Using scattered hyperspectral imagery data to map the soil properties of a region

Airborne hyperspectral imagery has been recently proved to be a successful technique for predicting soil properties of the bare soil surfaces that are usually scattered in the landscape. This new soil covariate could much improve the digital soil mapping (DSM) of soil properties over larger areas. To illustrate this, we experimented with digital soil mapping in a 24.6‐km² area located in the vineyard plain of Languedoc. As input data, we used 200 points with clay content measurements and 192 bare soil fields representing 3.5% of the total area in which the clay contents of the soil surface were successfully mapped at 5‐m resolution by hyperspectral remote sensing. The clay contents were estimated from CR₂₂₀₆, a spectrometric indicator that quantifies specific absorption features of clay at 2206 nm. We demonstrated by cross‐validation that the co‐kriging procedure based on our co‐regionalization model provided accurate error estimates at the clay measurement sites. Then, we applied a block co‐kriging model to map the mean clay content at increasing resolutions (50 , 100, 250 and 500 m). The results showed the following: (i) using hyperspectral data significantly increased the accuracy of the mean clay content estimations; (ii) a block co‐kriging procedure with reliable estimates of error variance can be used to estimate mean clay contents over larger areas and at coarser resolutions with acceptable and predictable errors and (iii) various maps can be produced that represent different compromises between prediction accuracy and spatial resolution.     

卫星遥感在东南沿海区域蒸散（发）量计算上的反演

蒸散（发）量是研究农业生产和气象变化的重要内容,而自然陆地区域蒸散（发）量的求取往往比较困难。本文利用Landsat TM/ETM＋卫星遥感资料求出能够体现地表特征的重要参数,遵循陆面能量平衡原理SEBAL,给出了依据研究区特点分为植被覆盖和裸土两种类型的区域蒸散（发）量计算模型。利用该模型对东南沿海城市泉州市的蒸散（发）量进行了反演,并分析了遥感反演模型的特点和该地区蒸散（发）作用的特征,使得区域蒸散（发）量的估算成为可能。     

基于Landsat8和Sentinel-2数据的砒砂岩区生物量估算的差异性

[目的] 分析基于不同空间分辨率遥感影像估算的地上生物量(above ground biomass,AGB)差异,为遥感估算荒漠生态系统AGB的研究中不同空间分辨率影像的选择提供依据。[方法] 在地面AGB调查的基础上,结合Landsat 8与Sentinel-2影像建立AGB-MSAVI统计模型,对砒砂岩区AGB进行了遥感估算,并分析不同植被覆盖区(高、中、低) AGB估算的差异性。[结果] Landsat 8与Sentinel-2影像均能较好地实现AGB估算,AGB估算结果在空间分布上具有相似性。基于Landsat 8和Sentinel-2数据估算AGB模型平均相对误差分别为13.41%和11.42%,基于Sentinel-2数据的AGB估算精度较高。[结论] 不同植被覆盖区Sentinel-2与Landsat 8数据估算的AGB存在一定的差异,低植被覆盖和高植被覆盖区,两种遥感数据估算的AGB差异相对较小;中植被覆盖区,遥感数据受到空间分辨率的制约,空间异质性影响相对显著,两种遥感数据估算的AGB差异较大。高空间分辨率遥感影像对AGB估算精度的提高具有一定效果。     

平原区土壤质地的反射光谱预测与地统计制图

基于地统计方法的土壤属性制图通常需要大量的采样与实验室测定。本研究提出利用可见光近红外(visible-nearinfrared spectroscopy,VNIR)光谱技术测定替代实验室测定,并与地统计方法相结合预测土壤质地的空间变异。通过建立砂粒(0.02 mm),粉粒(0.002~0.02 mm),黏粒(0.002 mm)含量的VNIR光谱预测模型,将模型预测得到的质地数据和建模点实测质地数据一同用于地统计分析和Kriging插值制图。以江苏北部黄淮平原地区为案例的研究结果表明,砂粒、粉粒、黏粒含量的预测值和实测值的均方根误差(RMSE)分别为8.67%、6.90%3、.51%,平均绝对误差(MAE)分别为6.46%、5.60%、3.05%,显示了较高的预测精度。研究为快速获取平原区土壤质地空间分布提供了新的可能的途径。     

Bare Soil Reflectance to Characterize Variability in Soil Properties

Remote sensing allows for the rapid and inexpensive acquisition of soil reflectance data. Knowing what soil parameters have the greatest influence on bare soil imagery will facilitate better use of remote sensing for precision crop management. The objectives of this study were (i) to determine measured soil properties that are most influential on remotely sensed bare soil reflectance and (ii) to select which spectral band or combination of spectral bands is best for describing individual soil properties. This study was conducted on three study sites located in northeastern Colorado. All sites were in irrigated continuous corn (Zea mays L.) cropping systems. Remotely sensed imagery was acquired by aircraft prior to planting. Soil samples were collected and analyzed for bulk density, soil color (moist and dry), organic matter, organic carbon, soil texture, and cone index. Principal component analysis (PCA) was performed for the green, red, and near-infrared (NIR) bands of the imagery. Least-squares regression analysis was used for analyzing relationships between remote sensing data and soil data. Across study sites, the first principal components of the green, red, and NIR bands were found to have significant statistical relationships with organic carbon and sand, silt, and clay fractions. Individual spectral bands explained a significant portion of the variability in soil moisture, moist soil color, dry soil color, organic carbon, sand, silt, and clay. Results from this study support the use of remote sensing for assessment of soil variability.     

基于地表昼夜温差的平原区土壤质地模糊聚类与预测制图

The use of landscape covariates to estimate soil properties is not suitable for the areas of low relief due to the high variability of soil properties in similar topographic and vegetation conditions.A new method was implemented to map regional soil texture (in terms of sand,silt and clay contents) by hypothesizing that the change in the land surface diurnal temperature difference (DTD) is related to soil texture in case of a relatively homogeneous rainfall input.To examine this hypothesis,the DTDs from moderate resolution imagine spectroradiometer (MODIS) during a selected time period,i.e.,after a heavy rainfall between autumn harvest and autumn sowing,were classified using fuzzy-c-means (FCM) clustering.Six classes were generated,and for each class,the sand (> 0.05 mm),silt (0.002-0.05 mm) and clay (< 0.002 mm) contents at the location of maximum membership value were considered as the typical values of that class.A weighted average model was then used to digitally map soil texture.The results showed that the predicted map quite accurately reflected the regional soil variation.A validation dataset produced estimates of error for the predicted maps of sand,silt and clay contents at root mean of squared error values of 8.4%,7.8% and 2.3%,respectively,which is satisfactory in a practical context.This study thus provided a methodology that can help improve the accuracy and efficiency of soil texture mapping in plain areas using easily available data sources.     

典型黑土区耕作土壤质地遥感时间窗口及影响因素分析

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。     

基于TM数据的黑土有机质含量空间格局反演研究

以吉林省黑土区为例,采集区域土壤样本,获取Landsat TM遥感影像,基于有机质含量(SOM)与土壤反射率的定量关系,筛选出与土壤有机质分布相关的波段TM1、TM5,建立区域土壤有机质遥感预测模型。结果表明,表层土壤有机质含量的对数值与TM1、TM5的灰度值(Digital Number,DN)呈显著负相关关系,满足二次多项式回归关系,基于TM1、TM5波段DN值的回归模型预测研究区表层土壤有机质含量,结果可靠。研究区表层土壤有机质含量15 g kg-1的区域主要分布在东部地区,含量在15~20 g kg-1的区域主要分布在中部地区,含量在20~25 g kg-1的土壤主要集中在西部地区。调查表明东部地区和中部地区主要是典型黑土,地形部位较高,排水条件较好;西部地区主要是草甸黑土,地势平坦,地下水位适中,水分条件充足,有机质含量较高。     

Uncertainty in the spatial prediction of soil texture: Comparison of regression tree and Random Forest models

Within the southern Ecuadorian Andes, landslides have an impact on landscape development. Landslide risk estimation as well as hydrological modelling requires physical soil data. Statistical models were adapted to predict the spatial distribution of soil texture from terrain parameters. For this purpose, 56 soil profiles were analysed horizon-wise by pipette and laser method. Results by pipette compared to laser method showed the expected shift to higher silt and lower clay contents. Linear regression equations were adapted. The performance of regression tree (RT) and Random Forest (RF) models was compared by hundredfold model runs on random Jackknife partitions. Digital soil maps of sand, silt and clay percentage mean and standard deviation indicate model variability and prediction uncertainty.RF models performed better than RT models. All terrain factors considered in the analysis influenced soil texture of the surface horizon, but altitude a.s.l. was assigned the highest variable importance during model construction. Shallow subsurface flow is considered responsible for increasing sand/clay ratios with increasing altitude, on steep slopes and with overland flow distance to the channel network by removing clay particles downslope. Deeper soil layers are not influenced by this process and therefore, did not show the same texture properties. However, the influence of parent material and landslides on the spatial distribution of soil texture cannot be neglected. Model performance, most probably, could be improved by a bigger dataset.     

Evaluation of the predictive capabilities of Landsat 7 ETM+ on 50‐cm mean annual soil temperature in the Transylvanian Plain,Romania

Mean annual soil temperature has important implications for crops as well as for soil classification and formation. Landsat 7 Enhanced Thematic Mapper Plus (ETM+) band‐6 was analysed to determine its relationship with mean annual soil temperature (MAST) at 50 cm in the Transylvanian Plain, Romania. Band‐6 is available in both high and low gain formats from the United States Geological Survey; for our study only high gain was evaluated because of the increased resolution that it provides. Both of the gain levels of band‐6 are measured at 10.4–12.5 µm (thermal infrared), at 60‐m spatial resolution. Four different months of Landsat 7 ETM+ data were used to predict MAST and compared with 50‐cm soil temperature data measured on‐site with in situ sensors and data logging stations. Despite no correction for land cover differences across the plain, strong relationships were found between the Landsat‐predicted and field measured MAST with a coefficient of determination (R²) for July, August, December and February of 0.63. Multiple regression analysis (MAST_Regression) provided a weaker relationship, when compared with MAST_{in situ}, with a coefficient of determination (R²) of 0.42. Significant differences existed between urban and agricultural land covers, as identified by Coordination of Information on the Environment (CORINE) data. The use of Landsat 7 ETM+ could reduce the time and expense of large field studies for determining MAST. These data could then be used for temperature models of entire regions, for a range of land management options.     

An assessment of Sentinel-1 synthetic aperture radar,geophysical and topographical covariates for estimating topsoil particle-size fractions

Data derived from synthetic aperture radar (SAR) are widely employed to predict soil properties, particularly soil moisture and soil carbon content. However, few studies address the use of microwave sensors for soil texture retrieval and those that do are typically constrained to bare soil conditions. Here, we test two statistical modelling approaches—linear (with and without interaction terms) and tree-based models, namely compositional linear regression model (LRM) and random forest (RF)—and both nongeophysical (e.g., surface soil moisture, topographic, etc) and geophysical-based (electromagnetic, magnetic and radiometric) covariates to estimate soil texture (sand %, silt % and clay %), using microwave remote sensing data (ESA Sentinel-1). The statistical models evaluated explicitly consider the compositional nature of soil texture and were evaluated with leave-one-out cross-validation (LOOCV). Our findings indicate that both modelling approaches yielded better estimates when fitted without the geophysical covariates. Based on the Nash–Sutcliffe efficiency coefficient (NSE), LRM slightly outperformed RF, with NSE values for sand, silt and clay of 0.94, 0.62 and 0.46, respectively; for RF, the NSE values were 0.93, 0.59 and 0.44. When interaction terms were included, RF was found to outperform LRM. The inclusion of interactions in the LRM resulted in a decrease in NSE value and an increase in the size of the residuals. Findings also indicate that the use of radar-derived variables (e.g., VV, VH, RVI) alone was not able to predict soil particle size without the aid of other covariates. Our findings highlight the importance of explicitly considering the compositional nature of soil texture information in statistical analysis and regression modelling. As part of the continued assessment of microwave remote sensing data (e.g., ESA Sentinel-1) for predicting topsoil particle size, we intend to test surface scattering information derived from the dual-polarimetric decomposition technique and integrate that predictor into the models in order to deal with the effects of vegetation cover on topsoil backscattering.     

珠江三角洲土地利用/覆盖变化对地表温度的影响

Remote sensing and geographic information systems （GIS） technologies were used to detect land use/cover changes （LUCC） and to assess their impacts on land surface temperature （LST） in the Zhujiang Delta. Multi-temporal Landsat TM and Landsat ETM＋ data were employed to identify patterns of LUCC as well as to quantify urban expansion and the associated decrease of vegetation cover. The thermal infrared bands of the data were used to retrieve LST, The results revealed a strong and uneven urban growth, which caused LST to raise 4.56℃ in the newly urbanized part of the study area. Overall, remote sensing and GIS technologies were effective approaches for monitoring and analyzing urban growth patterns and evaluating their impacts on LST.     

嫩江沙地沙质荒漠化程度遥感定量评价——以杜尔伯特蒙古族自治县为例

杜尔伯特蒙古族自治县位于嫩江沙地的中心,以杜尔伯特蒙古族自治县为例研究嫩江沙地的荒漠化程度有较好的代表性。在研究区选取63个样地实地调查荒漠化程度,同时获取研究区的ETM＋遥感影像,对荒漠化主要评价因子植被盖度、生物量、裸沙占地百分比进行遥感定量反演,地表结皮和土壤质地采用定性的方法结合目视解译进行提取。在现有荒漠化评价方法的基础上,建立以像元为单位的荒漠化程度评价的定量化遥感信息模型,并输出荒漠化程度分布图。利用63个实测样地数据评价遥感信息模型的精度,被正确评价的样点数为57个,遥感信息模型对杜尔伯特蒙古族自治县荒漠化程度进行定量评价的精度达到90.5%。     

Small scale digital soil mapping in Southeastern Kenya

Digital soil mapping techniques appear to be an interesting alternative for traditional soil survey techniques. However, most applications deal with (semi-)detailed soil surveys where soil variability is determined by a limited number of soil forming factors. The question that remains is whether digital soil mapping techniques are equally suitable for exploratory or reconnaissance soil surveys in more extensive areas with limited data availability. We applied digital soil mapping in a 13,500 km² study area in Kenya with the main aim to create a reconnaissance soil map to assess clay and soil organic carbon contents in terraced maize fields. Soil spatial variability prediction was based on environmental correlation using the concepts of the soil forming factors equation. During field work, 95 composite soil samples were collected. Auxiliary spatially exhaustive data provided insight on the spatial variation of climate, land cover, topography and parent material. The final digital soil maps were elaborated using regression kriging. The variance explained by the regression kriging models was estimated as 13% and 37% for soil organic carbon and clay respectively. These results were confirmed by cross-validation and provide a significant improvement compared to the existing soil survey.     

遥感与气象数据结合预测小麦灌浆期白粉病

利用多源数据对区域尺度上小麦白粉病的发生状况准确及时地预报能为农业服务和农业植保等部门提供重要信息,实现小麦白粉病的有效预防。研究利用一景2014年5月6日的landsat8遥感影像提取出植被指数、地表温度(land surface temperature,LST)和影像中各波段反射率特征,同时用2014年3月-5月份的站点逐日地面气象资料计算获得各气象特征,并经过GIS空间插值分析得到相应的空间气象特征。通过Relief算法和泊松相关系数相结合的方式进行遥感和气象特征的筛选,最终得出改进的简单比值指数(modified simple ratio index,MSR)、重归一化植被指数(re-normalized difference vegetation index,RDVI)、3月21日-4月20日总日照时数和4月11日-5月10日大于0.1 mm降雨日数。采用相关向量机(relevance vector machine,RVM)的方法分别用筛选出的遥感、气象数据特征及2种数据特征相结合的方式构建了河北省石家庄市藁城、晋州和赵县3地区小麦灌浆期白粉病的发生预测模型,并对3种不同数据模型进行了验证与评估。试验结果表明,遥感气象数据模型的总体精度达到84.2%,优于遥感数据模型的80.0%和气象数据模型的74.7%。进而得出,相比于单站点准确和空间不连续的气象数据和类型单一的遥感数据,遥感气象数据更适合于区域尺度范围内的作物病虫害发生发展状况的预测研究。     

Neural network ensemble residual kriging application for spatial variability of soil properties

High quality, agricultural nutrient distribution maps are necessary for precision management, but depend on initial soil sample analyses and interpolation techniques. To examine the methodologies for and explore the capability of interpolating soil properties based on neural network ensemble residual kriging, a silage field at Hayes, Northern Ireland, UK, was selected for this study with all samples being split into independent training and validation data sets. The training data set, comprised of five soil properties: soil pH, soil available P, soil available K, soil available Mg and soil available S,was modeled for spatial variability using 1) neural network ensemble residual kriging, 2) neural network ensemble and 3) kriging with their accuracies being estimated by means of the validation data sets. Ordinary kriging of the residuals provided accurate local estimates, while final estimates were produced as a sum of the artificial neural network (ANN) ensemble estimates and the ordinary kriging estimates of the residuals. Compared to kriging and neural network ensemble,the neural network ensemble residual kriging achieved better or similar accuracy for predicting and estimating contour maps. Thus, the results demonstrated that ANN ensemble residual kriging was an efficient alternative to the conventional geo-statistical models that were usually used for interpolation of a data set in the soil science area.     

基于温度植被旱情指数的徐州市郊干旱遥感监测

利用Landsat TM/ETM+数据,以徐州市郊为研究区,获取归一化植被指数(NDVI)、土壤调整植被指数(SAVI)和地表温度(Ts)信息,分别构建NDVI-Ts和SAVI-Ts特征空间,依据这两个特征空间计算出研究区2001年4月3日和2007年5月14日的温度植被旱情指数TVDI(NDVI)和TVDI(SAVI),并分别与地表温度(Ts)和降水量进行了相关评价.结果表明,TVDI可用于实现大范围的干旱监测,SAVI能够修正NDVI对土壤背景的敏感,基于SAVI的反演结果明显优于基于NDVI的反演结果,能够有效地运用于干旱监测.     

The use of region partitioning to improve the representation of geo statistically mapped soil attributes

An attempt to improve the representation of a geo statistically mapped soil attribute, clay content of the surface soil, through partitioning of the study area into two new regions was made. A topographic boundary divided the study area into hill and plain regions. Possible global non-stationarity or non-stationarity within the two newly defined regions was dealt with through the use of intrinsic random functions (IRF) of order k. Cross-validation of generalized covariance functions suggested that ordinary kriging might also have been appropriate. Exponential variogram models were subsequently fitted to the experimental variograms for each region. IRF-k block kriging and ordinary block kriging were then used as the primary methods of estimation. Both IRF-k and ordinary kriging performed badly in the vicinity of the topographic boundary when global models were used. This discontinuity was removed, at the expense of the introduction of some additional edge effects, when the hill and plain regions were kriged using models appropriate to each region. Independent zero-order generalized covariance functions with nugget and linear terms and exponential variogram models produced similar representations of clay content within each region, when used with their respective estimators. Splitting the region resulted in a 6% reduction in mean absolute deviation and a 14% reduction in mean squared deviation of predicted clay contents compared with a global model.     

Kriging and mapping of copper,lead, and mercury contents in surface soil in Shenzhen area

Two-dimensional ordinary block kriging as an optimal interpolation technique was applied to produce regular grids of predicted estimates of copper, lead, and mercury contents in surface soil in the Shenzhen area for mapping purposes. The kriging analysis was based on theoretical variograms calculated from measured data of eighty three top soil samples. The appropriateness of the whole procedure of kriging analysis was evaluated by both cross-validation and kriging standard deviation mapping. The similarity in area variation between the distribution of soil parent material and the mapped results of copper and lead is demonstrated. The possible influence of agricultural contamination on the spatial distribution pattern of mercurcy is also discussed.