Browsing by Autor "Renaud Hostache"

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Evaluating the Sensitivity of Hydrological Models to Remotely Sensed Precipitation in a Transboundary Basin
(2025) Paula Pacheco Mollinedo; Frédéric Satgé; Renaud Hostache; Marie‐Paule Bonnet; Jorge Molina‐Carpio; Ramiro Pillco Zolá; Edson Ramírez; Daniel Espinoza
Accurate precipitation data is vital for hydrological modelling, particularly in transboundary basins with scarce hydro-climatic stations. This study evaluates the performance of 20 gridded precipitation products (GPPs), derived from remotely sensed data and reanalyses, in the transboundary Lake Titicaca basin. The methodology integrates two approaches: first, a spatial and temporal accuracy assessment of the GPPs, and second, their application as input data in hydrological models.For spatial accuracy, annual precipitation maps were generated for each GPP, preserving their native resolution, and compared with gauge-based maps. Temporal accuracy was assessed using Taylor diagrams. To evaluate the impact of GPPs on hydrological modelling, streamflow simulations were performed using the GR4J (lumped) and MGB-IPH (semi-distributed) models for three sub-basins, with model performance assessed through Kling-Gupta Efficiency (KGE).Results indicate that CHIRPS, IMERG, and MSWEP excel in spatial and temporal accuracy, capturing the north-to-south precipitation gradient shaped by Andean topography. Streamflow simulations showed that GPPs often outperform gauge-based precipitation in basins with uneven station distribution. In GR4J, MSWEP and CHIRPS yielded the highest KGE values across all sub-basins, while in MGB-IPH, SM2Rain_CCI and IMERG-FR performed best. Notably, the higher KGE scores observed for the GR4J model can be attributed to its lumped structure, which compensates for GPP over/under estimations and spatial distribution inconsistencies.This comprehensive evaluation demonstrates the potential of remotely sensed precipitation products to address data scarcity in transboundary basins. By improving streamflow simulations, these products support informed water resource management, climate adaptation, and transboundary collaboration.
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%).
Machine-Learning Crop-Type Mapping Sensitivity to Feature Selection and Hyperparameter Tuning
(Multidisciplinary Digital Publishing Institute, 2026) Mayra Silvia Pérez-Flores; Frédéric Satgé; Paul Montesano; Renaud Hostache; Ramiro Pillco-Zolá; Diego Tola; Elvis Uscamayta-Ferrano; Lautaro Bustillos; Marie‐Paule Bonnet; Céline Duwig
To improve crop yields and incomes, farmers consistently adapt their practices to climate and market fluctuations, resulting in highly variable crop field distribution and coverage in space and time. As these dynamics illustrate farmers’ challenges, up-to-date crop-type mapping is essential for understanding farmers’ needs and supporting their adoption of sustainable practices. With global coverage and frequent temporal observations, remote sensing data are generally integrated into machine learning models to monitor crop dynamics. Unlike physical-based models that rely on straightforward use, implementing machine learning models requires extensive user interaction. In this context, this study assesses how sensitive the models’ outputs are to feature selection and hyperparameter tuning, as both processes rely on user judgment. To achieve this, Sentinel-1 (S1) and Sentinel-2 (S2) features are integrated into five distinct models (Random Forest (RF), Support Vector Machine (SVM), Light Gradient Boosting (LGB), Histogram-based Gradient Boosting (HGB), and Extreme Gradient Boosting (XGB)), considering several features selection (Variance Inflation Factor (VIF) and Sequential Feature Selector (SFS)) and hyperparameter tuning (Grid-Search) setup. Results show that the preprocess modeling feature selection (VIF) discards the features that the wrapped method (SFS) keeps, resulting in less reliable crop-type mapping. Additionally, hyperparameter tuning appears to be sensitive to the input features, and considering it after any feature selection improved the crop-type mapping. In this context a three-step nested modeling setup, including first hyperparameter tuning, followed by a wrapped feature selection (SFS) and additional hyperparameter tuning, leads to the most reliable model outputs. For the study region, LGB and XGB (SVM) are the most (least) suitable models for crop-type mapping, and model reliability improves when integrating S1 and S2 features rather than considering S1 or S2 alone. Finally, crop-type maps are derived across different regions and time periods to highlight the benefits of the proposed method for monitoring crop dynamics in space and time.
Sensitivity of Lumped and Semi-Distributed Hydrological Models to 20 Gridded Precipitation Products in a Transboundary Basin
(RELX Group (Netherlands), 2024) paula lady pacheco mollinedo; Frédéric Satgé; Renaud Hostache; Marie Paule-Bonnet; Jorge Molina‐Carpio; Ramiro Pillco Zolá; Edson Ramírez; Daniel Espinoza
Sensitivity of lumped and semi-distributed hydrological models to 20 gridded precipitation products in a transboundary basin
(Elsevier BV, 2025) Paula Pacheco Mollinedo; Frédéric Satgé; Marie‐Paule Bonnet; Jorge Molina‐Carpio; Ramiro Pillco Zolá; Edson Ramírez; Daniel Espinoza; Renaud Hostache
Sensitivity of machine-learning crop-type mapping to feature selection and hyper-parameter tuning.
(2026) Mayra Perez; Frédéric Satgé; Jorge Prado Molina; Renaud Hostache; Ramiro Pillco; Elvis Uscamayta; Diego Tola; Lautaro Bustillos; Céline Duwig
To improve crop yields and economic incomes, farmers consistently adapt their practices to climate and market fluctuations, resulting in highly variable crop field distribution and coverage in space and time. As these dynamics ilustrate, up-to-date crop-type mapping is essential to understand farmers’ needs and supporting them in adopting sustainable practices. With global coverage and frequent temporal observations, remote sensing data are generally integrated in machine learning models to monitor crop-type mapping dynamics. Unlike physical-based models that rely on straightforward use, the implementation of machine-learning approaches depends on deep interaction with users. In this context, the study assesses the output sensitivity of these models to features selection and hyper-parameter calibration, both of wich rely on user consideration. To do so, Sentinel-1 (S1) and Sentinel-2 (S2) features are integrated into five distinct models (RF, SVM, LGB, HGB, XGB), considering different features selection (VIF and SFS) and hyper-parameter calibration set-up. Results show that pre-process modeling VIF feature selection discards features that wrapped SFS feature selection keeps, resulting in less reliable crop-type mapping compared to using SFS. Additionally, hyper-parameter calibration appears to be sensitive to the input feature and its consideration after any the feature selection improved the crop-type mapping. In this context a three-step nested modelling set-up including a first hyper-parameters calibration followed by a wrapped feature selection (SFS) and another hyper-parameter calibration, lead to the most reliable model outputs. Across the considered region, LGB and XGB (SVM) are the most (less) suitable model for crop-type mapping and models reliability improved when integrated S1 and S2 features rather than the consideration of S1 or S2 alone. Finally, crop-type maps are derived across different regions and periods to highlight the benefits of the proposed method to monitor crops’ dynamics in space and time.
Soil Salinity Mapping of Plowed Agriculture Lands Combining Radar Sentinel-1 and Optical Sentinel-2 with Topographic Data in Machine Learning Models
(Multidisciplinary Digital Publishing Institute, 2024) Diego Tola; Frédéric Satgé; Ramiro Pillco Zolá; Humberto Sainz; Bruno Condori; Roberto Miranda; Elizabeth Yujra; Jorge Molina‐Carpio; Renaud Hostache; Raúl Espinoza-Villar
This study assesses the relative performance of Sentinel-1 and -2 and their combination with topographic information for plow agricultural land soil salinity mapping. A learning database made of 255 soil samples’ electrical conductivity (EC) along with corresponding radar (R), optical (O), and topographic (T) information derived from Sentinel-2 (S2), Sentinel-1 (S1), and the SRTM digital elevation model, respectively, was used to train four machine learning models (Decision tree—DT, Random Forest—RF, Gradient Boosting—GB, Extreme Gradient Boosting—XGB). Each model was separately trained/validated for four scenarios based on four combinations of R, O, and T (R, O, R+O, R+O+T), with and without feature selection. The Recursive Feature Elimination with k-fold cross validation (RFEcv 10-fold) and the Variance Inflation Factor (VIF) were used for the feature selection process to minimize multicollinearity by selecting the most relevant features. The most reliable salinity estimates are obtained for the R+O+T scenario, considering the feature selection process, with R2 of 0.73, 0.74, 0.75, and 0.76 for DT, GB, RF, and XGB, respectively. Conversely, models based on R information led to unreliable soil salinity estimates due to the saturation of the C-band signal in plowed lands.