Remote sensing of land surface mostly obtains a mixture of spectral information of soil and vegetation. It is thus of great value if soil and vegetation information can be acquired simultaneously from one model. In this study, we designed a laboratory experiment to simulate land surface compositions, including various soil types with varying soil moisture and vegetation coverage. A model of a one-dimensional convolutional neural network (1DCNN) was established to simultaneously estimate soil properties (organic matter, soil moisture, clay, and sand) and vegetation coverage based on the hyperspectral data measured in the experiment. The results showed that the model achieved excellent predictions for soil properties (R² = 0.88–0.91, RPIQ = 4.01–5.78) and vegetation coverage (R² = 0.95, RPIQ = 7.75). Compared with the partial least-squares regression (PLSR), the prediction accuracy of 1DCNN improved 42.20%, 45.82%, 43.32%, and 36.46% in terms of the root-mean-squared error (RMSE) for predicting soil organic matter, sand, clay, and soil moisture, respectively. The improvement might be caused by the fact that the spectral preprocessing and spectral features useful for predicting soil properties were successfully identified in the 1DCNN model. For the prediction of vegetation coverage, although the prediction accuracy by 1DCNN was excellent, its performance (R² = 0.95, RPIQ = 7.75, RMSE = 3.92%) was lower than the PLSR model (R² = 0.98, RPIQ = 12.57, RMSE = 2.41%). These results indicate that 1DCNN can simultaneously predict soil properties and vegetation coverage. However, the factors such as surface roughness and vegetation type that could affect the prediction accuracy should be investigated in the future.

Air-dried soil archives are important for microbial ecology research, although the process of air-drying preservation inevitably destroys the original microbial information in soils. Only upon fully understanding the limitations of air-dried soil can it play a greater role. The value of air-dried soil depends on the fidelity of microbial community structure information in the air-dried soil relative to that in fresh soil. To evaluate this, high-throughput sequencing was applied to investigate the microbial community of fresh soils and 227 days air-dried archives from typical farmland under a large spatial scale, and PERMANOVA was used to analyze the explanation proportion (EP) of the spatial factor on the microbial community structure in any paired-fresh or air-dried soils. The results show that for any paired soils, the value of EP ranged from 42.4% to 97.9% (p < 0.001). Importantly, taking fresh soil as a reference, the value of EP declined in air-dried soils (effect size r = 0.79, p < 0.001). Furthermore, the standardized difference in EP between fresh and air-dried soil (NDEP) was used to characterize the fidelity of variance source of microbial community structure in air-dried soils, and correlation tests showed that NDEP was negatively correlated with spatial distance (r = −0.21, p < 0.01) and with environmental difference (r = −0.37, p < 0.001). Further analyses show that larger NDEP was observed at a spatial distance <25 km or an environmental difference <0.58. Variance partitioning analysis showed that 28.0% of the variation in NDEP could be explained, with environmental difference constituting 14.0% and the interaction between the environmental difference and spatial distance constituting the remaining 14.0%. Soil texture was the most important factor for predicting NDEP, followed by soil pH and annual average temperature. This study not only emphasizes the possible decline in EP when using air-dried soils to reveal microbial community patterns, but also implies that air-dried soil is more suitable for addressing scientific questions under a large spatial scale or environmental differences.