## 3 Results
### Sensors and Sensor Types  
The choice of sensor is mainly determined by the study's focus and specific objectives. Different sensor technologies exhibit varying degrees of suitability for mapping small-scale agriculture and cropping systems. Furthermore, the characteristics of the study area are influenced by the sensor selection. There are several types available, each suited to distinct applications. For measurements in visible and infrared wavelengths, active sensors such as Light Detection and Ranging (LiDAR) are employed, while passive sensors rely on the electromagnetic energy from the sun which is reflected by the earth and subsequently received and measured. Passive sensors operating across the visible to infrared spectrum are broadly classified as optical sensors, with further differentiation based on their radiometric resolu  
Figure 6: Sankey diagram showing the connections between funding countries (left nodes) and study areas (right nodes) of the reviewed publications. Self-referential connections indicate studies funded by sources within the studied country. The categorization represents the country of origin, without differentiating between types of funding source, such as governmental programs, non-governmental organizations (NGOs), or private investments. If no specific funding source was listed, the first authors primary affiliation was used as the funding organization. The category “Other” includes all countries with only one study’s funding source. The color scheme categorizes funding sources by continent: blue for Europe, orange North America, grey for Asia, and yellow for Africa. The numbers represent the study counts. tion, including multispectral, hyperspectral, and thermal sensors [27; 68; 70]. Microwave RS can either be passive or active. Passive microwave sensors, such as radiometers can measure the emitted microwave wavelengths, but their intensity is relatively low. Active microwave sensing, achieved through Radio Detection And Ranging (RADAR) enables the assessment of measuring terrain properties, canopy structural properties, moisture status, or flooding [29; 71; 72]. RS is a vital tool for EO, offering timely, repetitive, and cost-effective information on the Earth's surface. It allows for data collection without direct physical contact, and advances in RS technology have led to numerous techniques for capturing data across the electromagnetic spectrum [27; 68; 70]. The utilized sensors, sensor types, and platforms are shown in Figure 7, respectively. It is important to note that all sensors utilized in each study are enumerated; consequently, multiple entries per study may be present. Multispectral sensors are predominantly used in the reviewed studies, accounting for 88.3%. The main share is allocated to Sentinel-2 (48 studies), Landsat (47), and MODIS (41), followed by multispectral UAV surveys (17). Other than these three mainly used multispectral satellite sensors, a wide range of other sensors have been used for multispectral analyses. As for RADAR analyses, the use of Sentinel-1 sensor is predominant. The only LiDAR sensor used in three publications is GEDI, a sensor mounted on the In  
Figure 7: Overview of the different RS sensors, their platform, and the sensor type combination used in the reviewed publications. Abbreviations: MODIS, Moderate Resolution Imaging Spectrometer; UAV, Unmanned Aerial Vehicle; AVHRR, Advanced High-Resolution Radiometer; SPOT, Satellite Pour I’Observation de la Terre; GEDI, Global Ecosystem Dynamics Investigation; ENVISAT, Environmental Satellite; ALOS, Advanced Land Observing Satellite; SMAP, Soil Moisture Active Passive; ASAR, Advanced Synthetic Aperture Radar; PALSAR, Phased Array L-band Synthetic Aperture Radar; SRTM, Shuttle Radar Topography Mission; ERS, European Remote Sensing Satellite. ternational Space Station, ISS [46; 78; 79]. Three studies employed PAN imaging for their analyses [22; 74; 75]. Two studies use thermal imaging [23; 76]. There are no studies that focus on hyperspectral data.