期刊
REMOTE SENSING
卷 14, 期 3, 页码 -出版社
MDPI
DOI: 10.3390/rs14030740
关键词
proximal soil sensing; soil spectral library; spectroscopy; soil analysis; soil quality; precision agriculture; community practice; soil health monitoring
类别
资金
- Sao Paulo Research Foundation (FAPESP) [2014/22262-0, 2016/26176-6, 2020/04306-0]
Although many Soil Spectral Libraries have been created globally, they have not been operationalized for end-users. To address this, an online Brazilian Soil Spectral Service (BraSpecS) was created. The system allows users to find spectra, estimate soil properties, and act as data custodians.
Although many Soil Spectral Libraries (SSLs) have been created globally, these libraries still have not been operationalized for end-users. To address this limitation, this study created an online Brazilian Soil Spectral Service (BraSpecS). The system was based on the Brazilian Soil Spectral Library (BSSL) with samples collected in the Visible-Near-Short-wave infrared (vis-NIR-SWIR) and Mid-infrared (MIR) ranges. The interactive platform allows users to find spectra, act as custodians of the data, and estimate several soil properties and classification. The system was tested by 500 Brazilian and 65 international users. Users accessed the platform (besbbr.com.br), uploaded their spectra, and received soil organic carbon (SOC) and clay content prediction results via email. The BraSpecS prediction provided good results for Brazilian data, but performed variably for other countries. Prediction for countries outside of Brazil using local spectra (External Country Soil Spectral Libraries, ExCSSL) mostly showed greater performance than BraSpecS. Clay R-2 ranged from 0.5 (BraSpecS) to 0.8 (ExCSSL) in vis-NIR-SWIR, but BraSpecS MIR models were more accurate in most situations. The development of external models based on the fusion of local samples with BSSL formed the Global Soil Spectral Library (GSSL). The GSSL models improved soil properties prediction for different countries. Nevertheless, the proposed system needs to be continually updated with new spectra so they can be applied broadly. Accordingly, the online system is dynamic, users can contribute their data and the models will adapt to local information. Our community-driven web platform allows users to predict soil attributes without learning soil spectral modeling, which will invite end-users to utilize this powerful technique.
作者
我是这篇论文的作者
点击您的名字以认领此论文并将其添加到您的个人资料中。
推荐
暂无数据