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, été validée en utilisant des données Sentinel-1 et des mesures in-situ

, Deux réseaux ont été appliqués l'un après l'autre, le premier pour estimer l'humidité du sol (mv) et le second pour estimer la rugosité de la surface du sol (Hrms) en utilisant en entrée l'humidité du sol estimée par le premier réseau. Deux tests ont été effectués, le premier est utilisé en entrée pour le second réseau de neurones (celui pour estimer Hrms) le mv estimé à très haute résolution spatiale "THSR, Deux configurations d'inversion ont été proposées. La première configuration d'inversion est basée sur l'estimation de la rugosité du sol à très haute résolution spatiale "THSR" (échelle de la parcelle ou à plus petite échelle)

, En effet, après une forte pluie le sol est supposé être humide. En l'absence de pluie depuis quelques jours, le sol est supposé être sec ou légèrement humide. L'utilisation d'une connaissance a priori sur l'humidité du sol

, La deuxième configuration d'inversion concerne l'estimation en même temps de la rugosité du sol (Hrms) et de l'humidité du sol (mv). Les deux polarisations VV et VH

, THSR" utilisant VV seul, VH seul ou VV et VH ensemble) et en utilisant les deux modèles de rétrodiffusion radar (IEM_B et Baghdadi), les meilleurs résultats sont obtenus en utilisant la polarisation VV seule pour le modèle IEM_B et les polarisations VV et VH ensemble pour le modèle semi-empirique de Baghdadi. L'humidité du sol pourra être estimée avec un RMSE de l'ordre de 6 vol.% quand une information a priori sur l'humidité est utilisée dans les réseaux de neurones (construits à partir du modèle IEM_B ou du nouveau modèle semi-empirique). Le deuxième réseau de neurones utilise cette estimation de mv pour estimer la, En utilisant la première configuration d'inversion du signal radar (estimation de la rugosité du sol à très haute résolution spatiale