Browsing by Autor "F. Arragan"

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High Spatial Resolution Soil Moisture Mapping over Agricultural Field Integrating SMAP, IMERG, and Sentinel-1 Data in Machine Learning Models
(Multidisciplinary Digital Publishing Institute, 2025) Diego Tola; Lautaro Bustillos; F. Arragan; René Chipana Rivera; Renaud Hostache; Eléonore Resongles; Raúl Espinoza-Villar; Ramiro Pillco Zolá; Elvis Uscamayta; María Eufemia Pérez-Flores
Soil moisture content (SMC) is a critical parameter for agricultural productivity, particularly in semi-arid regions, where irrigation practices are extensively used to offset water deficits and ensure decent yields. Yet, the socio-economic and remote context of these regions prevents sufficiently dense SMC monitoring in space and time to support farmers in their work to avoid unsustainable irrigation practices and preserve water resource availability. In this context, our study addresses the challenge of high spatial resolution (i.e., 20 m) SMC estimation by integrating remote sensing datasets in machine learning models. For this purpose, a dataset made of 166 soil samples’ SMC along with corresponding SMC, precipitation, and radar signal derived from Soil Moisture Active Passive (SMAP), Integrated Multi-satellitE Retrievals for GPM (IMERG), and Sentinel-1 (S1), respectively, was used to assess four machine learning models’ (Decision Tree—DT, Random Forest—RF, Gradient Boosting—GB, Extreme Gradient Boosting—XGB) reliability for SMC mapping. First, each model was trained/validated using only the coarse spatial resolution (i.e., 10 km) SMAP SMC and IMERG precipitation estimates as independent features, and, second, S1 information (i.e., 20 m) derived from single scenes and/or composite images was added as independent features to highlight the benefit of information (i.e., S1 information) for SMC mapping at high spatial resolution (i.e., 20 m). Results show that integrating S1 information from both single scenes and composite images to SMAP SMC and IMERG precipitation data significantly improves model reliability, as R2 increased by 12% to 16%, while RMSE decreased by 10% to 18%, depending on the considered model (i.e., RF, XGB, DT, GB). Overall, all models provided reliable SMC estimates at 20 m spatial resolution, with the GB model performing the best (R2 = 0.86, RMSE = 2.55%).
Modeling Wet Bulb and Soil Moisture Under Drip Irrigation in of the Bolivian Highlands, Through Dimensional Analysis and Multiple Correlation
(Facultad de Derecho (Universidad de la República), 2015) F. Arragan; R. Chipana
Agricultural areas of the Bolivian highlands are located between 3200-4000 meters above sea level, where soils are shallow and show great variability, which hinders the proper application of drip irrigation, as these systems are designed to achieve high efficiencies, however, technology by itself does not guarantee that. Therefore it is necessary consider in the design the wet bulb geometry and soil moisture. In this sense, the objective was to conduct mathematical modeling of wet bulb and humidity under surface drip irrigation, to soil from the highlands of Bolivia. Physical and hydrological characteristics of soil and water dynamics under drip irrigation were determined. For this we used a point source. Mathematical modeling was carried out for saturated and unsaturated zone. In the first case modeling was done using dimensional analysis, obtaining a quadratic equation for the maximum diameter of wet, reaching 28 cm for a flow rate of 1 l/h and 8 hours of irrigation, while the observed value field was 29 cm. In the unsaturated zone modeling was done by the method of multiple correlation, determining a maximum depth of 21 cm for a flow rate of 5 l/h and 4 hours of irrigation, in field a value of 20 cm was observed. Regarding the volumetric soil moiture, modeling was performed using matrix equations, observed that for high flow rates and high time irrigation, the simulated moisture was 0.52 and the observed was 0.54. In general the simulated and observed data of the three parameters had a proper fit.