Leaf chlorophyll content retrieval of wheat by simulated RapidEye, Sentinel-2 and EnMAP data

Leaf chlorophyll content(LCC)is an important physiological indicator of the actual health status of individual plants.An accurate estimation of LCC can therefore provide valuable information for precision field management.Red-edge information from hyperspectral data has been widely used to estimate crop LCC.However,after the advent of red-edge bands in satellite imagery,no systematic evaluation of the performance of satellite data has been conducted.Toward this end,we analyze herein the performance of winter wheat LCC retrieval of currant and forthcoming satellites(RapidEye,Sentinel-2 and EnMAP)and their new red-edge bands by using partial least squares regression(PLSR)and a vegetation-indexbased approach.These satellite spectral data were obtained by resampling ground-measured hyperspectral data under various field conditions and according to specific spectral response functions and spectral resolution.The results showed:1)This study confirmed that RapidEye,Sentinel-2 and EnMAP data are suitable for winter wheat LCC retrieval.For the PLSR approach,Sentinel-2 data provided more accurate estimates of LCC(R2=0.755,0.844,0.805 for 2002,2010,and 2002+2010)than do RapidEye data(R2=0.689,0.710,0.707 for 2002,2010,and 2002+2010)and EnMAP data(R2=0.735,0.867,0.771 for 2002,2010,and 2002+2010).For index-based approaches,the MERIS terrestrial chlorophyll index,which is a vegetation index with two red-edge bands,was the most sensitive and robust index for LCC for both the Sentinel-2 and EnMAP data(R2≥0.628),and the indices(NDRE1,SRRE1 and CIRE1)with a single red-edge band were the most sensitive and robust indices for the RapidEye data(R2≥0.420);2)According to the analysis of the effect of the wavelength and number of used red-edge spectral bands on LCC retrieval,the short-wavelength red-edge bands(from 699 to 734 nm)provided more accurate predictions when using the PLSR approach,whereas the long-wavelength red-edge bands(740 to 783 nm)gave more accurate predictions when using the vegetation indice(VI)approach.In addition,the prediction accuracy of RapidEye,Sentinel-2 and EnMAP data was improved gradually because of more number of red-edge bands and higher spectral resolution;VI regression models that contain a single or multiple red-edge bands provided more accurate predictions of LCC than those without red-edge bands,but for normalized difference vegetation index(NDVI)-,simple ratio(SR)-and chlorophyll index(CI)-like index,two red-edge bands index didn’t significantly improve the predictive accuracy of LCC than those indices with a single red-edge band.Although satellite data with higher spectral resolution and a greater number of red-edge bands marginally improve the accuracy of estimates of crop LCC,the level of this improvement remains insufficient because of higher spectral resolution,which results in a worse signal-to-noise ratio.The results of this study are helpful to accurately monitor LCC of winter wheat in large-area and provide some valuable advice for design of red-edge spectral bands of satellite sensor in future.

矿业废弃地重构土壤重金属含量高光谱反演

矿产资源对工业和国民经济的发展有重要的作用,但是随着矿业开采规模的扩大,资源枯竭、经营不善而形成的矿业废弃地越来越多。由于长时间受到采矿的影响,矿业废弃地土壤中存在大量的重金属元素,高浓度重金属可能会对环境和人体产生影响。土地复垦是整治污染、退化土壤再利用的重要方法,对重构后的土壤进行重金属含量检测是衡量土地复垦成效的重要指标,需要长期进行跟踪监测。传统的化学检测方法效率低、成本高、无法实现重金属大范围检测。高光谱是一种新兴的、发展潜力巨大的技术,在环境保护,资源利用,区域可持续发展等方面有着广泛的应用。经过近几十年的快速发展,仪器精度逐渐提高,检测方法逐渐成熟,为实现土壤重金属高效、便捷检测提供了可能。正常土壤重金属含量一般相对较低,采用光谱测量重金属含量较为困难,但铁矿开采区矿业废弃地由于土壤中的铁元素较多,会使土壤中的重金属的存在和聚集形式发生变化,影响重金属对光谱的响应,从而使土壤光谱反射率与重金属含量之间关系更加明显。以湖北省大冶市复垦矿区研究区,采样化学检测方法获取土壤重金属(As, Cr, Zn)含量;借助于美国ASD公司生产的FieldSpec4地物光谱仪(350～2 500 nm)获取土壤反射率,应用一阶微分、倒数对数、连续统去除法分别对反射率曲线进行预处理,提取出光谱特征波段,分析三种重金属元素与光谱特征间的相关性并建立逐步回归模型。研究表明,光谱数据预处理可使光谱特征波段更加明显,其中一阶微分和连续统去除法的效果最为明显。3种重金属元素的特征波段为495, 545, 675, 995, 1 425, 1 505, 1 935, 2 165, 2 205, 2 275和2 355 nm。将土壤重金属含量与光谱特征波段之间做相关性分析,三种重金属都表现出了与光谱曲线的相关性,相关系数大部分都达到了0.5以上,最大相关系数为0.663,由于重金属种类和预处理方式的不同会导致相关性系数存在明显的差异。利用与土壤重金属相关性最大的特征波段建立三种重金属反演模型,并以反演模型r大小选择每种重金属的最优反演模型。由于重金属种类的不同,模型的选择也有差异, Cr和Zn一阶微分逐步回归为最佳反演模型,重金属As连续统去除法逐步回归为最佳反演模型。通过检验,三种重金属中Cr反演效果最好, RMSE为2.67,其次是Zn和As。对比当前不同检测手段可知,基于土样和光谱数据预处理的土壤重金属含量地物光谱仪高光谱反演是比较理想的。可为矿业废弃地土壤重金属高光谱反演提供参考。

MaxEnt与遥感技术在草原蝗虫灾害风险监测中的应用:以东乌珠穆沁旗农业文化遗产为例

东乌珠穆沁旗游牧生产系统是我国重要的农业文化遗产,具有极高的生态、经济、景观、技术和文化价值,然而近年来当地饱受蝗虫灾害的影响,草原正面临着前所未有的威胁与挑战。该研究选取东乌珠穆沁旗为研究区,以草原蝗虫为风险因子,结合草原蝗虫生长特性,基于最大熵模型(MaxEnt),构建基于遥感、土壤、植被和地形的草原蝗虫发生风险指标体系,分析不同生境因子对草原蝗虫发生的影响,对草原蝗虫发生风险区进行提取并分级。结果表明:模型模拟结果良好,平均曲线下面积(areas under curve,AUC)为0.826;草原蝗虫发生风险的主要影响因子为孵化期地表温度、生长期地表温度和产卵期降水;高风险区主要分布在嘎达布其镇,面积为920 km 2。该研究有利于更好地保护东乌珠穆沁旗游牧生产系统农业文化遗产,也可为其他草原类农业文化遗产灾害风险监测提供技术支撑。

Spatial Interpolation of Soil Texture Using Compositional Kriging and Regression Kriging with Consideration of the Characteristics of Compositional Data and Environment Variables

The spatial interpolation for soil texture does not necessarily satisfy the constant sum and nonnegativity constraints. Meanwhile, although numeric and categorical variables have been used as auxiliary variables to improve prediction accuracy of soil attributes such as soil organic matter, they （especially the categorical variables） are rarely used in spatial prediction of soil texture. The objective of our study was to comparing the performance of the methods for spatial prediction of soil texture with consideration of the characteristics of compositional data and auxiliary variables. These methods include the ordinary kriging with the symmetry logratio transform, regression kriging with the symmetry logratio transform, and compositional kriging （CK） approaches. The root mean squared error （RMSE）, the relative improvement value of RMSE and Aitchison＇s distance （DA） were all utilized to assess the accuracy of prediction and the mean squared deviation ratio was used to evaluate the goodness of fit of the theoretical estimate of error. The results showed that the prediction methods utilized in this paper could enable interpolation results of soil texture to satisfy the constant sum and nonnegativity constraints. Prediction accuracy and model fitting effect of the CK approach were better, suggesting that the CK method was more appropriate for predicting soil texture. The CK method is directly interpolated on soil texture, which ensures that it is optimal unbiased estimator. If the environment variables are appropriately selected as auxiliary variables, spatial variability of soil texture can be predicted reasonably and accordingly the predicted results will be satisfied.

Effects of land use change on the spatiotemporal variability of soil organic carbon in an urban-rural ecotone of Beijing,China

Understanding the effects of land use changes on the spatiotemporal variation of soil organic carbon （SOC） can provide guidance for low carbon and sustainable agriculture. In this paper, based on the large-scale datasets of soil surveys in 1982 and 2009 for Pinggu District -- an urban-rural ecotone of Beijing, China, the effects of land use and land use changes on both temporal variation and spatial variation of SOC were analyzed. Results showed that from 1982 to 2009 in Pinggu District, the following land use change mainly occurred： Grain cropland converted to orchard or vegetable land, and grassland converted to forestland. The SOC content decreased in region where the land use type changed to grain cropland （e.g., vegetable land to grain cropland decreased by 0.7 g kg-1; orchard to grain cropland decreased by 0.2 g kg-l）. In contrast, the SOC content increased in region where the land use type changed to either orchard （excluding forestland） or forestland （e.g., grain cropland to orchard and forestland increased by 2.7 and 2.4 g kg-1, respectively; grassland to orchard and forestland increased by 4.8 and 4.9 g kg-1, respectively）. The organic carbon accumulation capacity per unit mass of the soil increased in the following order： grain cropland soil〈vegetable land/grassland soil〈orchard soil〈forestland soil. Therefore, to both secure supply of agricultural products and develop low carbon agriculture in a modern city, orchard has proven to be a good choice for land using.