The paper introduces a novel approach for estimating soil moisture in vegetated surfaces, specifically focusing on sugarcane crops throughout various growth stages in agriculture applications. While existing models typically address bare soil scenarios, this model utilizes data from P-, L-, and C-band Synthetic Aperture Radar (SAR) to estimate soil moisture. The semi-empirical Dubois model forms the basis of the proposed model, which has been adapted to accommodate multiband operation and crop height variations. Synthetic datasets are generated using the adjusted model to train two neural networks incorporated into the overall model. Additionally, a linear expression for estimating crop height is integrated into the model. The model is validated in an Experimental Site at the School of Agricultural Engineering, UNICAMP, and an independent area at the Sugarcane Technology Center in Piracicaba, Brazil. The model utilizes a multiband drone-borne SAR system with a 3-meter image resolution and radiometric accuracy of 0.5 dB. The results indicate that the model can estimate soil moisture with root-mean-square errors of 0.05 cm3.cm-3 (5 vol.

The detection and characterization of beaver burrows are essential for effective humanbeaver coexistence management. Currently, most burrow identification relies on visual observations, often conducted from a canoe or kayak. While sonar and ground-penetrating radar offer certain advantages, their effectiveness is limited by restricted accessibility, dense vegetation, obscured entrances, and time constraints. This study explores the potential of Synthetic Aperture Radar (SAR) integrated with Uncrewed Aircraft Systems (UAS) as an alternative detection method. The proposed methodology employs P-band SAR data acquired via a drone-borne system, leveraging convolutional neural networks (CNNs) and tomographic SAR imaging for enhanced burrow detection and classification. The SAR survey, conducted using helical flight trajectories, achieved detection rates of 100% and 80% for two burrow classes, with a false alarm rate of 12%.

The detection of buried bodies, whether human remains or other volumetric objects, is an intricate and resource-intensive process without the aid of remote sensing technologies. This study utilizes a drone-borne Synthetic Aperture Radar (SAR) system, which follows a helical flight path and applies SAR tomographic processing to identify subsurface objects with distinct reflectivity. A series of experiments were conducted to detect and classify various buried objects at depths of up to 1 meter, integrating SAR imaging with machine learning techniques. The results highlight the strong operational potential of this approach, achieving a 90% detection rate, a 0% false alarm rate, and a 7% classification error, largely attributed to the limited amount of training data.

The synthetic aperture radar (SAR) has long been used from satellites for forest monitoring at global level. The boreal forests in Sweden are well described wall-to-wall from airborne laser scanning and airborne photography. Hence, in Sweden, SAR can often only provide a limited added value, due to the low resolution compared to other sensors, despite its all-weather acquisition capabilities. By accounting for interfering effects that currently degrade the useful information in SAR images, it can be extremely valuable for both vegetation mapping and belowground mapping (e.g., soil conditions and tree roots). In the current work, we present the configuration of the first drone-based SAR experiment that allows us to image the forest in 3D with very high spatial resolution. We have started the analyses by using tomography to derive reflectivity for the roots of single trees, and comparing these with reference root biomass. The linear relationship indicates a potential for using SAR to derive forest variables that were yet neglected or little researched. Moreover, extensive additional remote sensing data have been collected from both airborne and spaceborne platforms, and reference data for both the vegetation and soil have been inventoried in-situ using complementary measurements and sensors. Hence, this unique experimental setup enables many unprecedented analyses about SAR applied to boreal forests.

Leaf-cutting ants, notorious for causing defoliation in commercial forest plantations, significantly contribute to biomass and productivity losses, impacting forest producers in Brazil. These ants construct complex underground nests, highlighting the need for advanced monitoring tools to extract subsurface information across large areas. Synthetic Aperture Radar (SAR) systems provide a powerful solution for this challenge. This study presents the results of electromagnetic simulations designed to detect leaf-cutting ant nests in industrial forests. The simulations modeled nests with 6 to 100 underground chambers, offering insights into their radar signatures. Following these simulations, a field study was conducted using a drone-borne SAR operating in the P-band. A helical flight pattern was employed to generate high-resolution ground tomography of a commercial eucalyptus forest. A convolutional neural network (CNN) was implemented to detect ant nests and estimate their sizes from tomographic data, delivering remarkable results. The method achieved an ant nest detection accuracy of 100%, a false alarm rate of 0%, and an average error of 21% in size estimation. These outcomes highlight the transformative potential of integrating Synthetic Aperture Radar (SAR) systems with machine learning to enhance monitoring and management practices in commercial forestry.

The Synthetic Aperture Radar operating in P-band and performing a helical flight pattern promises to be a potential tool for underground tomography, featuring high resolution and penetration. Based on the success of detecting ant nests beneath the industrial forest, a new experiment was carried out to detect buried bodies. Two sets of flights were performed and validated. Six bodies buried 0.5 m to 1.5 m deep, containing the remains of cow and pork, were located with a detection rate of 100% and a false alarm rate of 0%. Also, the ability to detect change across two sets of flights was verified by locating a bucket filled up with a large tarpaulin piece in the first set of flights.

The presence of leaf-cutting Acromyrmerx ants in industrial forest plantations is one of the main causes of the loss of biomass and productivity that affect the large pulp producers in Brazil. Thus, the development of monitoring tools that allow the extraction of information below the surface in large areas, such as Synthetic Aperture Radar (SAR) systems, is of crucial importance. This work presents a set of unprecedented electromagnetic simulations for the detection of Acromyrmex ant nests with 1 up to 13 chambers in industrial forests. Ant nests in bare soil and under forest cases were considered for simulations, obtaining promising results that can be used in subsequent tests with real data based on SAR circular and helical survey.

Drone-borne synthetic aperture radar (SAR) is becoming a powerful remote sensing tool for subsoil tomography. Two challenging aspects need to be considered: the development of processing techniques for complex subsoil tomography tasks and the reduction in volume and weight of the SAR system. These challenges will be addressed through two key applications. The first aspect will be exemplified by describing the identification of ant nests in the subsoil of an industrial forest using an electromagnetic-based P-band SAR approach. The second will be tackled by presenting the optimized design of an L-band polarimetric conformal antenna.

In synthetic aperture radar (SAR) imaging, the goal of subsurface tomography is to create detailed images of objects and structures underground. In order to obtain well-focused images, the refraction effect must be taken into account during SAR processing. This work proposes an adjusted back-projection algorithm and validates it with simulation and experimental results. The simulation model constitutes an underground air cavity and a multi-circular flight path. The experimental scenario includes a buried quad-corner reflector and an unmanned aerial vehicle (UAV) performing a spiral flight path. Both cases produced well-focused 3D images with the correct positioning of the targets.

Defoliation by leaf-cutting ants in commercial forest plantations is one of the leading causes of biomass and productivity losses affecting all of Brazilian industrial forest. Thus, the development of monitoring tools that allow extracting the information below the surface in large areas, such as synthetic aperture radar (SAR) systems, is crucial. This work presents a method for ant nest size estimation in industrial forest based on SAR images. A field study is carried out using a drone-borne SAR system to survey a commercial eucalyptus forest by using a helical flight pattern and P band transmitting frequency and finally generating a ground tomography. A convolutional neural network (CNN) is employed for the ant nests size estimation from the tomograms. A mean error of 5 % and 21 % was achieved for a training a validation dataset, respectively.

Tomography using drone-borne synthetic aperture radar (SAR) has proven to be very useful and efficient in surveying the subsurface of areas with gravimetric soil moisture ranging from 0 to 50%. The drone-borne SAR, along with the back-projection processing algorithms, and the proof of concept through the detection of a corner reflector buried 1.7 meters deep in clayey soil, are presented. Anthills with areas ranging from 0.05 m2 to 120 m2 were detected under an industrial eucalyptus forest, with a detection probability of more than 80% in two different regions.

The presence of beaver burrows, primarily found on riverbanks, poses significant risks due to the increased probability of subsidence, which can have a detrimental impact on infrastructures such as railways, bridges, flood embankments, and footpaths. Currently, identifying these structures is a complex and tedious job because they are natural underground formations, located in hard-to-reach areas. Alternative methods such as remote sensing based on Synthetic Aperture Radar (SAR) systems, offer great promise option for burrow detection. This work presents a set of software-based electromagnetic simulations of radar signals to evaluate the effectiveness of SAR technology to detect different beaver burrow formations. Beaver burrows in bare soil cases with varying dielectric constants were considered, achieving results that encourage us to test the beaver burrow detection with real data from SAR drone-borne survey.

This article reports two scenarios to demonstrate the subsurface survey capability for high resolution tomographic survey through a drone-borne tri-band synthetic aperture radar (SAR). In the first scenario, a linear flight survey was carried out for the identification of a leaf-cutting ant nest. A reflectivity reduction of up to 8 dB was obtained in the nest area, demonstrating the penetration capability of the SAR system. Additionally, electromagnetic simulations were carried out to better understand nest effects in SAR survey and also presented the same reduction. The second scenario verified the theoretical λ/4 resolution obtained by circular flight surveys. In this case, a quad-corner reflector was analyzed by performing a helical flight, where results very close to the theoretical one were obtained.

ABSTRACT Action planning and decision-making in the sugarcane management chain depend on yield estimates, which, in turn, vary with the soil. This study aimed to describe an applicable method of classifying sugarcane productivity into three categories, based on soil properties (medium, low, and high), determining which is most associated with biomass production. To this end, we applied the machine learning methods Naïve Bayes, Decision Trees, and Random Forest, as they proved to be useful tools for faster and more accurate results. Our results indicate that Random Forest is the most suitable for classifying all yield categories, and Naïve Bayes had good results for classification into “medium” and “low” and potential for solving multiclass problems in agriculture. Organic matter was the property most closely related to sugarcane biomass yield by the Random Forest and Decision Trees algorithms. The methods described can be used to obtain subsidies for sugarcane chain management, contributing to more sustainable decisions.

This article presents a novel method for predicting the sugarcane harvesting date and productivity using a three-band imaging radar. Taking advantage of working with a multi-band radar, this system was employed to estimate the above-ground biomass (AGB), achieving a root-mean-square error (RMSE) of 2 kg m−2 in sugarcane crops, which is an unprecedented result compared with other works based on the Synthetic Aperture Radar (SAR) system. By correlating the field measurements of the ripening index (RI) with the AGB measurements by radar, an indirect estimate of the RI by the radar is obtained. It is observed that the AGB reaches its maximum approximately 280 days after planting and the maximum RI, which defines the harvesting date, approximately 360 days after planting for the species IACSP97-4039. Starting from an AGB map collected by the radar, it is then possible to predict the harvesting date and the corresponding productivity with competitive average errors of 8 days and 10.7%, respectively, with three months in advance, whereas typical methods employed on a test site achieve an average error of 30 days with three months in advance. To the best of our knowledge, it is the first time that a multi-band radar is employed for productivity prediction in sugarcane crops.

The presence of leaf-cutting ants in commercial forest plantations is one of the main causes of the loss of biomass and productivity that affect the large pulp producers in Brazil. Thus, the development of monitoring tools that allow the extraction of information below the surface in large areas, such as SAR systems, is of crucial importance. This work presents a set of unprecedented electromagnetic simulations for the detection of leaf-cutting ant nests with 6 up to 385 chambers in industrial forests. The different tests range from the one-signal case to a tomographic processing based on SAR imaging, obtaining promising results that can be used in subsequent tests with real data based on SAR mapping.

This work presents a remote sensing solution for sugarcane precision agriculture based on a drone-borne differential interferometric synthetic aperture radar (DlnSAR) operating in the P-, L-, and C-bands. With one flight pass, the system can estimate the soil moisture, the plantation height, and the above-ground biomass map; and predict the harvest date and the respective productivity. With two flight passes, it assesses the crop growth via differential interferometry. A new methodology dedicated to sugarcane plantation was developed based on the existing methodologies for soil moisture and biomass measurement. The image information from the three bands, plus the C-band InSAR and P-band DInSAR information, show immense potential for efficient and low-cost monitoring. The results validated the methodology in a large sugarcane mill.

Accurate, high-resolution maps of for crop growth monitoring are strongly needed by precision agriculture. The information source for such maps has been supplied by satellite-borne radars and optical sensors, and airborne and drone-borne optical sensors. This article presents a novel methodology for obtaining growth deficit maps with an accuracy down to 5 cm and a spatial resolution of 1 m, using differential synthetic aperture radar interferometry (DInSAR). Results are presented with measurements of a drone-borne DInSAR operating in three bands-P, L and C. The decorrelation time of L-band for coffee, sugar cane and corn, and the feasibility for growth deficit maps generation are discussed. A model is presented for evaluating the growth deficit of a corn crop in L-band, starting with 50 cm height. This work shows that the drone-borne DInSAR has potential as a complementary tool for precision agriculture.

This paper presents a high-accuracy single-pass drone-borne Interferometric Synthetic Aperture Radar System in the P-band (P-InSAR) for forest inventory, where ground and canopy heights are accurately determined. Full penetration is proven for a eucalyptus forest with a tree spacing of 2.5 m by 3.0 m, and the measured digital terrain accuracy is compared with well-known statistical models. Combining a C-band single-pass InSAR with P-InSAR, forest height is estimated with 5 % accuracy for forest inventory. Both surface and digital ground models are presented and compared with ground truth measurements.