## 1 Introduction  
Irrigation is critical for agricultural production, and it plays an important role in food security in many areas of the world [1; 2; 3]. Irrigation not only increases the crop yield by overcoming climatic constraints, but it also modifies local climatic conditions and adds to evapotranspiration [4; 5]. In addition, irrigation can have some environmental impacts like increased soil salinity [6], changes in the water table [7] and altered hydrological cycles [8]. The stated economic and ecological importance of irrigation warrants that the extent and distribution of irrigation is mapped precisely and monitored regularly. Soil moisture is a key indicator of surface irrigation and irrigated landcover. The interaction of electromagnetic (EM) radiation with soil moisture and irrigated land has shown a significant correlation at various wavelengths. As a result, several methods have been developed to map surface irrigation in different EM regions, namely, optical, thermal and microwave. These methods have been summarized in detail in several recent studies [9; 10; 11; 12]. As microwave sensors are particularly sensitive to the changes in ground moisture, these have been used in mapping irrigated landcovers in many studies [13]. Though microwave sensors have provided promising results for the detection of irrigation in many areas, the coarse spatial resolution and the confounding effects of topography and vegetation created notable uncertainties in some regions [14]. However, the launch of the Sentinel-1 satellites constellation (S1-A and S1-B) provided an unprecedented radar data coverage at a high spatial and temporal resolution. This has led to some new approaches for mapping irrigation based on machine learning and empirical modelling, providing improved results [15; 16]. Thermal infrared (TIR) sensors are considered highly relevant for irrigation mapping [17; 18]. To distinguish between 'irrigated' and 'non-irrigated' land, the TIR-derived surface temperature (Ts) has been widely used [19; 20]. Ts is invariably used in combination with certain vegetation measures to map irrigated agriculture. There are very many vegetation indices which have been developed since the early attempts in the 1960s-1970s [21; 22]. Vegetation indices frequently use a combination of spectral responses between visible and the near- or mid-infrared range [23]. The most used vegetation measure is the normalized difference vegetation index (NDVI), which is based on the near-infrared (NIR) and red wavebands. Presently, there are many satellites with optical sensors onboard, which provide images suitable for vegetation indices at medium to high spatial resolution. However, there is a lack of satellites that provide thermal images with acceptable resolution, except for the Landsat series of satellites (presently Landsat-8/9), which continues to provide concurrent thermal and optical observations at a nominal resolution of 30 m.  
Although the thermal-based methods are powerful for irrigation mapping since they have physical rationale for their application, most methods depend on local weather conditions such as air temperature (Ta) close to the time of the satellite overpass. The relationship between the Ts and soil moisture is highly influenced by the Ta and other atmospheric conditions [24]. Without the calibration of the Ts, the results are likely to vary spatially across different landcovers, confounding the distinction of irrigated landcover. Considering this limitation, efforts have been made to find a robust substitute of Ts in the optical range to adequately account for surface moisture. Several indices that utilize optical observations have been proposed for the quantification of surface moisture or the lack of it, as shown in Table 1. Some of these indices are based on triangular or trapezoidal pixel distributions of optical observations in different spectral frequency ranges. It is notable that these optical indices (Table 1) are mostly empirical and lack any physical foundation.