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There are four sizable black soil regions throughout the world, all of which are valuable natural resources. The black soil region in Northeast China is a major foundation for grain production. Serious risks of soil erosion do exist, and they have an immediate impact on both the country's food security and future ecological security. Many soil and water conservation measures have been put in place to control soil erosion. However, how effective and suitable are these measures? Currently, systematic analyses and assessments are lacking. The objective of this study was to assess the effectiveness and suitability of soil and water conservation measures on hillslopes using the comprehensive index method and the Pressure–State–Response model. The categorization of effectiveness and suitability of these measures were similar for both methods: that is, very effective and suitable measures included no-tillage + straw mulch and ridge belt or contour ridge. The two methods validated one another. Thus, this categorization standard is useful for choosing the best soil and water conservation measures for different soil erosion regions. [ABSTRACT FROM AUTHOR]

Highlights: • Establishing STN prediction models using Sentinel images and environmental factors. • Exploring the importance of different predictors in explaining STN content. • Comparing the performance of RF models under bare soil and vegetated conditions. Abstract Estimation of soil total nitrogen (STN) is important for quantifying the spatial distribution of soil N (Nitrogen) stocks, determining the amount absorbed by crops and avoiding yield losses and environmental pollution. The objective of this research was to determine the spatial distribution of STN using a remote sensing approach and to investigate the importance of different predictors under two conditions: the bare soil condition and the vegetated condition. We also investigated whether the Sentinel-2A red-edge bands and relative spectral indices were suitable for STN estimation. Soil data were collected from the topsoil (0–20 cm) at 104 sampling sites on 06/15/2016 from farmland in a black soil region in Northeast China. Two Sentinel-2A Multispectral Instrument (MSI) remote sensing images were acquired on 05/26/2016 and 08/03/2016 to represent the two conditions. Environmental variables included the terrain attributes, temperature and precipitation. Then, 21 predictors, including the original bands (O), normal spectral indices (S), red-edge indices (R) and environmental variables (E), were employed to estimate the spatial distribution of the STN content using a random forest (RF) model. Finally, different predictors were combined to construct RF models, and the prediction model with the best performance was selected to determine the spatial pattern of the STN content. The results showed that the predictors had different levels of importance under the two conditions. However, most environmental variables and normal spectral indices always play a significant role in the estimation of STN. The model with the combination of the original bands, normal spectral indices, red-edge indices and environmental variables (O + S + E + R) under the bare soil condition had the best prediction performance, and the combination of the original bands, normal spectral indices and red-edge indices (O + S + R) model had a performance similar to the O + S + E + R model. Therefore, the selection of suitable predictors is necessary to predict STN. The spatial pattern of STN was related to the crop type and elevation of the study area. The results of this study suggested that the proposed RF-based remote sensing method was able to accurately capture the variation in STN and that the performance of the prediction model can be improved by providing enough types of suitable predictors. [ABSTRACT FROM AUTHOR]

Highlights • We estimated soil pH with remote sensing both at the field scale and the regional scale. • Strong correlations were existed between remote sensing spectral indices and soil pH. • The soil pH prediction model constructed by multispectral imagery can be improved. Abstract Soil pH is a dominant soil variable because it influences many physical, chemical, and biological soil properties and plant growth processes. This study aimed at identifying the potential for field hyperspectral reflectance, multispectral satellite imagery and environmental variables to predict soil pH changes for croplands in regional areas located in the black soil region of Dehui in Northeast China. First, hyperspectral reflectance was acquired from a small area of Dehui; then, Landsat images with a 30 m resolution were acquired on 10 Oct 2005 and 22 Oct 2016 for the whole study region. This showed the scope for estimating soil pH with remote sensing derived variables both at the field scale and the regional scale for croplands in black soil regions. We selected 18 spectral variables from hyperspectral reflectance, 17 spectral variables from multispectral imagery and 6 environmental variables, respectively, and found relationships between the controlling variables and the measured soil pH values. Subsequently, 7 spectral indicators from hyperspectral reflectance, 7 spectral indicators from multispectral reflectance and 3 environmental indicators had higher correlations with soil pH among those variables. Finally, we used the partial least squares regression (PLSR) model to build soil pH prediction models for the 7 spectral indicators from hyperspectral reflectance and for the 7 spectral indicators from Landsat image with 3 environmental indicators, then we combined the two models to obtain a soil pH prediction model at the regional scale. The results showed that several thermal reflectance, vegetation indices and environmental variables, including the ratio vegetation index (RVI), the normalized difference vegetation index (NDVI), the normalized difference water index (NDWI), land use, mean annual precipitation and mean annual temperature were strong predictors of soil pH distribution at the regional scale. The combined soil pH prediction model had an R2 of 0.50, a root mean square error (RMSE) of 0.30 and a residual prediction deviation (RPD) of 1.55, indicating reliable precision and average prediction abilities. We estimated that soil pH values showed obvious heterogeneity over the study region, and soil pH mainly changed with crop type. Our results also suggested that the average decrease in soil pH for most of the study area of was approximately 0.50 units during 2005–2016. This study provides new approaches and knowledge for the better investigation of soil properties at a regional scale by using RS technologies. [ABSTRACT FROM AUTHOR]