NDLI: A novel hybrid approach to the estimation of biophysical parameters from remotely sensed data

Content Provider	IEEE Xplore Digital Library
Author	Pasolli, L. Bruzzone, L. Notarnicola, C.
Copyright Year	2011
Description	Author affiliation: Institute for Applied Remote Sensing, EURAC Research, Viale Druso, 1 I-39100 Bolzano Italy (Notarnicola, C.) \|\| Dep. of Information Engineering and Computer Science, University of Trento, Via Sommarive, 14, I-38123 Italy (Pasolli, L.; Bruzzone, L.)
Abstract	This paper presents a novel hybrid approach to the estimation of biophysical parameters from remotely sensed data. This approach integrates theoretical analytical models and empirical models based on field reference samples to increase the reliability and the accuracy of the estimation. The estimation process is modeled by two terms: the first one expresses the relationship between the input features and the target biophysical variable according a theoretical model based on the physics of the considered problem; the second one corrects the deviation between theoretical model estimates and true target values according to an empirical data-driven model. The latter is derived by exploiting the available (typically few) field reference samples. In this way the robustness and generality of theoretical model based estimates, which stem from the rigorous theoretical foundation, is preserved, while the bias and imprecision (due to simplifications in the analytical formulations of the model with respect to the real estimation process) are reduced. Results achieved for the specific application of soil moisture estimation from microwave remotely sensed data with two different correction strategies are reported. These results show the effectiveness and the potentiality of the proposed integration approach.
Starting Page	1231
Ending Page	1234
File Size	211127
Page Count	4
File Format	PDF
ISBN	9781457710032
ISSN	21537003
e-ISBN	9781457710056
DOI	10.1109/IGARSS.2011.6049421
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2011-07-24
Publisher Place	Canada
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Estimation Remote sensing Biological system modeling Analytical models Soil moisture Accuracy Quantization soil moisture Remote Sensing biophysical parameters estimation data assimilation
Content Type	Text
Resource Type	Article

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