Modeling the absorption properties of organic carbon in biomass burning smoke in Siberia using remote sensing data

2020 ◽  
Author(s):  
Igor B. Konovalov ◽  
Nikolai A. Golovushkin ◽  
Irina N. Kuznetsova ◽  
Murat I. Nakhaev
Keyword(s):  
Remote Sensing ◽  
Organic Carbon ◽  
Biomass Burning ◽  
Remote Sensing Data ◽  
Absorption Properties ◽  
Sensing Data

Biomass Burning Emissions: A Review of Models Using Remote-Sensing Data

2005 ◽  
Vol 104 (1-3) ◽  
pp. 189-209 ◽  
Author(s):  
Alicia Palacios-Orueta ◽  
Emilio Chuvieco ◽  
Alexander Parra ◽  
C�sar Carmona-Moreno
Keyword(s):  
Remote Sensing ◽  
Biomass Burning ◽  
Remote Sensing Data ◽  
Sensing Data

scholarly journals Estimating dissolved organic carbon inventories in the East China Sea using remote-sensing data

2014 ◽  
Vol 119 (10) ◽  
pp. 6557-6574 ◽  
Author(s):  
Qiong Liu ◽  
Delu Pan ◽  
Yan Bai ◽  
Kai Wu ◽  
Chen-Tung Authur Chen ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Organic Carbon ◽  
Dissolved Organic Carbon ◽  
East China Sea ◽  
Remote Sensing Data ◽  
East China ◽  
The East China Sea ◽  
Sensing Data ◽  
China Sea

scholarly journals Spatial soil organic carbon (SOC) prediction by regression kriging using remote sensing data

2017 ◽  
Vol 20 (1) ◽  
pp. 61-70 ◽  
Author(s):  
Arun Mondal ◽  
Deepak Khare ◽  
Sananda Kundu ◽  
Surajit Mondal ◽  
Sandip Mukherjee ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Organic Carbon ◽  
Soil Organic Carbon ◽  
Remote Sensing Data ◽  
Regression Kriging ◽  
Sensing Data

scholarly journals Mapping Soil Organic Carbon for Airborne and Simulated EnMAP Imagery Using the LUCAS Soil Database and a Local PLSR

2020 ◽  
Vol 12 (20) ◽  
pp. 3451
Author(s):  
Kathrin J. Ward ◽  
Sabine Chabrillat ◽  
Maximilian Brell ◽  
Fabio Castaldi ◽  
Daniel Spengler ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Organic Carbon ◽  
Soil Organic Carbon ◽  
Prediction Accuracy ◽  
Remote Sensing Data ◽  
Soil Samples ◽  
Wet Chemistry ◽  
Sensing Data ◽  
Soil Database ◽  
Local Soil

Soil degradation is a major threat for European soils and therefore, the European Commission recommends intensifying research on soil monitoring to capture changes over time and space. Imaging spectroscopy is a promising technique to create spatially accurate topsoil maps based on hyperspectral remote sensing data. We tested the application of a local partial least squares regression (PLSR) to airborne HySpex and simulated satellite EnMAP (Environmental Mapping and Analysis Program) data acquired in north-eastern Germany to quantify the soil organic carbon (SOC) content. The approach consists of two steps: (i) the local PLSR uses the European LUCAS (land use/cover area frame statistical survey) Soil database to quantify the SOC content for soil samples from the study site in order to avoid the need for wet chemistry analyses, and subsequently (ii) a remote sensing model is calibrated based on the local PLSR SOC results and the corresponding image spectra. This two-step approach is compared to a traditional PLSR approach using measured SOC contents from local samples. The prediction accuracy is high for the laboratory model in the first step with R2 = 0.86 and RPD = 2.77. The HySpex airborne prediction accuracy of the traditional approach is high and slightly superior to the two-step approach (traditional: R2 = 0.78, RPD = 2.19; two-step: R2 = 0.67, RPD = 1.79). Applying the two-step approach to simulated EnMAP imagery leads to a lower but still reasonable prediction accuracy (traditional: R2 = 0.77, RPD = 2.15; two-step: R2 = 0.48, RPD = 1.41). The two-step models of both sensors were applied to all bare soils of the respective images to produce SOC maps. This local PLSR approach, based on large scale soil spectral libraries, demonstrates an alternative to SOC measurements from wet chemistry of local soil samples. It could allow for repeated inexpensive SOC mapping based on satellite remote sensing data as long as spectral measurements of a few local samples are available for model calibration.

Sign in / Sign up

Export Citation Format

Share Document