Assessing Non-Photosynthetic Cropland Biomass from Spaceborne Hyperspectral Imagery

Remote Sensing

Katja Berger¹  Matthias Wocher¹  Tobias Hank¹  Jochem Verrelst²  Miguel Morata Dolz²  Ana Belen Pascual Venteo²  Katarina Gerhátová³  Matej Mojses³  Andrej Halabuk³  Giulia Tagliabue⁴  Juan Pablo Rivera-Caicedo⁵ 

[1] Department of Geography, Ludwig-Maximilians-Universität München (LMU), Luisenstr. 37, 80333 Munich, Germany;Image Processing Laboratory (IPL), Parc Científic, Universitat de València, 46980 Paterna, Spain;Institute of Landscape Ecology, Slovak Academy of Sciences, Branch Nitra, 949 01 Nitra, Slovakia;Remote Sensing of Environmental Dynamics Lab, University Milano-Bicocca, 20126 Milano, Italy;Secretary of Research and Postgraduate, CONACYT-UAN, Tepic 63155, Mexico;

关键词: PRISMA;    CHIME;    NPV;    Gaussian process regression;    hybrid retrieval;    active learning;   

DOI  :  10.3390/rs13224711

来源: DOAJ

【 摘 要 】

Non-photosynthetic vegetation (NPV) biomass has been identified as a priority variable for upcoming spaceborne imaging spectroscopy missions, calling for a quantitative estimation of lignocellulosic plant material as opposed to the sole indication of surface coverage. Therefore, we propose a hybrid model for the retrieval of non-photosynthetic cropland biomass. The workflow included coupling the leaf optical model PROSPECT-PRO with the canopy reflectance model 4SAIL, which allowed us to simulate NPV biomass from carbon-based constituents (CBC) and leaf area index (LAI). PROSAIL-PRO provided a training database for a Gaussian process regression (GPR) algorithm, simulating a wide range of non-photosynthetic vegetation states. Active learning was employed to reduce and optimize the training data set. In addition, we applied spectral dimensionality reduction to condense essential information of non-photosynthetic signals. The resulting NPV-GPR model was successfully validated against soybean field data with normalized root mean square error (nRMSE) of 13.4% and a coefficient of determination ($R^2$) of 0.85. To demonstrate mapping capability, the NPV-GPR model was tested on a PRISMA hyperspectral image acquired over agricultural areas in the North of Munich, Germany. Reliable estimates were mainly achieved over senescent vegetation areas as suggested by model uncertainties. The proposed workflow is the first step towards the quantification of non-photosynthetic cropland biomass as a next-generation product from near-term operational missions, such as CHIME.

【 授权许可】

Unknown