Study On The Spatial Variability And Driving Factors Of Key Soil Characteristics In The Black Soil Region Of Northeast China

Soil is the foundation of agriculture.The black soil area of Northeast my country is an important food production area in my country.Large farms are important agricultural production management units in the black soil area of Heilongjiang.Due to the freezing and thawing of black soil and the melting of snow and rainfall in spring,soil erosion is caused.which easily leads to the saturation of farmland soil moisture or even accumulation of water,and the difficulty of agricultural machinery entering the ground directly affects the spring planting process.Studying the distribution law and variation characteristics of soil moisture during spring ploughing in the black soil area of Northeast China is helpful to grasp the soil distribution during this period,and analyzing its inlfluencing factors is of guiding significance for the arrangement of agricultural machinery entering the land.However,the traditional field monitoring of soil moisture is time-consuming and laborious.and remote sensing technology has great advantages.The advantages of area measurement and strong timeliness.At present、in order to solve the problems of soil nutrient loss and fertility decline in black soil areas,the spatial variability of soil nutrients cannot accurately and comprehensively reflect the soil nutrient status on a single scale.Studying the driving factors on the farm scale can be used for soil nutrient management,variable fertilization,and Cartography provides a theoretical basis.In this paper,Zhaoguang Farm in Heilongjiang Province is selected as the research area,and the two parts of soil moisture and soil nutrients are analyed separately.Using the 10 m resolution Sentinel-l radar data combined with the somoistrc retrieved from the measured sample points,the semi-variance function and the integrated boosted tree algorithm(Aggregated Boosted Tree,ABT)were used to analyze the spatial variability of-soil moisture and topography during the spring plowing period.The relative influcnce of factors(slope,aspect,position,elevation and topographic moisture index)on the spatial heterogeneity of soil moisture.Field sampling data of soil nutrients in the cultivated layer,using basic statistics from farm scale(soil pH,organic matter,available phosphorus,available potassium)and plot scale(organic matter,alkali hydrolyzable nitrogen,available phosphorus,available potassium)Describe the spatial variability of soil nutrients with geostatistical methods,and use redundancy analysis(RDA)and geographic detectors to analyze topographical factors and human factors(ridge orientation,planting crop types,management areas)on the soil at the farm scale.The influence of nutrients.The main findings are as follows:(1)The soil surface samples were collected from the scattered hills of Zhaoguang Farm,and the soil water content was measured.The soil water content in the study area was between 25.11%and 37.31%,with an average value of 30.55%.The soil moisture in the study area was retrieved by remote sensing.The spatial variability of soil moisture in the black soil overcast area of the farm during the spring plowing period includes two components at the farm scale and the plot scale.The plot scale variation coefficient(5.81%)is greater than the inter-plot variation coefficient(4.16%).Slope position,slope,and aspect are the main controlling factors that affect the spatial variability of soil moisture,and their cumulative relative explanation rate exceeds 70%,of which slope position accounts for 36.28%.(2)On the farm scale,the soil pH is slightly acidic,and it is rich in organic matter,alkali-hydrolyzable nitrogen,available phosphorus and available potassium.The soil pH showed weak variability,and the other indexes showed moderate variability.The optimal semi-variation model of soil pH,alkali-hydrolyzable nitrogen and available phosphorus is an exponential model,and the optimal semi-variation model of organic matter and available potassium is a spherical model.Soil pH and organic matter show strong spatial correlation;alkali hydrolyzable nitrogen,available phosphorus,and available potassium have moderate autocorrelation.(3)On the plot scale,the soil organic matter and available phosphorus are rich in organic matter and available phosphorus,which are high in content.The available phosphorus and available potassium have weak variability,and the organic matter and available phosphorus have moderate variability.The optimal semi-variation model for soil organic matter and available phosphorus is a spherical model,the optimal semi-variation model for alkali-hydrolyzable nitrogen is a Gaussian model,and the optimal semi-variation model for available potassium is an exponential model.All four nutrients show strong spatial correlation.(4)Redundant analysis of the influencing factors of soil nutrients at the farm scale showed that the three topographical factors,ridge orientation,and management area man-made factors have a greater impact on the five soil nutrients,including topographic moisture index(TWI),elevation,and slope position.The use of geographic detectors to analyze the spatial distribution of soil nutrients at the farm scale shows that the factor detector found that the management area has a higher degree of interpretation for each soil nutrient and has a greater impact.The factor interaction detector shows that the management area interacts with the slope position.The effect on the five soil nutrients is higher,and the interpretation of the interaction factor is up to 2 times higher than that of the single factor.