Monitoring Soil Water and Organic Carbon Storage Patterns at the Arctic Foothills, Alaska, Using Insar

Abstract. A new approach for the estimation of Soil Organic Carbon (SOC) pools North of the tree line has been developed based on synthetic aperture radar data (SAR). SOC values are directly determined from backscatter values instead of upscaling using land cover or soil classes. The multi-mode capability of SAR allows application across scales. It can be shown that measurements in C-band under frozen conditions represent vegetation and surface structure properties which relate to soil properties, specifically SOC. It is estimated that at least 29 PgC are stored in the upper 30 cm of soils North of the tree line. This is approximately 25 % less than stocks derived from the soil map based Northern Circumpolar Soil Carbon Database (NCSCD). The total stored carbon is underestimated since the established empirical relationship is not valid for peatlands as well as strongly cryoturbated soils. The approach does however provide the first spatially consistent account of soil organic carbon across the Arctic. Furthermore, it could be shown that values obtained from 1 km resolution SAR correspond to accounts based on a high spatial resolution (2 m) land cover map over a study area of about 7 x 7 km in NE Siberia. The approach can be also potentially transferred to medium resolution C-band SAR data such as ENVISAT ASAR Wide Swath with 120 m resolution but it is in general limited to regions without woody vegetation. Comparisons to the length of unfrozen period indicates the suitability of this parameter for modelling of the spatial distribution of soil organic carbon storage.

Download Full-text

Available water capacity and organic carbon storage profiles in soils developed from dark brown soil to boggy soil in Changbai Mountains, China

Soil and Water Research ◽

10.17221/150/2019-swr ◽

2020 ◽

Vol 16 (No. 1) ◽

pp. 11-21

Author(s):

Dandan Yu ◽

Feilong Hu ◽

Kun Zhang ◽

Li Liu ◽

Danfeng Li

Keyword(s):

Organic Carbon ◽

Soil Water ◽

Carbon Storage ◽

Development Process ◽

Soil Development ◽

Changbai Mountains ◽

Water Capacity ◽

Brown Soil ◽

Available Water Capacity ◽

Organic Carbon Storage

The available water capacity (AWC) is the most commonly used parameter for quantifying the amount of soil water that is readily available to plants. Specific AWC and soil organic carbon storage (SOCS) profiles are consequences of the soil development process. Understanding the distributions of AWC and SOCS in soil profiles is crucial for modelling the coupling between carbon and water cycle processes, and for predicting the consequences of global change. In this study, we determined the variations in the AWC and SOCS from the surface to a depth of 100 cm in soils developed from dark brown soil, skeletal dark brown soil, meadow dark brown soil, white starched dark brown soil, meadow soil, and boggy soil in the Changbai Mountains area of China. The AWC and SOCS profiles were calculated for each main soil group/subgroup using only the readily available variables for the soil texture and organic matter with the soil water characteristic equations. The results showed the following. (1) The AWC and SOCS decreased initially and then increased, before decreasing again in soils developed from dark brown soil to boggy soil, where the maximum SOCS occurred in the white starched dark brown soil, and the maximum AWC in the dark brown soil. (2) The SOCS was decreased by deforestation and concomitant soil erosion, but the negative impact of this decrease in the SOCS in the Changbai Mountains area was not caused completely by reductions in AWC. (3) In the soil development process from dark brown soil to boggy soil in response to deforestation, the AWC distribution differed in the profile and even among individual layers, whereas the SOCS was mainly present in the upper layer.

Download Full-text

Immediate and long-term effects of tillage practices with crop residue on soil water and organic carbon storage changes under a wheat-maize cropping system

Soil and Tillage Research ◽

10.1016/j.still.2021.105309 ◽

2022 ◽

Vol 218 ◽

pp. 105309

Author(s):

Hongxiang Zhao ◽

Jihao Qin ◽

Tianping Gao ◽

Mengkun Zhang ◽

Hongchang Sun ◽

...

Keyword(s):

Organic Carbon ◽

Soil Water ◽

Carbon Storage ◽

Crop Residue ◽

Cropping System ◽

Long Term Effects ◽

Tillage Practices ◽

Organic Carbon Storage

Download Full-text

Elevation Distribution of Fine Root Biomass and Soil Organic Carbon Storage of Mature Chinese Fir (Cunninghamia lanceolata) Plantations in East China

Polish Journal of Ecology ◽

10.3161/15052249pje2020.68.1.007 ◽

2020 ◽

Vol 68 (1) ◽

pp. 84

Author(s):

Qin Wang ◽

Zhen Teng ◽

Jingjing Wang ◽

Xinyun Xie ◽

Xiaoniu Xu

Keyword(s):

Organic Carbon ◽

Soil Organic Carbon ◽

Carbon Storage ◽

Root Biomass ◽

Fine Root ◽

Fine Root Biomass ◽

Chinese Fir ◽

Cunninghamia Lanceolata ◽

East China ◽

Organic Carbon Storage

Download Full-text

Response characteristics of organic carbon storage in sandy soil to sandstone

IOP Conference Series Earth and Environmental Science ◽

10.1088/1755-1315/514/2/022008 ◽

2020 ◽

Vol 514 ◽

pp. 022008

Author(s):

Zhen Guo ◽

Tingting Cao ◽

Chenxi Yang

Keyword(s):

Organic Carbon ◽

Carbon Storage ◽

Sandy Soil ◽

Response Characteristics ◽

Organic Carbon Storage

Download Full-text

A Two-Stage Approach to the Estimation of High-Resolution Soil Organic Carbon Storage with Good Extension Capability

Land ◽

10.3390/land10050517 ◽

2021 ◽

Vol 10 (5) ◽

pp. 517

Author(s):

Sunwei Wei ◽

Zhengyong Zhao ◽

Qi Yang ◽

Xiaogang Ding

Keyword(s):

Organic Carbon ◽

Soil Organic Carbon ◽

Carbon Storage ◽

Guangdong Province ◽

Soil Samples ◽

Estimation Accuracy ◽

Ann Model ◽

Soil Layers ◽

Second Stage ◽

Organic Carbon Storage

Soil organic carbon storage (SOCS) estimation is a crucial branch of the atmospheric–vegetation–soil carbon cycle study under the background of global climate change. SOCS research has increased worldwide. The objective of this study is to develop a two-stage approach with good extension capability to estimate SOCS. In the first stage, an artificial neural network (ANN) model is adopted to estimate SOCS based on 255 soil samples with five soil layers (20 cm increments to 100 cm) in Luoding, Guangdong Province, China. This method is compared with three common methods: The soil type method (STM), ordinary kriging (OK), and radial basis function (RBF) interpolation. In the second stage, a linear model is introduced to capture the regional differences and further improve the estimation accuracy of the Luoding-based ANN model when extending it to Xinxing, Guangdong Province. This is done after assessing the generalizability of the above four methods with 120 soil samples from Xinxing. The results for the first stage show that the ANN model has much better estimation accuracy than STM, OK, and RBF, with the average root mean square error (RMSE) of the five soil layers decreasing by 0.62–0.90 kg·m−2, R2 increasing from 0.54 to 0.65, and the mean absolute error decreasing from 0.32 to 0.42. Moreover, the spatial distribution maps produced by the ANN model are more accurate than those of other methods for describing the overall and local SOCS in detail. The results of the second stage indicate that STM, OK, and RBF have poor generalizability (R2 < 0.1), and the R2 value obtained with ANN method is also 43–56% lower for the five soil layers compared with the estimation accuracy achieved in Luoding. However, the R2 of the linear models built with the 20% soil samples from Xinxing are 0.23–0.29 higher for the five soil layers. Thus, the ANN model is an effective method for accurately estimating SOCS on a regional scale with a small number of field samples. The linear model could easily extend the ANN model to outside areas where the ANN model was originally developed with a better level of accuracy.

Download Full-text