Agricultural UAV-based remote sensing tools to facilitate decision-making for increasing productivity in developing countries were developed and tested. Specifically, a high-quality multispectral sensor and sophisticated-yet-user-friendly data processing techniques (software) under an open-access policy were implemented. The multispectral sensor—IMAGRI-CIP—is a low-cost adaptable multi-sensor array that allows acquiring high-quality and low-SNR images from a UAV platform used to estimate vegetation indexes such as NDVI. Also, a set of software tools that included wavelet-based image alignment, image stitching, and crop classification have been implemented and made available to the remote sensing community. A validation field experiment carried out at the International Potato Center facilities (Lima, Peru) to test the developed tools is reported. A thorough comparison study with a wide-used commercial agricultural camera showed that IMAGRI-CIP provides highly correlated NDVI values (R²≥ 0.8). Additionally, an application field experiment was conducted in Kilosa, Tanzania, to test the tools in smallholder farm settings, featuring high-heterogeneous crop plots. Results showed high accuracy (> 82%) to identify 13 different crops either as mono-crop or as mixed-crops.

Accurate determination of plant water status is mandatory to optimize irrigation scheduling and thus maximize yield. Infrared thermography (IRT) can be used as a proxy for detecting stomatal closure as a measure of plant water stress. In this study, an open-source software (Thermal Image Processor (TIPCIP)) that includes image processing techniques such as thermal-visible image segmentation and morphological operations was developed to estimate the crop water stress index (CWSI) in potato crops. Results were compared to the CWSI derived from thermocouples where a high correlation was found ( $r_{Pearson}$ = 0.84). To evaluate the effectiveness of the software, two experiments were implemented. TIPCIP-based canopy temperature was used to estimate CWSI throughout the growing season, in a humid environment. Two treatments with different irrigation timings were established based on CWSI thresholds: 0.4 (T2) and 0.7 (T3), and compared against a control (T1, irrigated when soil moisture achieved 70% of field capacity). As a result, T2 showed no significant reduction in fresh tuber yield (34.5 ± 3.72 and 44.3 ± 2.66 t ha ${}^{-1}$ ), allowing a total water saving of 341.6 ± 63.65 and 515.7 ± 37.73 m ${}^3$ ha ${}^{-1}$ in the first and second experiment, respectively. The findings have encouraged the initiation of experiments to automate the use of the CWSI for precision irrigation using either UAVs in large settings or by adapting TIPCIP to process data from smartphone-based IRT sensors for applications in smallholder settings.

Recent observations of significant enhancements in exospheric hydrogen (H) emission in response to geomagnetic storms have been difficult to interpret in terms of the evolution of the underlying global, 3‐D exospheric structure. In this letter, we report the first measurement of the timescales and spatial gradients associated with the exospheric response to a geomagnetic storm, which we derive from a novel, time‐dependent tomographic analysis of H emission data. We find that global H density at 3 R_E begins to rise promptly, by ∼15%, after storm onset and that this perturbation appears to propagate outward with an effective speed of ∼60 m/s, a response that may be associated with enhanced thermospheric temperature and vertical neutral wind. This effective upwelling has significant implications for atmospheric escape as well as for charge exchange reaction rates, which drive important space weather effects such as plasmaspheric refilling and ring current decay.

Variability in the location and shape of the dayside magnetopause is attributed to magnetic reconnection, a fundamental process that enables the transfer of mass, energy, and momentum from the solar wind into the magnetosphere. The spatial and temporal properties of the magnetopause, under varying solar and magnetospheric conditions, remain largely unknown because empirical studies using in-situ observations are challenging to interpret. Global wide field-of-view (FOV) imaging is the only means to simultaneously observe the spatial distribution of the plasma properties over the vast dayside magnetospheric region and, subsequently, quantify the energy transport from the interplanetary medium into the terrestrial magnetosphere. Two upcoming missions, ESA/CAS SMILE and NASA’s LEXI will provide wide-field imagery of the dayside magnetosheath in soft X-rays, an emission generated by charge exchange interactions between high charge-state heavy ions of solar wind origin and exospheric neutral atoms. High-cadence two-dimensional observations of the magnetosheath will allow the estimation of dynamic properties of its inner boundary, the magnetopause, and enable studies of its response to changes in the solar wind dynamic pressure and interplanetary magnetic field orientation. This work introduces a statistically-based estimation approach based on inverse theory to estimate the spatial distribution of magnetosheath soft X-ray emissivities and, with this, identify the location of the magnetopause over the Sun−Earth line. To do so, we simulate the magnetosheath structure using the MHD-based OpenGGCM model and generate synthetic soft X-ray images using LEXI’s orbit and attitude information. Our results show that 3-D estimations using the described statistically-based technique are robust against Poisson-distributed shot noise inherent to soft X-ray images. Also, our proposed methodology shows that the accuracy of both three-dimensional (3-D) estimation and the magnetopause standoff distance calculation highly depends on the observational point.

Quasiperiodic radio frequency interference (RFI), such as those generated by telecommunication and active radar systems, is commonly encountered in radio astronomy observations. Such RFI‐contaminated signals contain hidden periodicities due to cyclic features involved in their formation (e.g., carrier frequencies, periodic keying of the amplitude, and baud rates). RFI signal characterization and its subsequent excision based on the well‐known cyclic spectrum analysis have been previously demonstrated; however, the high complexity of the algorithm and the computational cost of its implementation have limited its utility in radio astronomy, rendering less sophisticated solutions. To overcome this challenge, we present a novel method for RFI detection and mitigation based on efficient estimation of the cyclic spectrum by compressive statistical sensing (CSS) of sub‐Nyquist data. CSS performs second‐order statistical estimation such as cyclic spectrum using a reduced number of input samples, thereby enabling accelerated performance. To validate the feasibility of the proposed method, we conduct experiments with simulated data and assess the detection and mitigation results under different parameter settings, for example, interference‐to‐noise ratio, additional RFI sources, frequency resolution, and input data size. We demonstrate the real performance of the method by analyzing radio astronomy data (∼1.3 GHz) acquired with the L‐wide band receiver at the Arecibo Observatory, which is typically corrupted by active air surveillance radars located nearby. Our CSS‐based solution enables robust and efficient detection of the RFI frequency bands present in the L‐band data, and subsequent excision by blanking is also demonstrated.

For the past decade, the Lyman‐alpha detectors on board National Aeronautics and Space Administration's Two Wide‐angle Imaging Neutral‐atom Spectrometers (TWINS) mission have obtained routine measurements of solar Lyman‐α photons (121.6 nm) resonantly scattered by atomic hydrogen (H) in the terrestrial exosphere. These data have been used to derive global three‐dimensional (3‐D) models of exospheric H density beyond 3 R_E, which are needed to understand various aspects of aeronomy and heliophysics, such as atmospheric chemistry and energetics, magnetospheric energy dissipation, ion‐neutral coupling, and atmospheric evolution through gravitational escape. These empirical distributions are obtained through parametric fitting of assumed functional forms that have little observational justification, thus limiting confidence in conclusions drawn from analysis of the resulting exospheric structure. In this work, we present a new means of global 3‐D reconstruction of exospheric H density through tomographic inversion of the scattered H Lyman‐α emission. Our approach avoids the conventional dependence on ad hoc parametric formulations and, based on the case studies reported here, appears to enable a more accurate characterization of the global structure of the H density in the outer exosphere. We evaluate the bounds of technique feasibility using simulated TWINS data and report new geophysical insights gained from applying this promising new approach to an example of actual TWINS data.