This study presents a Digital Twin (DT) that is being created to optimize the use of the available hydric resources, and mitigate the effects of the increasing water shortage in irrigated agriculture in fields in the Urgell channel region (Lleida). A DT is "a virtual representation of an object or system that spans its lifecycle, it is updated from real-time data, and uses simulation, machine learning and reasoning to help decision-making." It will model the water fluxes using the knowledge of the amounts of water taken in, used, and returned to the environment, and other parameters that impact the water budget, such as atmospheric variables (temperature, water vapor deficit, relative humidity, solar radiance…), surface soil moisture, and evapotranspiration maps, etc. Satellite Earth Observation (EO) data, collocated with in-situ data from a network of 20 soil moisture probes and 2 meteo stations will be used to train the DT. Additionally, a rover-based ground penetrating radar will be used for cross-calibration.

The Remote sensing and Interference detector with radiome-Try and vegetation Analysis (RITA Payload) [1] is a development of the UPC NanoSat Lab, aimed at Earth Observation (EO). It is one of the winners of the Second IEEE GRSS Student Grand Challenge, and will fly on-board AlainSat-1, a 3U CubeSat developed by the National Space Science and Technology Center (NSSTC) of the United Arab Emirates.RITA hosts three experiments. An L-band microwave radiometer (MWR) will gather data of soil moisture and sea ice thickness and concentration, aided with a Radio-frequency Interference (RFI) detection algorithm. A LoRa transceiver will perform on-demand execution of the EO experiments [2]. Finally, a Near-Infrared (NIR) Hyperspectral Camera will gather data for vegetation monitoring, agriculture applications, hydrology and coastal and inland waters mapping, among others [3].This work is focused on the calibration and validation of the Hyperspectral imager, at optical, electronic and spectral levels, as well as in the verification of its performance to measure Normalized Difference Vegetation Index (NDVI).

Detection of marine litter is an urgent task given the large amount of plastics that end up in all water systems. This manuscript provides the description and results obtained from a ground-based Global Navigation Satellite System - Reflectometry (GNSS-R) system built in a controlled indoor environment intended to test the capabilities of GNSS-R techniques for marine litter detection, when different wave conditions and plastic litter were used. Results show that it may be possible to detect the damping of waves produced by the accumulations of some types of plastic.

The Remote sensing and Interference detector with radiome-Try and vegetation Analysis (RITA) payload was selected in 2019 the 2nd GRSS Student Grand Challenge to fly onboard AlainSat-1, a 3U CubeSat developed at the National Space Science and Technology Center (NSSTC) in United Arab Emirates. RITA payload includes a passive Microwave Radiometer (MWR), a LoRa transceiver, and an hyper-spectral camera. The objective of the payload is to retrieve Earth Observation (EO) parameters such as: soil moisture, sea-ice thickness and concentration, among others. A Radio Frequency Interference (RFI) detection algorithm is also included to the payload to create interference maps in the received signals.

AlainSat-1 is an educational and scientific nanosatellite project that was initiated in late 2019 by the IEEE Geoscience and Remote Sensing Society (GRSS) along with National Space Science and Technology Center (NSSTC) of UAE University in the frame of the 2nd Student Grand Challenge [1]. The project involves close collaboration between four international universities to design, build, test and launch a remote sensing CubeSat.The spacecraft is a 3U CubeSat that has a mass of around 4 Kgs. The spacecraft has an active 3-axis control system capable of attitude determination and control to less than one degree. Two communications systems will be used on-board: a UHF System and an S-Band System. The project has passed the Critical Design Review (CDR) stage and is currently in the assembly and integration phase. The satellite is currently planned for launch to a sun-synchronous orbit on-board a Falcon 9 rocket in the second quarter of 2024.

Over 14 million tons of plastic end up yearly in the ocean, originating from polluted waterways such as rivers, sewers, or storm waters, or from industrial and nautical activities with improper garbage disposal methods. Due to its long decomposition time, and the presence of currents and gyres, most of plastic debris freely floating in the ocean end up forming large extensions of marine litter known as garbage patches. An example is the Great Pacific garbage patch located in the central North Pacific Ocean covering an area larger than 1.6 million squared kilometers. Some works have recently reported the potential of GNSS-Reflectometry to detect marine plastic litter from space. This study presents the results of a controlled field experiment conducted under the auspices of ESA at the "Atlantic Basin" at the Deltares research institute (Delft, The Netherlands).

In 2019, the IEEE 2nd GRSS Student Grand Challenge selected the Remote sensing and Interference detector with radiomeTry and vegetation Analysis (RITA) payload to fly on board of AlainSat-1, a 3U CubeSat from the United Arab Emirates' National Space Science and Technology Center (NSSTC). The RITA payload is equipped with a Commercial Off- The-Shelf (COTS) Software Defined Radio (SDR) for the RF signal conditioning and a System on a Chip (SoC) processor in charge of controlling the execution and processing of each experiment that conforms the payload. RITA includes an L-band microwave radiometer (MWR), a Radio Frequency Interference (RFI) detector, a LoRa transceiver, and a hyper-spectral camera. A multi-level acquisition strategy joining several sensors for the aforementioned experiments' allows this payload retrieve parameters such as: soil moisture derived from the MWR, Normalized Difference Vegetation Index (NVDI) obtained from the hyper-spectral camera, improved acquisition of vegetation-related measurements through the LoRa transceiver, or even the compilation of worldwide interference maps. This manuscript will focus on the design, implementation, verification, and testing of the MWR on board the RITA payload, a successor of previous CubeSat missions such as FSSCat, which carried the FMPL-2 payload, or 3Cat-4 carrying FMPL-1 payload.

Passive microwave remote sensing encompasses techniques well suited for use in systems with limited power budget, due to the low consumption linked to reception-only operation. Some examples of these techniques include Microwave Radiometry (MWR), Global Navigation Satellite Systems Reflectometry (GNSS-R), and Radio-Frequency Interference (RFI) detection algorithms. Small satellite missions including these instruments, such as FSSCat, have proven the value of passive remote sensing techniques with high revisit times due to the low orbits, and the possibility to horizontally scale the mission with more satellites. The Flexible Microwave Payload (FMPL) used in the FSSCat mission has continued development within the RITA Payload project, and it is now being used in Drone-based platforms to provide in-situ data using space-proven acquisition algorithms. In this work, the development of a drone-based FMPL will be presented.

Global change and sea level rise are increasing the interest in monitoring oceans, and specially the Arctic. L-band microwave radiometers can be used for thin sea ice thickness and oceans monitoring, and these maps are being routinely generated from SMOS [1] and SMAP [2], which if combined with imagers provide a higher resolution. The CubeSat-based payload RITA (RadIomeTry and vegetation Analysis) plans to address those monitoring needs by including an IoT enabled payload, a hyperspectral imager and an L-band microwave radiometer with RFI detection and mitigation [3]. The IoT payload implements an SDR-based LoRa modulation and it will be used to perform On-Demand executions of the radiometer and the imager payloads on-board RITA. As part of this work, an in-depth description of the objectives, concept of operations and hardware and software architecture and implementation of the IoT LoRa module will be provided.

The Remote sensing and Interference detector with radiomeTry and vegetation Analysis (RITA) payload is one of the Remote Sensing payloads selected by the 2nd GRSS Student Grand Challenge in 2019 to fly on board of a 3U satellite that is being developed at the National space Science and Technology Center (NSSTC), United Arab Emirates University. RITA has been designed as an academic mission with a strong focus on Earth Observation techniques and technologies. This payload is equipped with a Software-Defined Radio for microwave radiometry and RFI detection, a hyperspectral camera (25 bands from 600 to 975 nm), and a LoRa transceiver, which will work in tandem to produce vegetation-related measurements with improved accuracy. Soil moisture measurements, for example, will be derived from a Total Power Microwave Radiometer working at L-band and Normalized Difference Vegetation Index (NDVI) measurements in a technique known as Pixel Downscaling. In this work, recent advances in the payload design will be presented, as well as a more in depth mission analysis.