Soil is the largest carbon reservoir on the terrestrial surface. Soil organic carbon (SOC) not only regulates global climate change, but also indicates soil fertility level in croplands. SOC prediction based on remote sensing images has generated great interest in the research field of digital soil mapping. The short revisiting time and wide spectral bands available from Sentinel-2A (S2A) remote sensing data can provide a useful data resource for soil property prediction. However, dense soil surface coverage reduces the direct relationship between soil properties and S2A spectral reflectance such that it is difficult to achieve a successful SOC prediction model. Observations of bare cropland in autumn provide the possibility to establish accurate SOC retrieval models using the S2A super-spectral reflectance. Therefore, in this study, we collected 225 topsoil samples from bare cropland in autumn and measured the SOC content. We also obtained S2A spectral images of the western Guanzhong Plain, China. We established four SOC prediction models, including random forest (RF), support vector machine (SVM), partial least-squares regression (PLSR), and artificial neural network (ANN) based on 15 variables retrieved from the S2A images, and compared the prediction accuracy using RMSE (root mean square error), R² (coefficient of determination), and RPD (ratio of performance to deviation). Based on the optimal model, the spatial distribution of SOC was mapped and analyzed. The results indicated that the inversion model with the RF algorithm achieved the highest accuracy, with an R² of 0.8581, RPD of 2.1313, and RMSE of 1.07. The variables retrieved from the shortwave infrared (SWIR) bands (B11 and B12) usually had higher variable importance, except for the ANN model. SOC content mapped with the RF model gradually decreased with increasing distance from the Wei river, and values were higher in the west than in the east. These results matched the SOC distribution based on measurements at the sample sites. This research provides evidence that soil properties such as SOC can be retrieved and spatially mapped based on S2A images that are obtained from bare cropland in autumn.

The eastern foot of Helan mountain is a famous production area of high-end wine grapes in China. Excessive application of NPK fertilizer induced deficiency in trace elements, such as calcium, and seriously affected the properties of wine grapes. A vineyard in the eastern foot of Helan mountain was selected to investigate the influence of five different concentration treatments of 15 (T₁), 30 (T₂), 45 (T₃), 60 (T₄), and 75 (T₅) kg·ha^-1 of water-soluble calcium fertilizer (Ca(NO₃)₂·4H₂O) application on grape calcium content, yield and fruit properties. The application of calcium fertilizer significantly increased the calcium content in leaves but reduced that in stems and fruits. The highest grape production of 6560.83 kg·ha^-1 was achieved at T₅ calcium fertilizer application, corresponding to increase of 30.92% than that for the control (CK, normal fertilization) treatment. The minimum titratable acid of 4.63 g·L^-1 in grapes was detected at T₂ calcium fertilizer application, which was 16.38% lower than CK, however, 13.40% increase in sugar-to-acid ratio was observed at T₂. At 45 kg·ha^-1 calcium fertilizer concentration, the anthocyanins content was 6.47 mg·L^-1, indicating an increase of 53.23% than CK. This study showed that the optimal calcium fertilizer concentration was 30 kg·ha^-1 with the lowest °Brix, titratable acidity, anthocyanins, the highest total phenols, reducing sugar, sugar-to-acid ratio, and an acceptable concentration of the soluble sugar and tannins.

Since the beginning of the 21st century in Shaanxi Province, China, ecological restoration has increased fractional vegetation cover (FVC) and decreased soil and water erosion. The climate and topography will be critical factors for maintaining vegetation coverage in the future. Based on the moderate resolution imaging spectroradiometer (MODIS) normalized difference vegetation index (NDVI) data, we monitored FVC variations in Shaanxi Province, China, as well as in three subregions of the Loess Plateau (LOP), Qinling–Bashan Mountain (QBM), and Guanzhong Plain (GZP). Using Sen+Mann–Kendall, correlation analysis, and geodetector methods, we detected trends and responses to climate change and topographical characteristics in Shaanxi Province from 2000 to 2018. The results indicated that 73.86% of the area in Shaanxi Province exhibited an increasing FVC with a growth rate of 0.0026 year⁻¹ from 2000 to 2018. The FVC in the three subregions varied, as QBM (87.24–91.47%) > GZP (47.45–66.93%) > LOP (36.33–49.74%), which displayed a significant increase, slight increase, and slight decrease, respectively. The variation of FVC was significantly positively correlated with climate factors (precipitation, temperature, sunshine duration) at monthly and seasonal scales. The time-lag duration between FVC and climate factors was 1–3 months except for the conjunctional areas of GZP with the LOP and QBM, which exhibited a time-lag of 5–6 months. Topographically, the landform of hills had the highest FVC increase at an altitude of 500–1500 m and a slope of 2°–6°. The dominant driving factors affecting FVC variation in Shaanxi Province and LOP area were climatic factors. In the QBM area, the dominant factors were related to topography (relief, elevation, slope), whereas in the GZP area, they were relief and sunshine duration. We can conclude that local topography characteristics are important in implementing revegetation projects because they strongly influence water, temperature, and sunshine redistribution.

Soil visible-near-infrared reflection (Vis-NIR; 350 to 2500 nm) spectra is a comprehensive reflection of various soil physical and chemical properties, resulting in significant differences of hyperspectral characteristics and diversity of soil property prediction models. Calcium carbonate regulates several soil properties that were widely used to describe soil types and to quantify vulnerability to erosion. Therefore, spectral-based determination of soil calcium carbonate content is essential for agricultural management and environmental evaluation. Based on their reflectance similarity, the soil Vis-NIR spectra data itself can be classified into different spectral groups to predict soil calcium carbonate. To obtain the best hyperspectral quantitative prediction model for soil calcium carbonate based on soil group spectral data and spectral group data, a total of 246 soil samples were collected from seven soil types in Shaanxi province, China, and the soil Vis-NIR spectra were measured. All the soil spectral data were smoothed using Savitzky-Golay and continuum removal method. In this paper, we combined spectral angle cosine (SAC) algorithm with spectral correlation coefficient (SCC) algorithm to classify soil spectra. The results indicated that the coefficient of correlations between measured and predicted soil properties for seven prediction models based on random regression forest (RFR) were low (Ratio of performance to deviation, RPD < 2.00). When soil spectra were classified into three types based on the SAC-SCC spectral similarity measurement method, accuracy of all the prediction models increased significantly (R ² ≥ 0.96; RPD > 2.00) compared with the individual soil type models. These results provide a novel method for soil spectral classification and soil attribute estimation.

Four vineyards from the eastern foot of Helan Mountain within the same climate classification, where the planted grapes were 4-year-old Cabernet Sauvignon, were selected for investigating the difference in grape and wine properties. Results showed that the grapes of Lilan vineyard had a higher sugar content and sugar–acid ratio than Huida, Yuquan and Zhihuiyuanshi vineyards. The grapes of Zhihuiyuanshi vineyard had the highest tannin and total phenols content. Concerning the wines, the wine of Yuquan vineyard had low pH but had a great ageing potential, and wine of Zhihuiyuanshi vineyard had highest tone, chroma, tannins, total phenols and alcohol content than other vineyards. Soil pH was positively correlated with anthocyanins in the grapes and negatively correlated with total acid in wine. The microbial biomass carbon (MBC) was correlated with the anthocyanins in grape. Microbial biomass nitrogen (MBN) was positively correlated with soluble solid in grapes, and positively correlated with tannin and total phenols in the wine. The MBC/MBN ratio was negatively correlated with tone in the wine. Our findings indicate that adjusting soil pH and choosing microbial fertiliser with high soil microbial carbon and nitrogen nutrients are effective ways to improve the quality of regional grapes and wine.