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Drought, a highly detrimental natural disaster, poses significant threats to both human populations, wildlife, and vegetation. Traditional methods of monitoring soil moisture levels rely on ground-based measurements from meteorological stations. However, these stations often lack comprehensive coverage in certain agricultural areas, necessitating the use of alternative methods such as satellite remote sensing. This technique provides a reliable means of measuring soil moisture, a critical factor in effective agricultural management. This paper investigates variations in soil moisture and drought using data from the Cyclone Global Navigation Satellite System (CYGNSS) and the Soil Moisture Active and Passive (SMAP) system. To evaluate the accuracy of these data products, we compared both datasets with the Global Land Data Assimilation System (GLDAS) NOAH model from 2018 to 2019. Our findings reveal a strong correlation between the datasets and the model, with Pearson correlation coefficients (r) and Root Mean Square Errors (RMSE) of approximately r = 0.98 and RMSE = 0.03 for SMAP, and r = 0.97 and RMSE = 0.02 for CYGNSS, respectively. We further compared these measurement datasets with drought indicators such as the Standardized Precipitation Index over three months (SPI3), the Normalized Difference Vegetation Index (NDVI), and Total Water Storage (TWS). The correlation coefficients between SMAP and the three indicators (NDVI, SPI3, and TWS) were 0.93, 0.84, and 0.047, respectively, while the coefficients between CYGNSS and the same indicators were 0.86, 0.78, and 0.56, respectively. All the variables also exhibited significant p-values. Despite minor differences, the results demonstrate excellent agreement. Our findings underscore the sensitivity of space-based sensors to drought conditions, highlighting their effectiveness as tools for detecting and monitoring drought (e.g. agricultural drought), particularly in the short term. [ABSTRACT FROM AUTHOR]

The reflection of Global Navigation Satellite Systems (GNSS) signals, namely GNSS-Reflectometry (GNSS-R), has recently proven to be able to monitor land surface properties in the microwave spectrum, at a global scale, and with very low revisiting time. Moreover, this new technique has numerous additional advantages, including low cost, low power consumption, lightweight and small payloads, and near real-time massive data availability, as compared to conventional monostatic microwave remote sensing. However, the GNSS-R surface reflectivity values estimated through the bistatic radar equation, and the Fresnel coefficients have shown a lack of coincidence with real surface reflectivity data, mostly due to calibration issues. Previous studies have attempted to avoid this matter with direct regression methods between uncalibrated GNSS-R reflectivity data and external soil moisture content (SMC) products. However, calibration of GNSS-R reflectivity used in traditional inversion models is still a challenge, such as those to estimate SMC, freeze/thaw, or biomass. In this paper, a successful procedure for GNSS-R reflectivity calibration is established using data from the CYGNSS (Cyclone GNSS) constellation. The scale and bias parameters are estimated from the theoretical dielectric properties of water and dry sand, which are well-known and empirically validated values. We employ four calibration areas that provide maximum range limits of reflectivity, such as deserts and wetlands. The CYGNSS scale factor and the bias parameter resulted in a = 3.77 and b = 0.018, respectively. The derived scale and bias parameters are applied to the CYGNSS dataset, and the retrieved SMC values through the Fresnel reflection coefficients are in excellent agreement with the Soil Moisture Active Passive (SMAP) SMC product. Then, the SMAP SMC is used as a reference true value, and provides a standard linear regression with an R-square coefficient of 0.803, a root mean square error (RMSE) of 0.084, and a Pearson's correlation coefficient of 0.896. [ABSTRACT FROM AUTHOR]