSE) from 57 K to 4 K. Overall, the results demonstrated that the built environment had a substantial influence on the TB observed by the sensor, and that it was not possible to directly use indoor measurements for reliable SM retrieval. Use of the environment correction equation offers inspiration for SM retrieval from indoor measurements, but further studies are required. This result provides an early glimpse into the ability of microwave radiometers for SM monitoring in indoor environments.