Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely: C-band repeat pass interferometric synthetic aperture radar coherence can provide high-accuracy estimates of boreal forest stem volume in spite of environmental dependence. Typically, the retrieval methods require a data set of  in   situ  measurements for training the model linking coherence to stem volume. The drawback is the need for such data and the incapacity to take into account spatial variations of environmental conditions. Here,  demonstrate a model training method that does not require reference data. For the investigated case, the relative root mean square error of stem volume is 18% or as good as obtained using the traditional training method with  in   situ  data.
['J. Askne', 'Maurizio Santoro']
I am writing a research paper on a idea Automatic Model-Based Estimation of Boreal Forest Stem Volume From Repeat Pass C-band InSAR Coherence
[{"from": "human", "value": "I am writing a research paper on a idea Automatic Model-Based Estimation of Boreal Forest Stem Volume From Repeat Pass C-band InSAR Coherence"}, {"from": "gpt", "value": "Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely: C-band repeat pass interferometric synthetic aperture radar coherence can provide high-accuracy estimates of boreal forest stem volume in spite of environmental dependence. Typically, the retrieval methods require a data set of  in   situ  measurements for training the model linking coherence to stem volume. The drawback is the need for such data and the incapacity to take into account spatial variations of environmental conditions. Here,  demonstrate a model training method that does not require reference data. For the investigated case, the relative root mean square error of stem volume is 18% or as good as obtained using the traditional training method with  in   situ  data."}]