Is vegetation optical depth needed to estimate biomass from passive microwave radiometers? A statistical study using neural networks
- Resource Type
- Conference
- Authors
- Rodriguez-Fernandez, N. J.; Richaume, P.; Bousquet, E.; Mialon, A.; Bitar, A. Al; Saatchi, S.; Kerr, Y. H.
- Source
- IGARSS 2019 - 2019 IEEE International Geoscience and Remote Sensing Symposium Geoscience and Remote Sensing Symposium, IGARSS 2019 - 2019 IEEE International. :5496-5499 Jul, 2019
- Subject
- Aerospace
Geoscience
Signal Processing and Analysis
Vegetation mapping
Soil moisture
Artificial neural networks
Biomedical optical imaging
Optical network units
Optical sensors
L-Band
Vegetation Optical Depth
Soil Moisture and Ocean Salinity satellite
- Language
- ISSN
- 2153-7003
Neural networks were used to estimate the ability of different sets of predictors to capture the variability of above ground biomass (AGB). SMOS brightness temperatures (TBs) for only two incidence angles capture 85% of the AGB variance. Adding soil moisture or L-band vegetation optical depth (L-VOD) increase the ability to capture the AGB variance to 90 % and 92 %, respectively. With respect to using only TBs, L-VOD improves the AGB estimation in regions of low vegetation.