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Mapping cropland at metric resolution using the spatiotemporal information from multi-source GF satellite data

  • role： First author第一作者
  • Affiliation：

    Macro Agriculture Research Institute, College of Resources and Environment, Huazhong Agricultural University, Wuhan 430070, China

  • Email：zhiwen.cai@webmail.hzau.edu.cn
  • Introduction：蔡志文，研究方向为农作物遥感制图。E-mail： zhiwen.cai@webmail.hzau.edu.cn
CAI Zhiwen1,  
  • Affiliation：

    College of Urban and Environmental Sciences, Central China Normal University, Wuhan 430079, China

HE Zhen2,  
  • Affiliation：

    College of Urban and Environmental Sciences, Central China Normal University, Wuhan 430079, China

WANG Wenjing2,  
  • Affiliation：

    Macro Agriculture Research Institute, College of Resources and Environment, Huazhong Agricultural University, Wuhan 430070, China

YANG Jingya1,  
  • Affiliation：

    Macro Agriculture Research Institute, College of Resources and Environment, Huazhong Agricultural University, Wuhan 430070, China

WEI Haodong1,  
  • Affiliation：

    College of Urban and Environmental Sciences, Central China Normal University, Wuhan 430079, China

    State Key Laboratory of Remote Sensing Science, Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100101, China

WANG Cong23,  
  • role： Corresponding author通信作者
  • Affiliation：

    Macro Agriculture Research Institute, College of Resources and Environment, Huazhong Agricultural University, Wuhan 430070, China

    State Key Laboratory of Remote Sensing Science, Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100101, China

  • Email：xubaodong@mail.hzau.edu.cn
  • Introduction：徐保东，研究方向为农作物遥感制图和植被定量遥感。E-mail： xubaodong@mail.hzau.edu.cn
XU Baodong13*

résumé

Timely and accurate estimation of the spatial distribution of cropland is critical for agricultural production management, yield estimation, and planting structure adjustment. Previous studies on cropland mapping mostly focused on using moderate-/low-spatial-resolution images or single-phase high-spatial-resolution images, in which croplands in regions with fragmented landscapes and complex crop planting patterns are challenging to extract. The multi-source Gaofen (GF) satellites launched by China can provide images with high spatiotemporal resolution, thus presenting great potential for fine-scale cropland mapping with high accuracy. This study utilized GF-1, GF-2, and GF-6 satellites to explore a high-accuracy cropland mapping method at metric spatial resolution. Specifically, Cropland Extraction UNet (CEUNet) was developed based on the structure of UNet by integrating multi-temporal information from GF-1/6 and spatial details from GF-2 to fully exploit the spatial and temporal characteristics of cropland.To make full use of the details provided by high-spatial-resolution images, CEUNet adopted the same encoder structure as UNet. It consisted of the repeated application of two 3×3 convolutions (unpadded convolutions), each followed by a 2×2 max pooling operation with stride of 2 for down sampling. The input image size was gradually reduced at each down sampling step, and feature maps of each size were concatenated in the corresponding up-sample layer to extract the hierarchy of spatial information. Meanwhile, time-series feature maps of images with moderate to high spatial resolution were extracted by two consecutive 3×3 convolutional layers and a 1×1 convolutional layer. Then, the time-series and spatial feature maps were integrated via element-wise addition before they were sent to the decoder for pixel-wise classification.Evaluation results from randomly selected sample points showed that CEUNet achieved good performance with an overall accuracy of 92.92% over the whole of Qianjiang City, Hubei Province. The CEUNet-extracted cropland at the meter-level resolution was used to perform a wall-to-wall pixel comparison with the cropland extracted via semantic segmentation based on UNet by using multi-source remote sensing images with different resolutions (UNet_m). Semantic segmentation based on UNet using single-phase high-resolution images (UNet_s), object-based random forest classification (OBIA), and pixel-based random forest classification (RF) were employed to extract the results of cultivated land for comparison. The accuracy of cropland extraction by CEUNet was higher than that by others (the average F1 score was improved by about 0.04, 0.11, 0.21, and 0.21), indicating the effectiveness of the proposed approach for diverse agriculture landscapes. The F1 score of CEUNet was improved by about 0.09, 0.26, 0.27, and 0.27 over regions with high fragmentation and complex landscapes.

mots-clés

cropland extraction;multi-source remote sensing images;CNN;GF satellites

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