# High Spatio-Temporal Resolution CYGNSS Soil Moisture Estimates Using Artificial Neural Networks
## 3 Soil Moisture Retrieval Methodology
### Data Sets
#### 3.2.3 Ancillary Data
Two different, dynamic data sets were investigated for NDVI data throughout 2017 and 2018: (i) The NOAA Climate Data Record (CDR) of AVHRR Daily NDVI 0.05\({}^{\circ}\), Version 4 [62], (ii) MODIS Aqua Surface Reflectance  
Figure 4: Example grid analysis from a CYGNSS observation over the SCAN site Uapb-Marianna on day-of year (DOY) 71, 2018. The red square is the 9-km grid cell that centers the ISMN site (red dot). The black square is the 4-km grid cells that center the CYGNSS observation SP (black dot) that could fall into anywhere in the 9-km grid. (**a**) DDM _power_analog_; (**b**) digital elevation model [m]. Mean slope and elevation values calculated from 4-km grid are also shown; (**c**) IGBP land covers [IGBP]. The color scale for the IGBP land cover is the same as Figure 3. Mean NDVI calculated from the 4-km grid is also shown; (**d**) inland water bodies [Pekel indices [61]]. Permanent and temporary water body percentages within the 4-km grid are also shown. Daily Global 500 m data set [49]. The latter has been chosen for its higher spatial resolution. NDVI is calculated from the near-infrared (NIR) and red bands (RED) of the reflectance data as \(NDVI=(NIR-RED)/(NIR+RED)\). The mean NDVI for the 4-km grid of each CYGNSS observation is generated from the MODIS data with the following methodology: The data set is accessed via Google Earth Engine Python API [60] to rapidly and accurately perform analysis for multiple CYGNSS observations at a time. It is possible with the help of very high computing power of the Google Cloud Platform while benefiting the same interface for all different satellite data sets. NDVI data usually suffer from clouds because it is generated by optical instruments such as in the MODIS mission. To deal with this problem, we applied a sliding window averaging over 16 days, whose center is the day of interest (eight days ahead and seven days to the past). Noting that the 4-km grid houses \(8\times 8\) spatial pixels, we have an \(8\times 8\times 16\) NDVI cube for each day. To eliminate the ill (cloud-suffered or so) NDVI values, we only considered the width of two standard deviations of the distribution within the NDVI cube to calculate the mean NDVI for that particular grid and day of year [63]. Such an approach would produce close numerical values for adjacent days or short time-series; however, it is the representation of the reality rather than being a problem. More precisely, NDVI experiences quasi-constant trends in daily or weekly periods, and shows stronger dynamics through seasonal changes. Furthermore, spatial variations exist within the representativeness grid of each ISMN site mostly due to the mix of land covers. Land cover maps for the ISMN sites can be seen in Figure 5.  
VWC is computed by using the NDVI data via Equation (10). To the authors' knowledge, the accuracy of this empirical relation for high spatio-temporal CYGNSS resolution is not yet proven. Nevertheless, we decided to use VWC as an input feature in addition to NDVI since it encapsulates land cover information through _stem factor_ and temporal memory information through minimum and maximum NDVI values. The _stem factor_ information is a land cover-based LUT in the SMAP mission [39]. The _stem factor_ value for the 4-km grid of each CYGNSS observation in this study is calculated as a weighted sum of LUT _stem factors_ based on the land cover percentages in the scene. We performed two different VWC calculations: (i) \(NDVI_{max}\) and \(NDVI_{min}\) are computed from 2017 and 2018 NDVI data. (ii) The current NDVI is used in place of \(NDVI_{max}\) for the entire data set, and a global constant value of 0.1 was used for \(NDVI_{min}\), as suggested in the SMAP's VWC report [39] (Though suggestion for \(NDVI_{max}\) was for croplands and grasslands only). The former performed better in the SM retrieval, and we link this to the phenomenon that ISMN sites in this study were not only from croplands and grasslands as the latter method assumes.