In:
Photogrammetric Engineering & Remote Sensing, American Society for Photogrammetry and Remote Sensing, Vol. 85, No. 11 ( 2019-11-01), p. 789-798
Abstract:
In order to eliminate the influences of surface roughness and vegetation on radar signals in the vegetation-covered soil moisture estimation, the present paper proposes a combining method based on modified particle swarm optimization (MPSO) and back-propagation (BP) neural network algorithm.
This method combines optical and radar data at the field scale and uses MPSO to optimize the weight of the neural network. An effective inertia weight is introduced in the MPSO and an implicit relationship between backscatter coefficient and soil moisture is established. Experimental results show that the combining method produces better accuracy than other inversion methods with R 2 of 72.2% and Root Mean Square Error (RMSE) of 0.033 cm 3 /cm 3 , respectively. Meanwhile, the estimated accuracy of surface soil moisture using radar and optical data simultaneously
is much higher than that using only a single data source as input with R 2 of 0.827 and RMSE of 0.029 cm 3 /cm 3 . Therefore, the combining method can effectively improve the accuracy of soil moisture retrieval and provide support for large-scale agricultural monitoring.
Type of Medium:
Online Resource
ISSN:
0099-1112
DOI:
10.14358/PERS.85.11.789
Language:
English
Publisher:
American Society for Photogrammetry and Remote Sensing
Publication Date:
2019
detail.hit.zdb_id:
2317128-5
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