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Plant’s health status is one of the most important indicators to evaluate restoration projects and environmental evolution of degraded land, particularly in mine restoration areas. Most studies on vegetation cover changes did not consider the phenological characteristics and health status in remote sensing monitoring, although optical remote sensing technology has been available since the 1980s. Taking a basalt stone pit in Qitaihe City, Heilongjiang Province, China as the research area, this study aims to detect the health status of corn crops using remote sensing. Thirteen images were captured from the Sentinel-2 satellite from April to October 2020, and then reconstructed by the time-series of Normalized Difference Vegetation Index (NDVI). Different fittings were selected, including the Savitzky-Golay (S-G) filtering, double logistic (D-L) fitting, and asymmetric Gaussian (A-G) filtering. Dynamic thresholds and curvatures were used, where the phenological indicators of corn crops covered the periods of emergence, jointing, tasseling, and maturity, as well as the interval length between phenological periods. An analysis was made on the difference of corn phenological characteristics between the mine restored and normal cultivated areas. Some remote sensing spectral indices were extracted, including the Red-Edge Inflection Point (REIP), NDVI, green NDVI (GNDVI), Meris Terrestrial Chlorophyll Index (MTCI), pigment specific simple ratio (PSSRA), Inverted Red-Edge Chlorophyll Index (IRECI), Modified Chlorophyll Absorption Ratio Index (MCARI) and vegetation coverage (FVC). A systematic evaluation was made on the feasibility and sensitivity of indices to distinguish between healthy crops and sub-health crops in the restoration area. The results showed that the key phenological periods of corn was delayed in the restoration treatment area, where the emergence period was delayed by 5-12 days, while the jointing period was delayed by 9-12 days, and the maturity period was delayed by 21-22 days, compared with those in the normal cultivated areas. The phenological difference of crops gradually emerged as the crop grew. Remote sensing indicators, including the REIP, NDVI, GNDVI, PSSRA, IRECI, and FVC, can effectively distinguish the crops spectral features and the health status difference between the mine restoration and normal cultivation areas. The GNDVI received the lowest standard deviation for the restoration area (0.093 5 for area A, and 0.056 2 for area B), indicating the most stable indicator of the overall health status of crops. In PSSRA amplitude interval of pixels, the lower limit in the C area was 12.61% and 44.72% higher than the upper limit in the A and B areas, respectively, indicating the most sensitive among the indicators. The spectral features were related to the comprehensive influence of physiological difference between restoration and normal cultivation areas, including the leaf green content, leaf area index, and leaf water content of corn crops. This finding can provide an insightful understanding to rapidly evaluate the mine restoration using remote sensing technologies. [ABSTRACT FROM AUTHOR]

• Phenological indices were derived from NDVI time series using a dynamic threshold method. • The base level was the most important variable for predicting SOC in this study area • Phenological indices with crop rotation type improved prediction of SOC in croplands. • The study demonstrates the effectiveness of including phenological indices in SOC prediction. Mapping the spatial distribution of soil organic carbon (SOC) content or stock is important for climate change studies and land management decisions. When using environmental covariates to map SOC content or stock, variables indicating human activities have drawn growing attentions. Crop species/crop rotations and agricultural management significantly affect the spatial variation of SOC in croplands. For areas where climatic conditions and farming managements are generally consistent in cultivation territory of one crop species, crop phenology largely indicates the crop response to soil. Therefore, phenological parameters incorporating with crop rotation could be effective for mapping soil organic carbon in these areas. In this study, we extracted phenological parameters from Normalized Difference Vegetation Index (NDVI) time series data, and used these variables with crop rotation for predicting topsoil organic carbon content in a cropland area in Anhui province, China. Forty-nine sampling points were collected in field. For these points, there were three crop rotations each with two crop species per year. Twenty-two HJ-1 A/B images for 2010 year with a 30 m resolution were obtained. Eleven phenological parameters for each of the two growing seasons were obtained with a dynamic threshold method. Various combinations of predictive variables were developed based on variable importance and experimented for predicting topsoil organic carbon using random forest. The prediction results were validated using a cross validation approach. Results showed that base levels (given as the average of the left and right minimum values of a time series profile) for both seasons were the most important predictors in this area. Adding both crop rotation and the two phenological parameters to the natural environment variables increased the prediction accuracies by 50% in terms of R2 and 13.4% in terms of root mean square error (RMSE). This study demonstrates the effectiveness of crop phenology in mapping SOC in croplands. [ABSTRACT FROM AUTHOR]