基于SOM和NDVI的黑土区精准管理分区对比

Comparison of precision management zoning methods in black soil area based on SOM and NDVI

根据土壤养分的空间异质性对耕地进行分区是实施变量施肥管理的关键环节,施肥的变量管理将减轻黑土区农业面源污染和土壤退化问题。该文以典型黑土区黑龙江省海伦市某合作社地块为研究对象,利用SPOT-6遥感影像提取归一化植被指数(normalized differential vegetation index, NDVI)、插值计算土壤有机质(soil organic matter,SOM),结合数字高程模型(digital elevation model,DEM),应用面向对象的分割方法,对研究地块进行分区,并应用莫兰(Morans)指数对分区结果进行评价,以期对比研究基于SOM空间插值与遥感信息的分区精度。结果表明:结合4期NDVI空间信息分区的精度最高;结合SOM、DEM、NDVI空间信息进行分区的精度次之;结合地形与SOM空间信息分区精度较低;仅根据SOM空间插值进行分区的精度最低。研究结果可为黑土区农田精准管理分区输入量的选择与多尺度分区提供思路,为实施田间精准追肥提供科学依据。

Cultivated land allocation is the key link to implement variable fertilization management. According to spatial heterogeneity, a field is divided into several sub-field blocks with different homogeneity to adjust soil and crop management measures. The explanate Machinery Cooperative of Heilongjiang Province is taken as a research object in the typical black soil area, and the SPOT-6 remote sensing images from June to September are obtained. With the support of Arcgis, crop growth can be simulated well with, such as the Normalized Difference Vegetation Index (NDVI);the soil organic matter (SOM) content is calculated according to the spatial interpolation method;and the field sample information is measured with iRTK2 and converted into the digital elevation model (DEM) raster data. Based on the spatial SOM distribution information, the SOM spatial information with the topographical factors, the spatial information of SOM with both DEM and NDVI in August, and spatial information with 4 phases of NDVI(in June, July, August, and September) are used as input. Since the inputs of this study are different from the previous single soil nutrient information, the synthesis of multiple spatial information can reflect the spatial difference of the study area in many aspects, which is more consistent with the actual influencing factors. The object-oriented segmentation method is used to divide the study area according to the principle of high homogeneity within the partition and high heterogeneity between partitions. In order to find the index elements that can better reflect the actual growth, the partition accuracy under different inputs is evaluated by two standard indicators, pixel standard deviation and Morans index, which reflect the suitability and accuracy of the partition. When the internal standard deviation of pixels is small, which proves that the soil physical and chemical properties and vegetation growth of each field are more similar to the reality;when the Morans index between the partitions is small, which shows that the differences between the partitions are large, and the spatial similarity is not obvious;which conforms to the principle of division of precise management partitions. The results show that the precision of the precise management partition based on spatial information with the 4 phases of NDVI is the highest, the internal standard deviation of the partition and the Morans index are 0.010 and 0.065, respectively. The partition accuracy for spatial information of SOM with both DEM and NDVI is the secondly, with standard deviation of 0.011 and the Morans index of 0.072 respectively. The accuracy for the SOM spatial information considering the topographical factors is relatively lower, with the internal standard deviation of 0.014 and the Morans index of 0.192. The accuracy of the partition based on only the SOM spatial information has the lowest accuracy, which internal pixel standard deviation and the Morans index are 0.015 and 0.223 respectively. Compared with the traditional spatial interpolation in precision management partition, the remote sensing image has advantages in both data acquisition and precision. In addition, the advantage of multi-source spatial data is that multiple factors can be considered comprehensively, which is more accurate than single data. This method saves a lot of time and more efficient than traditional grid sampling partitioning. The zoning results are expected to promoted field division and management in future research.