Agriculture provides for the most basic needs of humankind: food and fiber. The introduction of new farming techniques in the past century (e.g., during the Green Revolution) has helped agriculture keep pace with growing demands for food and other agricultural products. However, further increases in food demand, a growing population, and rising income levels are likely to put additional strain on natural resources. With growing recognition of the negative impacts of agriculture on the environment, new techniques and approaches should be able to meet future food demands while maintaining or reducing the environmental footprint of agriculture. Emerging technologies, such as geospatial technologies, Internet of Things (IoT), Big Data analysis, and artificial intelligence (AI), could be utilized to make informed management decisions aimed to increase crop production. Precision agriculture (PA) entails the application of a suite of such technologies to optimize agricultural inputs to increase agricultural production and reduce input losses. Use of remote sensing technologies for PA has increased rapidly during the past few decades. The unprecedented availability of high resolution (spatial, spectral and temporal) satellite images has promoted the use of remote sensing in many PA applications, including crop monitoring, irrigation management, nutrient application, disease and pest management, and yield prediction. In this paper, we provide an overview of remote sensing systems, techniques, and vegetation indices along with their recent (2015–2020) applications in PA. Remote-sensing-based PA technologies such as variable fertilizer rate application technology in Green Seeker and Crop Circle have already been incorporated in commercial agriculture. Use of unmanned aerial vehicles (UAVs) has increased tremendously during the last decade due to their cost-effectiveness and flexibility in obtaining the high-resolution (cm-scale) images needed for PA applications. At the same time, the availability of a large amount of satellite data has prompted researchers to explore advanced data storage and processing techniques such as cloud computing and machine learning. Given the complexity of image processing and the amount of technical knowledge and expertise needed, it is critical to explore and develop a simple yet reliable workflow for the real-time application of remote sensing in PA. Development of accurate yet easy to use, user-friendly systems is likely to result in broader adoption of remote sensing technologies in commercial and non-commercial PA applications.

Applications of Remote Sensing in Precision Agriculture: A Review

Polarimetric Phased-Array-type L-band Synthetic Aperture Radar (PALSAR) system parameters are assessed using data sets collected over several calibration sites. The data collected over the Amazonian rain forest help validate the hypothesis that Faraday rotation is zero near the equator. The analysis of the Amazonian forest data and the response of the corner reflectors deployed there during the PALSAR acquisitions lead to the conclusion that the antenna is highly isolated (better than -35 dB). These results are confirmed using the data collected over calibration sites located in Sweden and Canada. The 5-m-height corner reflector deployed at the Remningstorp (Sweden) calibration site by Chalmers University of Technology provides accurate measurement of antenna parameters and detection of a (2deg -3deg) Faraday rotation during day-time acquisitions, whereas no Faraday rotation was noted during night-time acquisitions. Small Faraday rotation angles (2deg-3deg) have been measured using acquisitions over the DLR Oberpfaffenhofen and the Ottawa calibration sites. The presence of small but still significant Faraday rotation (2deg-3deg) induces a corner reflector return at the cross-polarization HV and VH that should not be interpreted as the actual antenna crosstalk. These results show that the PALSAR antenna is highly isolated (better than -35 dB), and diagonal antenna distortion matrices (with zero crosstalk terms) can be used for accurate calibration of PALSAR polarimetric data.

Polarimetric Palsar Calibration

The perspective of unmanned aerial systems in forest management: Do we really need such details?

We address here the issue of jointly estimating the angle parameters of a single sinusoid with Wiener carrier phase noise and observed in additive, white, Gaussian noise (AWGN). We develop the theoretical foundation for time-domain, phase-based, joint maximum likelihood (ML) estimation of the unknown carrier frequency and the initial carrier phase, with simultaneous maximum a posteriori probability (MAP) estimation of the time-varying carrier phase noise. The derivation is based on the amplitude and phase-form of the noisy received signal model together with the use of the best, linearized, additive observation phase noise model due to AWGN. Our newly derived estimators are closed-form expressions, consisting of both the phase and the magnitude of all the received signal samples. More importantly, they all have a low-complexity, sample-by-sample iterative processing structure, which can be implemented iteratively in real-time. As a basis for comparison, the Cramer-Rao lower bound (CRLB) for the ML estimators and the Bayesian CRLB (BCRLB) for the MAP estimator are derived in the presence of carrier phase noise, and the results simply depend on the signal-to-noise ratio (SNR), the observation length and the phase noise variance. It is theoretically shown that the estimates obtained are unbiased, and the mean-square error (MSE) of the estimators attain the CRLB/BCRLB at high SNR. The MSE performance as a function of the SNR, the observation length and the phase noise variance is verified using Monte Carlo simulation, which shows a remarkable improvement in estimation accuracy in large phase noise.

Joint ML/MAP Estimation of the Frequency and Phase of a Single Sinusoid With Wiener Carrier Phase Noise

The paper presents a method for the estimation of the complex channels transmission coefficients of an airborne radar array with small number of antennas. It uses a signal reflected from the earth’s surface when the antenna is stationary or from an artificial reflector. The signal processing takes into account the channels mutual coupling in all phase states. To estimate the complex gains, a phase modulator is used in each channel of the receiving and transmitting arrays. The signal in the channels is modulated by an ensemble of orthogonal sequences. The estimation of the complex gains (amplitudes and phases) is carried out by the calculating of the correlation function the signal of each channel.

Antenna Array Complex Channel Gain Estimation Using Phase Modulators

The scope of application of imaging radars is constantly growing and now starts to cover agricultural field. Hence, there is a growing demand for mobile system that can deliver all of the advantages of a radar remote sensing system. The main goal of the system described in this article is to provide farmers with tools for all-weather monitoring of natural and anthropogenic objects: control of vegetation, control of soil moisture etc. Authors is currently taking part in ongoing research into creation of such system. This article describes general setup of the system and algorithms based on which such system will work.

UAV synthetic aperture radar system for control of vegetation and soil moisture

Advanced method of inner calibration of individual receive modules of active phased antenna arrays (APAA) in synthetic aperture radar (SAR) is discussed. This method allows fast and accuracy amplitude and phase characterization of every receive modules and uses pseudo-noise sequences as core of calibrating sequences system. Decorrelation method of cancelling mutual interference using simple matrix algebra is introduced. Its limitations due to strict timing synchronization requirements is described too. Some computing considerations of this method are discussed. Characterizations results are developed through simulation in the case of different types of receive modules failures.

https://ieeexplore.ieee.org/iel7/7442935/7448099/07448212.pdf

Approach to use pseudo-noise sequences in inner calibration system of active phased antenna arrays

SAR image quality depends on the RF frontend parts characteristics including PLL stability parameters. Estimation of PLL stability most convenient with the phase noise characteristic. In this research simulation of the RF frontend PLL system was performed. Phase noise influence was presented as random frequency addition to signal samples. According to results of this research the expressions for the depending of the SAR image quality of the RF frontend PLL Phase noise level was obtained. Simulation results and obtained expressions are in good agreement with the SAR prototype experimental results. The research results can decrease laboriousness of SAR RF frontend designing, adjustment and diagnostics.

The study of RF PLL phase noise influence to SAR image quality

Fast method of inner calibration of individual channel of active phased antenna arrays (APAA) has been introduced earlier. This method can be used for measuring complex amplitude characteristics of individual channels of both receive and transmit APAA using single measurement procedure. However, this method returns complex amplitude characteristics only at single carrier frequency; for wideband systems these measurements can be useless. Wideband expansion for this method was proposed. In this approach complex amplitudes can be measured at multiple frequency points using single measurement procedure. Well known properties of digital signal processing are used to reduce complexity of synthesizing calibrating signal and measurement processing. Limitations of proposed wideband calibration method are discussed.

Approach to use pseudo-noise calibration method for wideband calibration of antenna arrays

We describe the most important aspects of Smart Antenna Array design and functionality and give details of the operational Smart Antenna breadboard designed at the Moscow National Research University of Electronic Technology (MIET) for spatial interference reduction, including a description and some experimental results from beamformed pattern measurements.

Konstantin S. Lyalin

Papers

UAV synthetic aperture radar system for control of vegetation and soil moisture

Approach to use pseudo-noise sequences in inner calibration system of active phased antenna arrays

The study of RF PLL phase noise influence to SAR image quality

Approach to use pseudo-noise calibration method for wideband calibration of antenna arrays

A spatial interference filter system breadboard on the base of a digital beamforming antenna