Abstract
Accurate crop distribution maps provide important information for crop censuses, yield monitoring and agricultural insurance assessments. Most existing studies apply low spatial resolution satellite images for crop distribution mapping, even in areas with a fragmented landscape. Unmanned aerial vehicle (UAV) imagery provides an alternative imagery source for crop mapping, yet its spectral resolution is usually lower than satellite images. In order to produce more accurate maps without losing any spatial heterogeneity (e.g., the physical boundary of land parcel), this study fuses Sentinel-2A and UAV images to map crop distribution at a finer spatial scale (i.e., land parcel scale) in an experimental site with various cropping patterns in Heilongjiang Province, Northeast China. Using a random forest algorithm, the original, as well as the fused images, are classified into 10 categories: rice, corn, soybean, buckwheat, other vegetations, greenhouses, bare land, water, roads and houses. In addition, we test the effect of UAV image choice by fusing Sentinel-2A with different UAV images at multiples spatial resolutions: 0.03 m, 0.10 m, 0.50 m, 1.00 m and 3.00 m. Overall, the fused images achieved higher classification accuracies, ranging between 10.58% and 16.39%, than the original images. However, the fused image based on the finest UAV image (i.e., 0.03 m) does not result in the highest accuracy. Instead, the 0.10 m spatial resolution UAV image produced the most accurate map. When the spatial resolution is less than 0.10 m, accuracy decreases gradually as spatial resolution decreases. The results of this paper not only indicate the possibility of combining satellite images and UAV images for land parcel level crop mapping for fragmented landscapes, but it also implies a potential scheme to exploit optimal choice of spatial resolution in fusing UAV images and Sentinel-2A, with little to no adverse side-effects.
Highlights
Crop classification and identification is one of the classic research topics in the scientific community of remote sensing
We explored the impact of the choice of Unmanned aerial vehicle (UAV) spatial resolution on crop classification
The results showed that Random Forest produced the most accurate result (Overall Accuracy = 88.32%, Kappa Coefficient = 0.84), followed by Support Vector Machine (Overall Accuracy = 86.75%, Kappa Coefficient = 0.82) and Neural Net classification (Overall Accuracy = 85.34%, Kappa Coefficient = 0.81)
Summary
Crop classification and identification is one of the classic research topics in the scientific community of remote sensing. Great efforts have been taken to develop multiple methods for crop classification using different remotely sensed data These studies are generally focused on crop composition surveys and the classifications are conducted with the low (MODIS, AVHRR) and medium (Landsat, Sentinel, HJ, GF) spatial resolution data. In addition to the needing to calibrate the sensors for operation (e.g., by deploying a standard (white) reference in the field), the exact spectral behaviour of the sensor spectral bands must be known to derive physical quantities eventually [20] In this regard, the fusion of satellite and UAV data could potentially make full use of the advantages of these two platforms
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