TY - GEN
T1 - Integrating microwave and optical data for monitoring soil moisture
AU - Morgan, R. S.
AU - Abd El-Hady, M.
AU - Rahim, I. S.
AU - Silva, Joel
AU - Berg, A.
N1 - Morgan, R. S., Abd El-Hady, M., Rahim, I. S., Silva, J., & Berg, A. (2016). Integrating microwave and optical data for monitoring soil moisture. In L. Ouwehand (Ed.), Proceedings of Living Planet Symposium 2016 (European Space Agency, (Special Publication) ESA SP; Vol. SP-740). European Space Agency.
PY - 2016/8/1
Y1 - 2016/8/1
N2 - In arid regions, such as Egypt, irrigation is the main source of water consumption and freshwater resources are getting scarcer. Therefore, the development of an appropriate irrigation water practices becomes a necessity. Soil moisture, in particular, plays a key role in any efficient water use strategy for agriculture. This study aims at suggesting a protocol for processing microwave data (Sentinel-1) supported by optical data (Landsat 8) with and without ancillary data and utilizing Artificial Neural Network (ANN) to provide repeatable, reliable and accurate estimation of soil moisture content and at a practical interval. The results of this study suggested two approaches for soil moisture predictions using Sentienl-1 data. The first approach depended totally on remote sensing data with a correlation of 0.76. The second approach is more suitable when accurate detailed field survey of soil field capacity is available and reached a correlation of about 0.98.
AB - In arid regions, such as Egypt, irrigation is the main source of water consumption and freshwater resources are getting scarcer. Therefore, the development of an appropriate irrigation water practices becomes a necessity. Soil moisture, in particular, plays a key role in any efficient water use strategy for agriculture. This study aims at suggesting a protocol for processing microwave data (Sentinel-1) supported by optical data (Landsat 8) with and without ancillary data and utilizing Artificial Neural Network (ANN) to provide repeatable, reliable and accurate estimation of soil moisture content and at a practical interval. The results of this study suggested two approaches for soil moisture predictions using Sentienl-1 data. The first approach depended totally on remote sensing data with a correlation of 0.76. The second approach is more suitable when accurate detailed field survey of soil field capacity is available and reached a correlation of about 0.98.
KW - Landsat 8
KW - Neural Network
KW - Sentinel-1
KW - Soil moisture content
KW - Water resource management
UR - http://www.scopus.com/inward/record.url?scp=84988474893&partnerID=8YFLogxK
M3 - Conference contribution
AN - SCOPUS:84988474893
T3 - European Space Agency, (Special Publication) ESA SP
BT - Proceedings of Living Planet Symposium 2016
A2 - Ouwehand, L.
PB - European Space Agency
T2 - Living Planet Symposium 2016
Y2 - 9 May 2016 through 13 May 2016
ER -
