Review and evaluation of remote sensing methods for soil-moisture estimation
2011
Soil-moisture information plays an important role in disaster predictions, environmental
monitoring, and hydrological applications. A large number of research papers have
introduced a variety of methods to retrieve soil-moisture information from different types of
remote sensing data, such as optical data or radar data. We evaluate the most robust methods for
retrieving soil-moisture information of bare soil and vegetation-covered soil. We begin with an
introduction to the importance and challenges of soil-moisture information extraction and the
development of soil-moisture retrieval methods. An overview of soil-moisture retrieval methods
using different remote sensing data is presented-either active or passive or a combination of
both active and passive remote sensing data. The results of the methods are compared, and
the advantages and limitations of each method are summarized. The comparison shows that
using a statistical method gives the best results among others in the group: a combination of
both active and passive sensing methods, reaching a 1.83% gravimetric soil moisture (%GSM)
root-mean-square error (RMSE) and a 96% correlation between the estimated and field soil
measurements. In the group of active remote sensing methods, the best method is a backscatter
empirical model, which gives a 2.32-1.81%GSM RMSE and a 95-97% correlation between the
estimated and the field soil measurements. Finally, among the group of passive remote sensing
methods, a neural networks method gives the most desirable results: a 0.0937%GSM RMSE
and a 100% correlation between the estimated and field soil measurements. Overall, the newly
developed neural networks method with passive remote sensing data achieves the best results
among all the methods reviewed.
Keywords:
- Correction
- Source
- Cite
- Save
- Machine Reading By IdeaReader
63
References
38
Citations
NaN
KQI