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Diamond grinding is a concrete pavement maintenance technique, and concrete grinding residue (CGR) is the byproduct. Concrete grinding residue deposited along roadsides affects soil chemical properties, but impacts of CGR on soil physical properties and plant growth are rarely studied. In this study, a controlled field experiment was performed to determine the influence of CGR on selected soil physical properties (i.e., bulk density [r b ], saturated hydraulic conductivity [K s ], and water infiltrability [I t ]) and on plant biomass and plant coverage under four application rates (0, 2.24, 4.48, and 8.96 kg m −2 ). Field measurements were performed before the CGR applications, and 1, 7, and 12 mo after the CGR applications. No significant CGR effects on soil physical properties were detected. The r b was relatively stable for all of the treatments, whereas some nonsignificant variations (e.g., 10-30% of mean K s values and mean I t values among four CGR rates) were found. Plant biomass with a CGR rate of 2.24 kg m −2 tended to be 10 to 40% larger than biomass in the control treatment, whereas plant biomass with a CGR rate of 8.96 kg m −2 tended to be ?10% smaller than the control treatment. Concrete grinding residue had no significant effects on plant coverage, richness, Simpson's diversity, and evenness. Thus, CGR applications up to 8.96 kg m −2 did not significantly affect soil physical properties and plant growth in this controlled field study. This study can serve as a reference for results obtained from roadsides in Minnesota and Iowa that receive CGR applications.Abbreviations: CGR, concrete grinding residue; EC, electrical conductivity; RCBD, randomized complete block design.

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This paper develops a new model for surface soil moisture (SSM) retrieval from CBERS-02B images. The paper first analyzes the existing SSM retrieval model from Landsat TM imagery and establishes the spectral radiance relationship of each band between Landsat TM and CBERS-02B. The model associated parameters including mean reflectance, mean atmospheric transmittance, and mean sun radial brightness of each band between Landsat TM and CBERS-02B is established. The model is finally adjusted by considering the differences of response frequency and sensitivity in the two satellite sensors. Two test areas, Jili Village of Laibin county, Guangxi Province, China and Yuanjiaduan Village of Jiujiang County, JiangXi Province, China are chosen to verify the correctness of the developed model. The SSMs retrieved from Landsat TM imagery are chosen as references. The accuracy of the proposed model is evaluated through correlation coefficient and root-mean-square error (RMSE) relative to the SSMs retrieved from Landsat TM images. The verified results discover that the relative accuracy of the average SSMs retrieved by the proposed model from CBERS-02B can reach over 91.0% when compared to the SSMs retrieved from Lansat TM. In addition, six types of lands are used to further evaluate the accuracy of the proposed model. The experimental results in two areas show that the correlation coefficient and the RMSE between two SSMs from CBERS-02B and Landsat TM achieves over 0.9 and 0.011 (m 3 /m 3 ), respectively, in both rocky desertification land and dry land; achieve over 0.81 and 0.09 (m 3 /m 3 ), respectively, in rice field, shrub land, and woodland. These results demonstrate that the model developed in this paper can effectively calculate the SSMs for CBERS-02B satellite imagery.Index Terms-Algorithms, CBERS-02B satellite, retrieval, surface soil moisture (SSM).

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