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利用星载高光谱影像评估非光合农田生物量

Assessing Non-Photosynthetic Cropland Biomass from Spaceborne Hyperspectral Imagery.

作者信息

Berger Katja, Hank Tobias, Halabuk Andrej, Rivera-Caicedo Juan Pablo, Wocher Matthias, Mojses Matej, Gerhátová Katarina, Tagliabue Giulia, Dolz Miguel Morata, Venteo Ana Belen Pascual, Verrelst Jochem

机构信息

Department of Geography, Ludwig-Maximilians-Universitat Munchen (LMU), Luisenstr. 37, 80333 Munich, Germany.

Institute of Landscape Ecology, Slovak Academy of Sciences, Branch Nitra, 949 01 Nitra, Slovakia.

出版信息

Remote Sens (Basel). 2021 Nov 21;13(22):4711. doi: 10.3390/rs13224711.

DOI:10.3390/rs13224711
PMID:36082004
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC7613388/
Abstract

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) 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.

摘要

非光合植被（NPV）生物量已被确定为即将开展的星载成像光谱任务的一个优先变量，这需要对木质纤维素植物材料进行定量估计，而不仅仅是表面覆盖的指示。因此，我们提出了一种用于反演非光合农田生物量的混合模型。工作流程包括将叶片光学模型PROSPECT-PRO与冠层反射率模型4SAIL耦合，这使我们能够从基于碳的成分（CBC）和叶面积指数（LAI）模拟NPV生物量。PROSAIL-PRO为高斯过程回归（GPR）算法提供了一个训练数据库，模拟了广泛的非光合植被状态。采用主动学习来减少和优化训练数据集。此外，我们应用光谱降维来压缩非光合信号的基本信息。所得的NPV-GPR模型通过大豆田数据成功验证，归一化均方根误差（nRMSE）为13.4%，决定系数（R）为0.85。为了展示制图能力，NPV-GPR模型在德国慕尼黑北部农业地区获取的PRISMA高光谱图像上进行了测试。如模型不确定性所示，可靠估计主要在衰老植被区域实现。所提出的工作流程是朝着将非光合农田生物量量化作为来自近期运行任务（如CHIME）的下一代产品迈出的第一步。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/7334/7613388/870307763195/EMS152677-f009.jpg
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3
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4
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4
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5
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