International Center for Insect Physiology and Ecology (ICIPE), P.O. Box 30772, 00100 Nairobi, Kenya.
Department of Agronomy, Faculty of Agriculture, University of Khartoum, Khartoum North 13314, Sudan.
Sensors (Basel). 2017 Nov 3;17(11):2537. doi: 10.3390/s17112537.
Cropping systems information on explicit scales is an important but rarely available variable in many crops modeling routines and of utmost importance for understanding pests and disease propagation mechanisms in agro-ecological landscapes. In this study, high spatial and temporal resolution RapidEye bio-temporal data were utilized within a novel 2-step hierarchical random forest (RF) classification approach to map areas of mono- and mixed maize cropping systems. A small-scale maize farming site in Machakos County, Kenya was used as a study site. Within the study site, field data was collected during the satellite acquisition period on general land use/land cover (LULC) and the two cropping systems. Firstly, non-cropland areas were masked out from other land use/land cover using the LULC mapping result. Subsequently an optimized RF model was applied to the cropland layer to map the two cropping systems (2nd classification step). An overall accuracy of 93% was attained for the LULC classification, while the class accuracies (PA: producer's accuracy and UA: user's accuracy) for the two cropping systems were consistently above 85%. We concluded that explicit mapping of different cropping systems is feasible in complex and highly fragmented agro-ecological landscapes if high resolution and multi-temporal satellite data such as 5 m RapidEye data is employed. Further research is needed on the feasibility of using freely available 10-20 m Sentinel-2 data for wide-area assessment of cropping systems as an important variable in numerous crop productivity models.
在许多作物建模例程中，明确尺度的作物种植系统信息是一个重要但很少提供的变量，对于理解农业生态景观中的病虫害传播机制至关重要。在这项研究中，利用高时空分辨率的 RapidEye 生物时间数据，采用新颖的两步分层随机森林 (RF) 分类方法，对单一和混合玉米种植系统的区域进行了映射。肯尼亚 Machakos 县的一个小规模玉米种植场被用作研究地点。在研究地点内，在卫星采集期间，根据一般土地利用/土地覆盖 (LULC) 和两种种植系统收集了实地数据。首先，使用 LULC 映射结果从其他土地利用/土地覆盖中掩蔽出非耕地区域。随后，将优化后的 RF 模型应用于耕地层，以映射两种种植系统（第二步分类）。LULC 分类的总体精度达到 93%，而两种种植系统的类别精度（PA：生产者精度和 UA：用户精度）始终高于 85%。我们得出结论，如果使用高分辨率和多时相卫星数据（如 5 m RapidEye 数据），则在复杂且高度碎片化的农业生态景观中，对不同种植系统进行明确的映射是可行的。需要进一步研究在广泛的种植系统评估中使用免费的 10-20 m Sentinel-2 数据的可行性，因为它是许多作物生产力模型中的重要变量。
