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).

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.

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.

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.

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 to fly on board of a 3U satellite from the United Arab Emirates' National Space Science and Technology Center (NSSTC). The main objectives of RITA are to perform microwave radiometry measurements at L-band, vegetation analysis using a hyper-spectral camera, Radio-Frequency Interference (RFI) detection and classification, and a technology demonstration of sensor networks using a custom LoRa transceiver. Radiometry measurements, RFI detection, and the LoRa experiment will be performed using a Software-Defined Radio (SDR) with a frontend designed as a modified version of the Flexible Microwave Payload 1 (FMPL-1) used in the3Cat-4 mission, whereas an hyper-spectral camera will be used primarily for vegetation measurements. This work discusses the description and design of the payload, and emphasizes on the techniques used for the generation of the scientific outcome.