Xiaoyuan Geng

Organic Soils of canada: Part 1. Wetland Organic soils

Kroetsch, D. J., Geng, X., Chang, S. X. and Saurette, D. D. 2011. Organic soils of Canada:Part 1. Wetland Organic soils. Can. J. Soil Sci. 91: 807-822. In the Canadian System of Soil Classification, the Organic order represents those soils that have developed from materials that are comprised primarily of plant tissue remains and includes both wetland Organic soils and upland Organic soils. This review focuses on the first group; the latter group is discussed in Fox and Tarnocai (2011). Wetland Organic soils can be subdivided into three great groups: Fibrisol, Mesisol, and Humisol, reflecting the degree of decomposition of organic material and the vertical arrangement of different organic horizons and other horizons. Wetland Organic soils are present in all regions of Canada and are commonly referred to as (unfrozen) peatland soils. Unfrozen peatlands with Organic soils cover approximately 75 5568 km2 (8.4%) of the land area of Canada. The two primary processes of formation of wetland Organic soils are paludification and terrestrialization. The major taxonomic issues identified for the wetland Organic soils concerns the lack of taxonomic protocols for limnic materials within the soil control section. This is an issue for those soil profiles in which the middle tier is dominated by, if not entirely composed of, deposited limnic materials. Further work is required to determine if these issues should be expressed at the great group or subgroup level of classification. Our understanding of the effects of management practices such as cultivation, tree removal, drainage, and peat extraction on soil properties needs to be translated into models of soil development.

Estimating plant area index for monitoring crop growth dynamics using Landsat-8 and RapidEye images

Abstract This study investigates the use of two different optical sensors, the multispectral imager (MSI) onboard the RapidEye satellites and the operational land imager (OLI) onboard the Landsat-8 for mapping within-field variability of crop growth conditions and tracking the seasonal growth dynamics. The study was carried out in southern Ontario, Canada, during the 2013 growing season for three annual crops, corn, soybeans, and winter wheat. Plant area index (PAI) was measured at different growth stages using digital hemispherical photography at two corn fields, two winter wheat fields, and two soybean fields. Comparison between several conventional vegetation indices derived from concurrently acquired image data by the two sensors showed a good agreement. The two-band enhanced vegetation index (EVI2) and the normalized difference vegetation index (NDVI) were derived from the surface reflectance of the two sensors. The study showed that EVI2 was more resistant to saturation at high biomass range than NDVI. A linear relationship could be used for crop green effective PAI estimation from EVI2, with a coefficient of determination ( R 2 ) of 0.85 and root-mean-square error of 0.53. The estimated multitemporal product of green PAI was found to be able to capture the seasonal dynamics of the three crops.

Integration of RADARSAT-2 ScanSAR and AWiFS for operational agricultural land use monitoring over the Canadian prairies

Agriculture plays an important role in the global economy, and sustainability of this sector is critical for world food security. Annual information on agricultural land use (crop inventory) would permit efficient and effective delivery of agricultural programs that support sustainability of this resource. Previous research has revealed encouraging results on using space borne satellite data (Landsat, SPOT) for crop mapping at the regional scale. Given Canadas large land mass, for operational crop monitoring satellite data with a wide swath and moderate spatial resolution are needed. This study presents the results on integrating RADARSAT-2 ScanSAR data with AWiFS data to improve crop identification. This study demonstrates that multi-temporal AWiFS data can produce an adequate crop classification, with an overall accuracy of 83%. The addition of ScanSAR data increases the overall classification accuracies. The radar contribution is most pronounced during the earlier season.