| 典型黑土区耕作土壤质地遥感时间窗口及影响因素分析 |
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| 引用本文： | 刘琼,罗冲,孟祥添,张新乐,唐海涛,马士耐,刘焕军.典型黑土区耕作土壤质地遥感时间窗口及影响因素分析[J].农业工程学报,2022,38(18):122-129. |
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| 作者姓名： | 刘琼 罗冲 孟祥添 张新乐 唐海涛 马士耐 刘焕军 |
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| 作者单位： | 1. 东北农业大学公共管理与法学院，哈尔滨 150030；;2. 中国科学院东北地理与农业生态研究所，长春 130012 |
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| 摘 要： | 了解黑土区耕作土壤质地的空间分布对于黑土区农业精准管理以及耕地保护至关重要。遥感技术是快速获取土壤质地空间分布的有效方法。该研究以黑龙江省友谊农场耕地为研究对象，评估研究区土壤质地遥感反演的最佳时间窗口并分析其影响因素。筛选覆盖研究区的2019-2021年25幅Sentinel-2影像，将每幅影像的波段和构建的光谱指数输入随机森林模型，建立土壤质地遥感反演模型，比较不同时期影像反演土壤质地的模型精度，确定土壤质地遥感反演的最适宜影像，并分析造成反演土壤质地精度变化的原因，获取友谊农场土壤质地空间分布。结果表明：1）友谊农场反演土壤质地的最佳时间窗口为4月下旬至5月中旬；2）在25幅Sentinel-2影像中，2020年5月7日反演粉粒和砂粒的模型精度最高（粉粒的R2为0.785，均方根误差为6.697%；砂粒的R2为0.776，均方根误差为8.296%）；2019年5月3日反演黏粒的模型精度最高（R2为0.776，均方根误差为1.6%）；3）不同时期的Sentinel-2影像对土壤质地反演的准确性有很大的影响，而土壤含水量和秸秆覆盖是造成不同时期土壤质地预测精度差异的重要原因。研究为确定土壤质地遥感反演的最佳时间窗口、实现区域尺度土壤质地制图提供关键技术。
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| 关 键 词： | 土壤 随机森林 遥感 黑土 时间窗口 Sentinel-2 影响因素 质地 |
| 收稿时间： | 2022/8/10 0:00:00 |
| 修稿时间： | 2022/8/10 0:00:00 |
Time window and influencing factors analysis of tillage soil texture remote sensing in the typical black soil region |
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| Liu Qiong,Luo Chong,Meng Xiangtian,Zhang Xinle,Tang Haitao,Ma Shinai,Liu Huanjun.Time window and influencing factors analysis of tillage soil texture remote sensing in the typical black soil region[J].Transactions of the Chinese Society of Agricultural Engineering,2022,38(18):122-129. |
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| Authors: | Liu Qiong Luo Chong Meng Xiangtian Zhang Xinle Tang Haitao Ma Shinai Liu Huanjun |
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| Institution: | 1. School of Public Administration and Law, Northeast Agricultural University, Harbin 150030, China;;2. Northeast Institute of Geography and Agroecology, Chinese Academy of Sciences, Changchun 130012, China |
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| Abstract: | Abstract: Spatial distribution of cultivated soil texture is generally crucial to the precision management of agriculture and farmland protection in black soil areas. Remote sensing technology can be an effective way to rapidly acquire the spatial distribution of soil texture. Taking the You Yi Farm in Heilongjiang Province of China as the study area, this study aims to evaluate the best time window for the remote sensing inversion of soil texture in the cultivated land. 25 Sentinel-2 images were selected in the study area from 2019 to 2021. The band and spectral index of each image were input into the random forest model, in order to establish a remote sensing inversion model of soil texture. A comparison was made on the model accuracy of soil texture inversion from the images in different periods. The most suitable image was determined for the remote sensing inversion of soil texture. The spatial distribution of soil texture was obtained to evaluate the accuracy of soil texture inversion from the Sentinel-2 multi-spectral images on different dates. The results showed that: 1) From the visible light to short wave infrared 1 (1 565-1 655nm) spectral reflectance increased with the increase of wavelength, from the short wave infrared 1 to short wave infrared 2 (2 100-2 280nm) spectral reflectance decreased significantly. The spectral reflectance decreased with the increase of silt and clay content, whereas there was an increase with the increase of sand content. 2) There was the maximum accuracy of inversion model in the silt and sand on May 7, 2020 (the coefficient of determination (R2) of silt was 0.785 in 25 Sentinel-2 images, and Root Mean Square Error (RMSE) was 6.697%; the R2 of sand was 0.776, and RMSE was 8.296 %). The maximum accuracy was also achieved in the clay inversion model on May 3, 2019 (R2=0.776, RMSE=1.6%). 3) The appropriate time of satellite images was selected as an important impact on soil texture inversion. The best time window was from late April to mid-May in the study area. The time window and good-quality spectral data were obtained to develop a stable spectral model for the spatial distribution of soil texture. 4) Different inversion accuracy of soil texture were obtained using the 25 Sentinel-2 images from 2019 to 2021. This data was attributed to the soil water content and straw mulching. 5) The silt and clay particles were distributed more in the northeast, north, and south, and less in the middle and southwest of the study area. There was the opposite trend of the sand, especially the generally high sand content in the middle of the study area. Therefore, the high-precision remote sensing inversion model was achieved in the soil texture. The finding can provide the key technologies for regional soil texture mapping and farmland protection in the black soil areas. |
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| Keywords: | soils random forest remote sensing black soil time window Sentinel-2 influencing factor texture |
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