Journal Paper Digests 2018 #18
- Downscaling of SMAP Soil Moisture Using Land Surface Temperature and Vegetation Data
- A SPECLib-based operational classification approach: A preliminary test on China land cover mapping at 30 m
- Spatial structuring of soil microbial communities in commercial apple orchards
Downscaling of SMAP Soil Moisture Using Land Surface Temperature and Vegetation Data
Authors: Fang, B; Lakshmi, V; Bindlish, R; Jackson, TJ
Source: VADOSE ZONE JOURNAL, 17 (1):70198-70198; AUG 9 2018
Abstract: Remotely sensed soil moisture retrieved by the Soil Moisture Active and Passive (SMAP) sensor is currently provided at a 9-km grid resolution. Although valuable, some applications in weather, agriculture, ecology, and watershed hydrology require soil moisture at a higher spatial resolution. In this study, a passive microwave soil moisture downscaling algorithm based on thermal inertia theory was improved for use with SMAP and applied to a data set collected at a field experiment. This algorithm utilizes a normalized difference vegetation index (NDVI) modulated relationship between daytime soil moisture and daily temperature change modeled using output variables from the land surface model of the North American Land Data Assimilation System (NLDAS) and remote sensing data from the Moderate-Resolution Imaging Spectroradiometer (MODIS) and Advanced Very High Resolution Radiometer (AVHRR). The reference component of the algorithm was developed at the NLDAS grid size (12.5 km) to downscale the SMAP Level 3 radiometer-based 9-km soil moisture to 1 km. The downscaled results were validated using data acquired in Soil Moisture Active Passive Validation Experiment 2015 (SMAPVEX15) that included in situ soil moisture and Passive Active L-band System (PALS) airborne instrument observations. The resulting downscaled SMAP estimates better characterize soil moisture spatial and temporal variability and have better overall validation metrics than the original SMAP soil moisture estimates. Additionally, the overall accuracy of the downscaled SMAP soil moisture is comparable to the PALS high spatial resolution soil moisture retrievals. The method demonstrated in this study downscales satellite soil moisture to produce a 1-km product that is not site specific and could be applied to other regions of the world using the publicly available NLDAS/Global Land Data Assimilation System data.
A SPECLib-based operational classification approach: A preliminary test on China land cover mapping at 30 m
Authors: Zhang, X; Liu, LY; Wang, YJ; Hu, Y; Zhang, B
Source: INTERNATIONAL JOURNAL OF APPLIED EARTH OBSERVATION AND GEOINFORMATION, 71 83-94; SEP 2018
Abstract: Fine-resolution land cover mapping at the regional or global scale is usually time-consuming and involves a lot of manual participation. This article proposes a novel and automatic land cover mapping approach called the SPatial-tEmporal speCtral Library (SPECLib), which can produce a land cover map with a 30-m spatial resolution. First, the SPECLib is developed by extracting reference spectra for uniform, typical samples of each land cover type by combining the GlobCover2009 land cover product and the 8-day composite MODIS Surface Reflectance Product (MODO9 A1). SPECLib has a temporal resolution of 8 days and contains most of the land cover types in each 1.5 degrees x 1.5 degrees geographic cell. For an arbitrary temporal stage, for each geographical cell of the SPECLib, there are a total of about 5000 MODIS spectra. Secondly, an operational approach for automatic land cover mapping using Landsat data is developed based on SPECLib. For each Landsat scene to be classified, the reference spectra of the land cover types in the corresponding geographic cell were automatically extracted and normalized. The Landsat scene was then classified by using the normalized reference spectra from SPECLib in an integrated classification approach that combined MLC (Maximum Likelihood Classification) and SAM (Spectral Angle Mapper). Finally, the SPECLib-based approach is tested using 510 single-date Landsat 8 OLI scenes to produce a land cover map of China. The map was validated using 11,366 interpretation samples and was shown to have an overall accuracy of 77.34% and a kappa coefficient of 0.729. Therefore, the preliminary results indicate that the SPECLib-based operational classification approach is a promisingly automatic method for global land cover mapping at a resolution of 30 m.
Spatial structuring of soil microbial communities in commercial apple orchards
Authors: Deakin, G; Tilston, EL; Bennett, J; Passey, T; Harrison, N; Fernandez-Fernandez, F; Xu, XM
Source: APPLIED SOIL ECOLOGY, 130 1-12; SEP 2018
Abstract: Characterising spatial microbial community structure is important to understand and explain the consequences of continuous plantation of one crop species on the performance of subsequent crops, especially where this leads to reduced growth vigour and crop yield. We investigated the spatial structure, specifically distance-decay of similarity, of soil bacterial and fungal communities in two long-established orchards with contrasting agronomic characteristics. A spatially explicit sampling strategy was used to collect soil from under recently grubbed rows of apple trees and under the grassed aisles. Amplicon-based metabarcoding technology was used to characterise the soil microbial communities. The results suggested that (1) most of the differences in soil microbial community structure were due to large-scale differences (i.e. between orchards), (2) within-orchard, small-scale (1-5 m) spatial variability was also present, but spatial relationships in microbial community structure differed between orchards and were not predictable, and (3) vegetation type (i.e. trees or grass and their associated management) can significantly alter the structure of soil microbial communities, affecting a large proportion of microbial groups. The discontinuous nature of soil microbial community structure in the tree stations and neighbouring grass aisles within an orchard illustrate the importance of vegetation type and allied weed and nutrient management on soil microbial community structure.