Cartography, GIS and Remote Sensing Department, Institute of Geography, University of Göttingen, Goldschmidt Street 5, 37077 Göttingen, Germany.
Sensors (Basel). 2022 Jul 29;22(15):5683. doi: 10.3390/s22155683.
Appropriate crop type mapping to monitor and control land management is very important in developing countries. It can be very useful where digital cadaster maps are not available or usage of Remote Sensing (RS) data is not utilized in the process of monitoring and inventory. The main goal of the present research is to compare and assess the importance of optical RS data in crop type classification using medium and high spatial resolution RS imagery in 2018. With this goal, Landsat 8 (L8) and Sentinel-2 (S2) data were acquired over the Tashkent Province between the crop growth period of May and October. In addition, this period is the only possible time for having cloud-free satellite images. The following four indices "Normalized Difference Vegetation Index" (NDVI), "Enhanced Vegetation Index" (EVI), and "Normalized Difference Water Index" (NDWI1 and NDWI2) were calculated using blue, red, near-infrared, shortwave infrared 1, and shortwave infrared 2 bands. Support-Vector-Machine (SVM) and Random Forest (RF) classification methods were used to generate the main crop type maps. As a result, the Overall Accuracy (OA) of all indices was above 84% and the highest OA of 92% was achieved together with EVI-NDVI and the RF method of L8 sensor data. The highest Kappa Accuracy (KA) was found with the RF method of L8 data when EVI (KA of 88%) and EVI-NDVI (KA of 87%) indices were used. A comparison of the classified crop type area with Official State Statistics (OSS) data about sown crops area demonstrated that the smallest absolute weighted average (WA) value difference (0.2 thousand ha) was obtained using EVI-NDVI with RF method and NDVI with SVM method of L8 sensor data. For S2-sensor data, the smallest absolute value difference result (0.1 thousand ha) was obtained using EVI with RF method and 0.4 thousand ha using NDVI with SVM method. Therefore, it can be concluded that the results demonstrate new opportunities in the joint use of Landsat and Sentinel data in the future to capture high temporal resolution during the vegetation growth period for crop type mapping. We believe that the joint use of S2 and L8 data enables the separation of crop types and increases the classification accuracy.
在发展中国家，进行适当的作物类型制图以监测和控制土地管理非常重要。在没有数字地籍图的情况下，或者在监测和清查过程中没有使用遥感 (RS) 数据的情况下，这将非常有用。本研究的主要目标是比较和评估 2018 年使用中高空间分辨率 RS 图像的光学 RS 数据在作物类型分类中的重要性。为此，在作物生长期间的 5 月至 10 月，获取了 Landsat 8 (L8) 和 Sentinel-2 (S2) 数据。此外，这个时期是拥有无云卫星图像的唯一可能时间。计算了以下四个指数：归一化植被指数 (NDVI)、增强型植被指数 (EVI)、归一化差异水指数 (NDWI1 和 NDWI2)，使用蓝色、红色、近红外、短波红外 1 和短波红外 2 波段。使用支持向量机 (SVM) 和随机森林 (RF) 分类方法生成主要作物类型图。结果，所有指数的总体精度 (OA) 均高于 84%，使用 L8 传感器数据的 EVI-NDVI 和 RF 方法达到了最高的 OA 92%。使用 L8 数据的 RF 方法时，EVI 的 Kappa 精度 (KA) 最高（88%），EVI-NDVI 的 KA 为 87%。将分类作物类型面积与官方国家统计数据（OSS）中关于播种作物面积的数据进行比较，结果表明，使用 EVI-NDVI 和 RF 方法以及 L8 传感器数据的 NDVI 和 SVM 方法，加权平均（WA）值差异最小（0.2 千公顷）。对于 S2 传感器数据，使用 RF 方法的 EVI 和 SVM 方法的 NDVI 获得的绝对差值结果最小（0.1 千公顷）。因此，可以得出结论，结果表明未来联合使用 Landsat 和 Sentinel 数据具有新的机遇，可以在植被生长期间捕获高时间分辨率，用于作物类型制图。我们相信，联合使用 S2 和 L8 数据可以分离作物类型并提高分类精度。
