Improving prediction of soil organic carbon content in croplands using phenological parameters extracted from NDVI time series data
Improving prediction of soil organic carbon content in croplands using phenological parameters extracted from NDVI time series data
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使用从 NDVI 时间序列数据提取的物候参数改进农田土壤有机碳含量的预测
DOI：
10.1016/j.still.2019.104465
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发表时间：
2020-02
影响因子：
6.5
通讯作者：
Li Manchun
中科院分区：
文献类型：
--
作者：
Yang Lin;He Xianglin;Shen Feixue;Zhou Chenghu;Zhu A-Xing;Gao Bingbo;Chen Ziyue;Li Manchun
绘制土壤有机碳 (SOC) 含量或库的空间分布图对于气候变化研究和土地管理决策非常重要。当使用环境协变量绘制 SOC 含量或储量时，指示人类活动的变量引起了越来越多的关注。作物种类/轮作和农业管理显着影响农田有机碳的空间变化。对于单一作物种植区内气候条件和耕作管理基本一致的地区，作物物候在很大程度上反映了作物对土壤的反应。因此，与作物轮作相结合的物候参数可以有效地绘制这些地区的土壤有机碳图。在本研究中，我们从归一化植被指数（NDVI）时间序列数据中提取物候参数，并将这些变量与轮作一起用于预测中国安徽省农田地区的表土有机碳含量。现场采集了49个采样点。对于这些点，每年进行三种作物轮作，每种作物有两种作物品种。获得了22张2010年HJ-1 A/B图像，分辨率为30米。采用动态阈值法获得了两个生长季节各十一个物候参数。根据变量重要性开发了预测变量的各种组合，并进行了使用随机森林预测表土有机碳的实验。使用交叉验证方法验证预测结果。结果表明，两个季节的基准水平（以时间序列剖面左右最小值的平均值给出）是该区域最重要的预测因子。将轮作和两个物候参数添加到自然环境变量中，R2 预测精度提高了 50%，均方根误差 (RMSE) 预测精度提高了 13.4%。本研究证明了作物物候学在绘制农田 SOC 方面的有效性。
Mapping the spatial distribution of soil organic carbon (SOC) content or stock is important for climate change studies and land management decisions. When using environmental covariates to map SOC content or stock, variables indicating human activities have drawn growing attentions. Crop species/crop rotations and agricultural management significantly affect the spatial variation of SOC in croplands. For areas where climatic conditions and farming managements are generally consistent in cultivation territory of one crop species, crop phenology largely indicates the crop response to soil. Therefore, phenological parameters incorporating with crop rotation could be effective for mapping soil organic carbon in these areas. In this study, we extracted phenological parameters from Normalized Difference Vegetation Index (NDVI) time series data, and used these variables with crop rotation for predicting topsoil organic carbon content in a cropland area in Anhui province, China. Forty-nine sampling points were collected in field. For these points, there were three crop rotations each with two crop species per year. Twenty-two HJ-1 A/B images for 2010 year with a 30 m resolution were obtained. Eleven phenological parameters for each of the two growing seasons were obtained with a dynamic threshold method. Various combinations of predictive variables were developed based on variable importance and experimented for predicting topsoil organic carbon using random forest. The prediction results were validated using a cross validation approach. Results showed that base levels (given as the average of the left and right minimum values of a time series profile) for both seasons were the most important predictors in this area. Adding both crop rotation and the two phenological parameters to the natural environment variables increased the prediction accuracies by 50% in terms of R2and 13.4% in terms of root mean square error (RMSE). This study demonstrates the effectiveness of crop phenology in mapping SOC in croplands.
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