基于GIS、RS的滴灌棉田土壤养分精确管理分区研究

Defining Agricultural Management Zones Using Remote Sensing and GIS Techniques for Drip-irrigated Cotton Fields

在GIS和RS支持下,针对新疆生产建设兵团第五师81团滴灌棉田,选用遥感结合土壤、土壤、遥感数据为数据源,利用模糊c均值聚类法进行土壤养分精确管理分区研究。研究结果表明:无论以何种数据源划分分区,分区后各分区养分指标变异系数均有所下降,空间分布朝均一方向发展;不同管理分区间差异明显,同一管理分区内土壤养分含量的空间变异差异较小。以遥感结合土壤为数据源所划分管理分区与实际产量所划分分区符合度最高达到91.36%,以土壤为数据源的管理分区次之,符合度达到84.40%,仅以遥感数据(归一化植被指数)为数据源所划分管理分区符合度最低为75.47%。因此,运用聚类分析法以遥感结合土壤数据为数据源可获得较好的分区结果,可实施变量投入和精确施肥推荐,为棉田土壤养分管理提供科学的理论依据。

Fuzzy c-means clustering was used to define soil-nutrient management zones. Remote sensing （RS） data, soil sampling data, and a combination of both were tested to identify which data source was the best for partitioning optimum zones, using a geographical information system and various statistical techniques. The study area was a region of large-scale drip-irrigated cotton cultivation in China. For all three data sources, the area was portioned into three zones. With the aim to confirm the resulting zones, the coefficient of variation of the nutrient index was calculated for the RS data, soil data, and combination of both types of data. There was no significant difference among the results calculated using the three data types. The least spatial variation in soil nutrient content was found within the same management zones, with larger variation between zones. The highest degree of conformity （91.36%） with zones derived using actual cotton production data was found for the management zones defined using the combination of RS and soil data. Using soil nutrient data alone, the degree of conformity was lower, at 84.40%. The lowest conformity （75.46%） was found for the zones based on the RS data alone （using the normalized difference vegetation index）. The method proposed here, using fuzzy c-means clustering and a combination of RS and soil sampling data, can be useful in determining zones for optimal fertilizer application and resource management in cotton systems.