Field Crop 2025, Vol.8, No.6, 293-300 http://cropscipublisher.com/index.php/fc 294 2 Remote Sensing Technologies in Crop Monitoring 2.1 Types of remote sensing platforms When it comes to crop monitoring, the most common "eyes" can be roughly divided into three types: satellites, drones and ground sensors. Satellite platforms, such as Sentinel-2 or Landsat 8, can cover large areas at one go and are frequently revisited, making them particularly suitable for observing macroscopic changes in crop growth (Ibrahim et al., 2023). However, their resolution is limited and sometimes the details are not clear enough. On the contrary, drones can "bend down" to observe the field conditions, with high resolution, flexibility and mobility, and can take off at any time to take pictures. They are especially suitable for monitoring fine indicators such as leaf area index (LAI) or chlorophyll content. As for ground sensors, although they have a small field of view, they are highly accurate. Both handheld and fixed types are available and are often used to calibrate or verify data from satellites and drones (Mukiibi et al., 2024). It can be said that each of the three has its own strengths and weaknesses. When used in combination, the effect is often better. 2.2 Key indices and parameters: NDVI, LAI, chlorophyll content In remote sensing images, truly valuable information is often hidden in those indices. NDVI is the most commonly used one to determine the "mental state" of vegetation. The higher the value, the more lush the crops (Han et al., 2021). LAI, however, took a different perspective, reflecting biomass and yield potential from the number of leaves (Sishodia et al., 2020). Chlorophyll content is mostly calculated with indices such as SPAD or MCARI, which can reveal the photosynthesis and nitrogen conditions of plants (Shanmugapriya et al., 2022). Of course, not all crops are equally sensitive to these indicators, so sometimes scientists also use "advanced" parameters such as the red border index and multi-band index to make the results closer to reality (Figure 1). Figure 1 Spatial variability of vegetation indices (VIs) during the kharif maize: (a) normalised difference vegetation index (NDVI); (b) green normalised difference vegetation index (GNDVI); (c) normalised difference red-edge index (NDRE); (d) enhanced vegetation index (EVI); (e) excess green vegetation index (ExGVI); (f) wide dynamic range vegetation index (WDRVI); (g) atmospherically resistant vegetation index (ARVI); (h) green chlorophyll vegetation index (GCVI); (i) modified chlorophyll absorption ratio index (MCARI) (Adopted from Parida et al., 2024)
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