## 1 Introduction
However, the radar failed in September of the same year, with the mission continuing to the present only with the radiometer data, which involve a resolution of 40 km. There is an especially high variability of soil moisture in zones where land use, topography, and soil type are also highly variable [13]. Knowledge of soil moisture and its spatial distribution is of considerable importance to economic, social, hydrological, and agronomical planning. The scale required for each purpose varies, with initiatives at a resolution on a medium or global scale unsuitable for working on precision agriculture at a plot scale. Over the last decade, the data obtained by unmanned aerial vehicles (UAV) have been intensively studied for agricultural applications given their flexibility of image acquisition and high spatial resolution, with customized cameras installed depending on the band (spectral resolution) and resolution requirements [14], especially including infrared bands used in several vegetation indices to monitor their states. Advances in UAV technology and sensor size, lower costs, global positioning systems (GPS), and pre-programmed flights have led to this knowledge gap being filled and a reduction in the spatial resolution of the most common current remote sensing systems. However, UAVs currently have several limitations related to weather conditions and re-visit times, in addition to being costly [15]. Soil mapping and image analysis are recent tools to simulate and monitor soil moisture [16]. The topographic wetness index (TWI) enables potential sites where moisture or water is accumulated to be identified by means of the geomorphological analysis of the land using the DEM (digital elevation model), considering that topography is a first-order control of the spatial variation of hydrological conditions [17]. The TWI is effective for studying soil moisture on a coarse scale with slope variability and is dependent on geology and the possible divergence between surface and subsurface conditions [17]. Multispectral satellite imagery is another approach to estimate soil moisture content [18] by means of the reflectance of the Earth's surface, although pixel spatial resolution is too coarse to be used on agriculture on a plot scale. Satellite measurements are also limited by their return period and are often impacted by cloud cover, particularly in tropical mountainous regions [5], reducing the available images to study the landscape. Soil reflectance is influenced by soil moisture and other intrinsic parameters, such as soil texture, mineral composition, and organic matter [19], affecting the absorption of different wavelengths. Recent laboratory studies have demonstrated the effect of soil moisture on reflectance for different orders of soils [20]. Organic matter and mineral composition affect short visible wavelengths and soil moisture in the NIR (near-infrared) and SWIR (shortwave infrared) spectral bands [19]. Regarding the spectral variations of water absorption, several multispectral indices using NIR and SWIR to analyze water content and soil moisture by means of optical sensors from space have been studied over the last decade [21; 22; 23]. For instance, the soil moisture of land covered by vegetation has been studied using indices such as the vegetation dryness index (VDI), the temperature vegetation dryness index (TVDI) [5], the enhanced vegetation index (EVI), the green coverage index (GCI) and, most commonly, the normalized difference vegetation index (NDVI), an enhanced vegetation index to determine vegetation status using drought as an indicator of soil moisture, and the normalized difference water index (NDWI), used to determine water bodies and areas where soil is saturated and additionally used to determine the vegetation hydric index, maximizing water reflectance. There are several methodologies to determine the NDWI.