Modeling spatial patterns of soil respiration in maize fields from vegetation and soil property factors with the use of remote sensing and geographical information system.

Ni Huang,Li Wang,Zheng Niu,Yiqiang Guo,Pengyu Hao,Ben Bond-Lamberty

doi:10.1371/journal.pone.0105150

Abstract

To examine the method for estimating the spatial patterns of soil respiration (Rs) in agricultural ecosystems using remote sensing and geographical information system (GIS), Rs rates were measured at 53 sites during the peak growing season of maize in three counties in North China. Through Pearson's correlation analysis, leaf area index (LAI), canopy chlorophyll content, aboveground biomass, soil organic carbon (SOC) content, and soil total nitrogen content were selected as the factors that affected spatial variability in Rs during the peak growing season of maize. The use of a structural equation modeling approach revealed that only LAI and SOC content directly affected Rs. Meanwhile, other factors indirectly affected Rs through LAI and SOC content. When three greenness vegetation indices were extracted from an optical image of an environmental and disaster mitigation satellite in China, enhanced vegetation index (EVI) showed the best correlation with LAI and was thus used as a proxy for LAI to estimate Rs at the regional scale. The spatial distribution of SOC content was obtained by extrapolating the SOC content at the plot scale based on the kriging interpolation method in GIS. When data were pooled for 38 plots, a first-order exponential analysis indicated that approximately 73% of the spatial variability in Rs during the peak growing season of maize can be explained by EVI and SOC content. Further test analysis based on independent data from 15 plots showed that the simple exponential model had acceptable accuracy in estimating the spatial patterns of Rs in maize fields on the basis of remotely sensed EVI and GIS-interpolated SOC content, with R2 of 0.69 and root-mean-square error of 0.51 µmol CO2 m−2 s−1. The conclusions from this study provide valuable information for estimates of Rs during the peak growing season of maize in three counties in North China.

Highlights

Soil CO2 efflux from terrestrial ecosystems to the atmosphere has been considered the second largest global carbon flux and is a vital component of ecosystem respiration [1]
On the basis of the theoretical knowledge on the major factors that influence spatial patterns of Rs at regional scales [8,13,26], we developed an structural equation modeling (SEM) model to relate Rs to soil organic carbon (SOC) content, soil total nitrogen (STN) content, leaf area index (LAI), aboveground biomass (AGB), and Chlcanopy
The vegetation indices (VIs) modified the effect of soil reflectance (i.e. modified soil adjusted vegetation index (MSAVI)) did not exhibit a significantly greater advantage than normalized difference vegetation index (NDVI), which is strongly affected by soil reflectance in sparsely vegetated areas [50]

Summary

Introduction

Soil CO2 efflux from terrestrial ecosystems to the atmosphere has been considered the second largest global carbon flux and is a vital component of ecosystem respiration [1]. Understanding the spatial and temporal changes of these sources will facilitate the modeling of Rs. the large spatial and temporal heterogeneity of root and microbial activity within the landscape and the covariation of potentially important factors (i.e., temperature and water content) pose great challenges to the development of mechanistically based models that account for spatial and temporal variability in Rs [2]. Aside from soil temperature and moisture, plant productivity proxies [e.g., leaf area index (LAI), canopy chlorophyll content (Chlcanopy), and plant biomass] [8,9,10] and soil properties [e.g., soil organic carbon (SOC) content, soil total nitrogen (STN) content, and soil C and N ratio (soil C/N)] [11,12] potentially influence Rs and are often included in models of Rs. most of the factors that affect variations in Rs tend to be derived through field measurements [13]. A simple method to derive data related to variations in Rs is necessary to facilitate the determination of the spatial and temporal distribution of Rs

Objectives

Methods

Findings

Discussion

Conclusion