Spatial-temporal pattern and cause analysis for accurate management of remote sensing zoning at field scale in black soil areas

Division of accurate management area can make a great contribution to the ecological conservation in precision agriculture, particularly on preventing excessive fertilization and pesticides. There is a regularly uniform distribution of management zoning in different years in the same field. It is necessary to quantitatively analyze the spatial-temporal changes of zoning for better field accurate management according to local conditions. Most previous studies focused on the accurate partition, with emphasis on the selection of input quantity for high accuracy of zoning. The innovation of this study lies in the quantitative expression of a multiyear pattern after the division, together with the influencing factors of zoning. A corn field was selected as the research area at the Youyi Farm in Heilongjiang Province of northeastern China. The remote sensing images were captured from the Sentinel-2A satellite under the European Space Agency (ESA) during the corn seedling stage in the first ten days of June 2017-2020. The Normalized Difference Vegetation Index (NDVI) was extracted from the preprocessed images on the ArcGIS software. The object-oriented segmentation was used to segment NDVI images, where the coefficient of variation was selected to evaluate each segmentation. The coefficients of variation in the NDVI were reduced by more than 70% after segmentation. In the partition, a natural breakpoint was used to classify the NDVI. A superposition analysis was utilized to calculate the spatial transfer matrix. The results showed that the patterns of maize in the seedling stage were similar in the study area. The coefficients of variation for the elevation and slope after segmentation were reduced by 42.857%-57.143% and 30.723%-34.940%, respectively, indicating that the growth of crops was affected by terrain factors. There was also a significant correlation between the NDVI and terrain factors, such as the elevation and slope. The sensors of soil temperature and humidity were embedded in four different positions on the slope along the ridge line with obvious topographic relief. A line chart or histogram was obtained for the four NDVI, the average values of soil moisture and temperature on the same ridge line according to the elevation changes in June. The highest NDVI values in the four stages were observed on the sunny slope, while gradually decreased from the top to the bottom of a slope. The reason was that the soil temperature of the sunny slope was the highest, while the soil moisture was sufficient, suitable for the emergence conditions of crops, where the emergence was faster with the high NDVI. On the shady slope, the soil temperature was relatively low, unsuitable for the emergence conditions with a low NDVI. In a specific field, the precise management pattern was similar to the same crop in the growth period over many years. The growth of crops at the early stage and the zonings of precise management depended mainly on the distribution of soil moisture and temperature under the different topographies. The findings can provide a spatiotemporal framework to integrate accurate management with variable fertilization and pesticides in precision agriculture.