基于Sentinel-2卫星影像的黑龙江绥化市土壤全氮定量遥感反演

Quantitative inversion of soil total nitrogen in Suihua City of Heilongjiang in China using Sentinel-2 remote sensing images

为及时准确评估黑土区土壤全氮(soil total nitrogen,STN)含量的空间分布,以指导作物精准施肥和提高农作物产量,该研究基于绥化市实测STN数据和Sentinel-2卫星Level-2A遥感影像反射率,构建光谱指数结合环境变量的STN预测模型,包括随机森林(random forest,RF)、自适应增强(adaptive boosting,AdaBoost)、梯度提升(gradient boosting categorical features,CatBoost)等集成学习算法和多元逐步线性回归(simple linear regression,SLR)、支持向量机(support vector regression,SVR)、神经网络(back propagation neural network,BPNN)等监督学习算法,并考虑波段1~12遥感反射率、波段1~12遥感反射率联合光谱指数和环境变量作为算法输入变量的2种情景。结果表明:1)绥化市实测STN平均含量为1904.06 mg/kg,变异系数为17.93%;2)以波段1~12遥感反射率作为输入变量时,6种STN模型验证集拟合决定系数(coefficient of determination,R^(2))小于0.6,模型验证集决定系数精度由大到小顺序为:RF、CatBoost、AdaBoost、BPNN、SLR、SVR;3)结合波段1~12遥感反射率、光谱指数和环境变量优选方法,构建STN含量预测模型,模型验证集决定系数精度由大到小顺序为:RF、CatBoost、BPNN、AdaBoost、SLR、SVR,验证集模型决定系数精度提升幅度从大到小依次为RF、SVR、BPNN、AdaBoost、CatBoost、SLR,其中RF模型验证集决定系数预测精度提升最大,决定系数增加0.22,均方根误差(root mean square error,RMSE)降低了35.30 mg/kg;4)基于光谱指数和环境变量优选的机器学习算法具有强大的非线性拟合能力,RF能够更好地模拟STN与遥感光谱信息及地形因子之间复杂的多元非线性关系,并获得较高的实测和反演模型拟合结果;5)结合模型,绥化市STN的空间分布呈现东北高西南低、由北向南逐渐降低及中部略高的空间分布特点。研究结果为东北黑土区STN含量实时动态监测、土地肥力评价和农业可持续发展提供技术支持,为开展黑土地保护与利用及农田生态系统保护提供决策依据。

Spatial distributions of soil total nitrogen(STN)can greatly contribute to the precision fertilization and crop yield in black soil area.Many efforts have been devoted to the accurate algorithms for the estimation of STN contents.This study aims to firstly propose the applicable integrated machine learning algorithms(e.g.,Random Forest(RF),Adaptive boosting(AdaBoost)and Gradient boosting categorical features(CatBoost))and Supervised learning algorithms(e.g.,Simple linear regression(SLR),Support vector regression(SVR)and Back propagation neural network(BPNN)).The spectral indexes and environmental variables were then integrated using Multispectral Imager(MSI)product,in order to seamlessly retrieve the spatial distributions of STN.A large number of soil samples were collected in Suihua City,and the synchronous reflectance that embedded in better quality of Sentinel-2 Level-2A images.Likewise,two scenarios were considered,e.g.,band 1-12 reflectance or combining them with spectral indexes and environmental variables(digital elevation model,temperature,precipitation and soil types).The results showed that the average STN of in situ measured samples was 1904.06 mg/kg,with a coefficient of variation of 17.93%.The coefficients of determination(R^(2))were smaller than 0.6 between the measured and derived values from the developed STN algorithms,when the band 1-12 reflectance as the input variables.The performances of six STN algorithms for the validated dataset were ranked in the descending order of RF,CatBoost,AdaBoost,BPNN,SLR and SVR,whereas,the importance were ranked in the order of RF,SVR,BPNN,AdaBoost,CatBoost,and SLR.Once the band 1-12 reflectance,spectral indexes,and environmental variables were as the input variables,the performance of STN algorithms was improved significantly in the validated dataset,of which the R^(2)increased by 0.22 and root mean square error(RMSE)decreased by 35.30 mg/kg.In total,the accuracies of STN algorithms were in the descending order of RF,CatBoost,AdaBoost,BPNN,MLSR,and SVR.Hence,the RF can be expected to simulate the nonlinear relationships between reflectance and STN,and then obtain a better degree of measured-and derived-fitting,indicating powerful nonlinear ability.Furthermore,the STN content was mapped using Sentinel-2 level2A imagery and RF algorithm,in order to examine the spatial variation.The spatial distribution of STN content was higher in the northeast,whereas lower in southwest-decreasing gradually from north to southand slightly higher in middle of Suihua City.This was attributed to the large number of environmental variables.Anyway,much more attention can be paid for the decision-making on the protection of‘Black soil’and natural ecosystems.The finding can provide the technical assistances on dynamically monitoring STN contents,in order to evaluate the soil fertility for the sustainable agricultural development in black soil area of Northeast China.