TY - GEN
T1 - High-Resolution Soil Moisture Retrieval Using C-Band Radar Sentinel-1 and Cosmic-ray Neutron Sensor Data
AU - Said, Hami
AU - Mbaye, Modou
AU - Heng, Lee Kheng
AU - Weltin, Georg
AU - Franz, Trenton
AU - Dercon, Gerd
AU - Toloza, Arsenio
AU - Strauss, Peter
AU - Rab, Gerhard
N1 - Publisher Copyright:
© 2021 IEEE.
PY - 2021
Y1 - 2021
N2 - Agricultural water management is key for a sustainable agricultural productivity. Accurate soil moisture measurements are essential in irrigation management, hydrological modelling, ground water recharge, flood, and drought forecasting. There are many different techniques for estimating soil moisture at different scales, from point to landscape scales. In this paper, we used a modified version of change detection approach for near surface soil moisture modelling coupled with a Cosmic-Ray Neutron Sensor (CRNS) installed at a field site 100 km outside Vienna, Austria. The model allows a conversion of Vertical-Vertical (VV) polarization into soil moisture. The model is calibrated with 2019 CRNS data and validated with data from 2020 to 2021. The results showed the good performance of the model with a high correlation for the calibration (R2 = 0.81) and predicted the soil moisture with an absolute error of 0.2%. This study is a major step in the monitoring of soil moisture at high spatial and temporal resolution by combining remote sensing and the CRNS based nuclear technology.
AB - Agricultural water management is key for a sustainable agricultural productivity. Accurate soil moisture measurements are essential in irrigation management, hydrological modelling, ground water recharge, flood, and drought forecasting. There are many different techniques for estimating soil moisture at different scales, from point to landscape scales. In this paper, we used a modified version of change detection approach for near surface soil moisture modelling coupled with a Cosmic-Ray Neutron Sensor (CRNS) installed at a field site 100 km outside Vienna, Austria. The model allows a conversion of Vertical-Vertical (VV) polarization into soil moisture. The model is calibrated with 2019 CRNS data and validated with data from 2020 to 2021. The results showed the good performance of the model with a high correlation for the calibration (R2 = 0.81) and predicted the soil moisture with an absolute error of 0.2%. This study is a major step in the monitoring of soil moisture at high spatial and temporal resolution by combining remote sensing and the CRNS based nuclear technology.
KW - Agricultural water management
KW - C-Band SAR Sentinel-1
KW - Cosmic-Ray Neutron Sensor
KW - Remote sensing
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U2 - 10.1109/MetroAgriFor52389.2021.9628594
DO - 10.1109/MetroAgriFor52389.2021.9628594
M3 - Conference contribution
AN - SCOPUS:85123410002
T3 - 2021 IEEE International Workshop on Metrology for Agriculture and Forestry, MetroAgriFor 2021 - Proceedings
SP - 221
EP - 225
BT - 2021 IEEE International Workshop on Metrology for Agriculture and Forestry, MetroAgriFor 2021 - Proceedings
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 3rd IEEE International Workshop on Metrology for Agriculture and Forestry, MetroAgriFor 2021
Y2 - 3 November 2021 through 5 November 2021
ER -