E. D. Warner

National Scale Analysis of Soil Organic Carbon Storage in China Based on Chinese Soil Taxonomy

Patterns of soil organic carbon (SOC) storage and density in various soil types or locations are the foundation for examining the role of soil in the global carbon cycle. An assessment of SOC storage and density patterns in China based on soil types as defined by Chinese Soil Taxonomy (CST) and the recently compiled digital 1:1000000 Soil Database of China was conducted to generate a rigorous database for the future study of SOC storage. First, SOC densities of 7292 soil profiles were calculated and linked by soil type to polygons of a digital soil map using geographic information system resulting in a 1:1000000 SOC density distribution map of China. Further results showed that soils in China covered 9281×10km^2 with a total SOC storage of 89.14 Gt and a mean SOC density 96.0t ha^(-1). Among the 14 CST orders, Cambosols and Argosols constituted high percentage of Chinas total SOC storage, while Andosols, Vertosols, and Spodsols had a low percentage. As for SOC density, Histosols were the highest, while Primosols were the lowest. Specific patterns of SOC storage of various soil types at the CST suborder, group, and subgroup levels were also described. Results obtained from the study of SOC storage and density of all CST soil types would be not only useful for international comparative research, but also for more accurately estimating and monitoring of changes of SOC storage in China.

Relationship between oriental migratory locust plague and soil moisture extracted from MODIS data

Abstract Locust plagues have been the source of some of the most severe natural disasters in human history. Soil moisture content is among the most important of the numerous factors influencing plague onset and severity. This paper describes a study initiated in three pilot locust plague monitoring regions, i.e., Huangzao, Yangguanzhuang, and Tengnan in Huanghua county, Hebei province, China, to examine the impact of soil moisture status on oriental migratory locust [ Locusta migratoria manilensis (L.) Meyen] plague breakout as related to the life cycle, oviposition in autumn, survival in winter, and incubation in summer. Thirty-nine temperature vegetation dryness index (TVDI) data sets, which represent soil moisture content, were extracted from MODIS remote sensing images for two representative time periods: a severe locust plague breakout year (2001–2002) and a slight plague year (2003–2004). TVDI values demonstrated distinctive soil moisture status differences between the 2 years concerned. Soil moisture conditions in the severe plague year were shown to be lower than those in slight plague year. In all three pilot regions, average TVDI value in the severe plague year was 0.07 higher than that in slight plague year, and monthly TVDI values in locust oviposition period (September and October) and incubation period (March, April and May) were higher than their corresponding monthly figures in slight plague year. No remarkable TVDI differences were found in other months during the locust life cycle between the 2 years. TVDI values for September and October (2001), March, April and May (2002) were 0.11, 0.08, 0.16, 0.11 and 0.16 higher than their corresponding monthly figures in 2003–2004 period, respectively.

Application of categorical information in the spatial prediction of soil organic carbon in the red soil area of China

Abstract Predicting soil organic carbon (SOC) content distribution accurately from limited soil samples has received a great deal of attention recently in an effort to support soil fertility mapping and to improve our understanding of carbon sequestration variability. Kriging methods combined with auxiliary variables are frequently used at present. However, studies using categorical information, such as soil type and land use, which are closely related to local trends in SOC spatial variation, as auxiliary variables are seldom conducted. In the present investigation, a total of 254 surficial soil samples were collected in the study area, Yujiang county in the hilly red soil region of South China, and a comparison of performance of four kriging approaches was conducted, ordinary kriging (OK), kriging combined with soil-type information (KST), land use (KLU) and combined land use–soil type information (KLUST). Results of the assessment were based on 85 validation samples. The results indicate that the best correlation between the measured and predicted values for validation location was obtained with KLUST (r = 0.854), whereas the lowest was obtained using OK (r = 0.383). Furthermore, the root mean square error (RMSE) from KLUST (3.47 g kg−1) is the lowest, whereas the one obtained using OK (6.49 g kg−1) is the highest. The correlation coefficient and RMSE from KST (r = 0.784, RMSE = 4.15 g kg−1) and KLU (r = 0.795, RMSE = 3.95 g kg−1) are the second and third most correlated, respectively. Comparing the SOC distribution maps generated by the four prediction approaches, the KLUST rendering best reflects the local change associated with soil types and land uses, whereas the map from the OK is the least representative. The results demonstrate that soil type and land use have an important impact on SOC spatial distribution, and KLUST, which reduces their influence as a local trend, is an efficient and practical prediction approach for the hilly red soil region of South China.

A WebGIS system for relating genetic soil classification of China to soil taxonomy

Soil classification is the basis for the exchange of soil science research results and the foundation for the application of modern soil resource management methods. A WebGIS-based system designed to relate genetic soil classification of China (GSCC) to soil taxonomy (ST) was developed to enhance global cooperation and to support communication between China and the other countries on important agricultural and environmental issues. The system has a Browse Server (B/S) structure and exploits the 1:1,000,000 soil databases of China using WebGIS functionality. This paper describes the application of the WebGIS system for easily accessing cross-reference information between GSCC to ST. First, we describe the three-level B/S structure of the system. The cross-reference methodologies, referenceability and maximum referenceability, are then explained and applied at three geographic scales (i.e. nation, region and pedon). Finally, three sub-modules based on the supported scales are described and illustrated with application scenarios to familiarize users with the inquiry system and its usage. The main advantage of the system is that it considers statistical similarity in the spatial distributions between the two different classification systems. Users with limited knowledge are able to obtain soil cross-reference information using an intuitive interface, which supports query, visualization and analysis via a web browser at the most detailed level. The inquiry system benefits the development of soil classification science and international academic exchange.

C‐band, multi‐angle SAR imaging of agricultural cover

The launch of the Canadian Space Agency’s RADARSAT will provide the capability to supply imagery acquired from different instrument look angles, resulting in a multi‐incidence view of an area of interest. Previous research has utilized multi‐incidence imaging to map land use/land cover and to model soil moisture content. This research utilizes an innovative approach to the use of multi‐angle SAR imagery to detect differences in land use/land cover. In this study, plant canopies and bare soil are imaged with multi‐angle and multi‐frequency data acquired with the airborne NASA/JPL AIRSAR instrument. Findings are relevant for possible applications of data from the soon to be orbited RADARSAT instrument. Backscatter from a distributed target, such as vegetation, is the product of microwave absorption and scattering interactions with all features within the imaged area. The intensity of the backscatter from the area varies with the angle of the incident wave. This investigation uses this physical understanding...

An assessment of soil productivity loss caused by expanding urban land use using remote sensing and soil productivity models

An EOS IDS project has been recently designed to assess the loss of soil productivity resulting from expanding urbanization in the U.S. and selected regions in Mexico and the Middle East using remotely sensed data and soil productivity models. The extent of urbanization will be determined by generating urban land cover layers from DMSP/OLS (Defense Meteorological Satellite Program’s Operational Linescan System) nighttime imagery. This imagery will be calibrated using Landsat Thematic Mapper (TM) and population/housing census data. A range of soil/land productivity models will be evaluated using soil factors computed from the State Soil Geographic Database (STATSGO) and FAO soil databases, terrain models, climate and vegetation to rank soil mapping units based on their productivity potential. Examples of these models are the Net Primary Productivity (NPP) and FAO Fertility Capability Classification (FCC) system. The magnitude of soil productivity loss due to urbanization will finally be determined by analysis of data obtained from GIS overlays of urban land use and soil productivity layers.