粒子群优化神经网络的土壤有机质高光谱估测

针对提高土壤有机质高光谱估测精度的问题,该文对山东省泰安市的92个棕壤样本进行光谱去噪,剔除异常样本处理后,对光谱反射率进行11种变换,发现一阶微分变换最佳;然后计算土壤有机质含量与变换后光谱反射率的相关系数,选取5个特征波段,分别利用多元线性回归、BP神经网络、支持向量机、粒子群优化神经网络4种方法建立土壤有机质含量高光谱估测模型并进行精度比较。实验结果表明,多元线性回归、BP神经网络、支持向量机和粒子群优化神经网络模型的决定系数R2分别为0.520 3、0.665 4、0.735 0和0.853 0,均方根误差分别为2.12、1.99、1.45和1.08。研究结果表明,粒子群优化神经网络的反演精度高、稳定性强,可有效提高土壤有机质的光谱估测能力。     

利用ANFIS方法反演裸土区土壤水分含量

为了更好地进行土壤水分反演,发展了一种基于ALOS/PALSAR数据、利用自适应神经模糊推理系统(adaptive neuro fuzzy inference system,ANFIS)反演土壤水分的方法.首先,根据研究区实际情况,利用AIEM和Oh模型模拟了试验区裸土区的后向散射特性,建立了后向散射系数与地表粗糙度之间的关系;然后,考虑到研究区地表粗糙度几乎没有变化这一情况,设定了地表粗糙度对后向散射系数的影响为常量;在此基础上,分别利用ANFIS,BP神经网络、多元线性回归和多元非线性回归方法构建了裸土区土壤水分的反演模型,并利用野外实测数据对模型进行了验证.研究结果表明,采用ANFIS方法构建的模型反演精度最高,其均方根误差为0.030,相对误差为14.5％.因此,可以利用ANFIS方法反演裸土区的土壤水分含量,其反演结果具有较高的精度.     

星地多源数据的区域土壤有机质数字制图

土壤有机质(SOM)是全球碳循环、土壤养分的重要组成部分,精确估算土壤有机质含量具有重要意义。本文以中国东北—华北平原为研究区,收集了1078个土壤样本,以遥感数据(MODIS,TRMM和STRM数据)与土壤地面光谱数据为预测因子,运用基于树形结构的数据挖掘技术构建土壤有机质-环境预测因子模型进行数字土壤制图。通过不同建模样本数建模精度比较,选择300个样本数时的模型为最优模型。建模结果表明土壤光谱和气候因子是研究区SOM变异的主控因子,生物因子次之,而地形因子影响最小。预测结果经检验,RMSE为7.25,R2为0.69,RPD为1.53制图结果与基于第二次全国土壤普查数据的土壤有机质地图具有相似的分布规律,呈现SOM自东北向西南递减的趋势。通过比较分析发现,经过20年左右的土地开发与利用,研究区低SOM和高SOM含量土壤面积减少,而中等SOM含量土壤面积增加。     

Determining iron content in Mediterranean soils in partly vegetated areas,using spectral reflectance and imaging spectroscopy

The possibility of quantifying iron content in the topsoil of the slopes of the El Hacho Mountain complex in Southern Spain using imaging spectroscopy is investigated. Laboratory, field and airborne spectrometer (ROSIS) data are acquired, in combination with soil samples, which are analysed for dithionite extractable iron (Fe_d) content. Analysis of the properties of two iron related absorption features present in laboratory spectra demonstrates good relations, especially between the standard deviation (S.D.) of the values in an absorption feature and the Fe_d content (R² = 0.67) as well as the ratio based Redness Index (R² = 0.51). Such derived relations are less strong for the ROSIS data (R² for S.D. = 0.26 and R² for Redness Index = 0.22). The spatial distribution of iron in vegetated areas shows a strong sensitivity of these relations with the presence of vegetation. A combination of both methods shows that the overestimation of the Fe_d content with the one method is (partly) compensated by the underestimation with the other method.     

Motivation,development and validation of a new spectral greenness index: A spectral dimension related to foliage projective cover

A method is presented for the development of a regional Landsat-5 Thematic Mapper (TM) and Landsat-7 Enhanced Thematic Mapper plus (ETM+) spectral greenness index, coherent with a six-dimensional index set, based on a single ETM+ spectral image of a reference landscape. The first three indices of the set are determined by a polar transformation of the first three principal components of the reference image and relate to scene brightness, percent foliage projective cover (FPC) and water related features. The remaining three principal components, of diminishing significance with respect to the reference image, complete the set.The reference landscape, a 2200 km² area containing a mix of cattle pasture, native woodland and forest, is located near Injune in South East Queensland, Australia. The indices developed from the reference image were tested using TM spectral images from 19 regionally dispersed areas in Queensland, representative of dissimilar landscapes containing woody vegetation ranging from tall closed forest to low open woodland. Examples of image transformations and two-dimensional feature space plots are used to demonstrate image interpretations related to the first three indices. Coherent, sensible, interpretations of landscape features in images composed of the first three indices can be made in terms of brightness (red), foliage cover (green) and water (blue). A limited comparison is made with similar existing indices. The proposed greenness index was found to be very strongly related to FPC and insensitive to smoke. A novel Bayesian, bounded space, modelling method, was used to validate the greenness index as a good predictor of FPC. Airborne LiDAR (Light Detection and Ranging) estimates of FPC along transects of the 19 sites provided the training and validation data. Other spectral indices from the set were found to be useful as model covariates that could improve FPC predictions. They act to adjust the greenness/FPC relationship to suit different spectral backgrounds. The inclusion of an external meteorological covariate showed that further improvements to regional-scale predictions of FPC could be gained over those based on spectral indices alone.     

Wavelength selection and spectral discrimination for paddy rice, with laboratory measurements of hyperspectral leaf reflectance

The objective of this research is to select the most sensitive wavelengths for the discrimination of the imperceptible spectral variations of paddy rice under different cultivation conditions. The paddy rice was cultivated under four different nitrogen cultivation levels and three water irrigation levels. There are 2151 hyperspectral wavelengths available, both in hyperspectral reflectance and energy space transformed spectral data. Based on these two data sets, the principal component analysis (PCA) and band-band correlation methods were used to select significant wavelengths with no reference to leaf biochemical properties, while the partial least squares (PLS) method assessed the contribution of each narrow band to leaf biochemical content associated with each loading weight across the nitrogen and water stresses. Moreover, several significant narrow bands and other broad bands were selected to establish eight kinds of wavelength (broad-band) combinations, focusing on comparing the performance of the narrow-band combinations instead of broad-band combinations for rice supervising applications. Finally, to investigate the capability of the selected wavelengths to diagnose the stress conditions across the different cultivation levels, four selected narrow bands (552, 675, 705 and 776 nm) were calculated and compared between nitrogen-stressed and non-stressed rice leaves using linear discriminant analysis (LDA). Also, wavelengths of 1158, 1378 and 1965 nm were identified as the most useful bands to diagnose the stress condition across three irrigation levels. Results indicated that good discrimination was achieved. Overall, the narrow bands based on hyperspectral reflectance data appear to have great potential for discriminating rice of differing cultivation conditions and for detecting stress in rice vegetation; these selected wavelengths also have great potential use for the designing of future sensors.     

Remote determination of chromophoric dissolved organic matter in lakes,China

Chromophoric dissolved organic matter (CDOM) strongly influences the water-leaving radiance from aquatic ecosystems. In most inland waters, the remote determination of CDOM absorption presents a central challenge due to their complex optical conditions. However, identifying the temporal and spatial variability of CDOM is fundamental to the understanding of aquatic biogeochemical dynamics. In the present study, semi-analytical and empirical modeling approaches were used to examine CDOM absorption in four, shallow, inland water bodies using the spectral bands and sensitivities of major satellite observational systems. Of the models examined, an empirical multiband model was found to provide the highest correlation with measured CDOM absorption. The spectral characteristics of the MERIS sensors yielded the best results with respect to the other available satellite sensors. High detrital load was observed to be a major impediment to estimating CDOM absorption, while lakes with elevated phytoplankton biomass did not present similar problems.     

Estimating soil salinity in Pingluo County of China using QuickBird data and soil reflectance spectra

Soil salinization is a worldwide environmental problem with severe economic and social consequences. In this paper, estimating the soil salinity of Pingluo County, China by a partial least squares regression (PLSR) predictive model was carried out using QuickBird data and soil reflectance spectra. At first, a relationship between the sensitive bands of soil salinity acquired from measured reflectance spectra and the spectral coverage of seven commonly used optical sensors was analyzed. Secondly, the potentiality of QuickBird data in estimating soil salinity by analyzing the correlations between the measured reflectance spectra and reflectance spectra derived from QuickBird data and analyzing the contributions of each band of QuickBird data to soil salinity estimation Finally, a PLSR predictive model of soil salinity was developed using reflectance spectra from QuickBird data and eight spectral indices derived from QuickBird data. The results indicated that the sensitive bands covered several bands of each optical sensor and these sensors can be used for soil salinity estimation. The result of estimation model showed that an accurate prediction of soil salinity can be made based on the PLSR method (R² = 0.992, RMSE = 0.195). The PLSR model's performance was better than that of the stepwise multiple regression (SMR) method. The results also indicated that using spectral indices such as intensity within spectral bands (Int1, Int2), soil salinity indices (SI1, SI2, SI3), the brightness index (BI), the normalized difference vegetation index (NDVI) and the ratio vegetation index (RVI) as independent model variables can help to increase the accuracy of soil salinity mapping. The NDVI and RVI can help to reduce the influences of vegetation cover and soil moisture on prediction accuracy. The method developed in this paper can be applied in other arid and semi-arid areas, such as western China.     

Effects of granulometric and mineralogical composition on spectral reflectance of soils in a Sahelian area

During the Hapex-Sahel international investigation, portable radiometers (Barringer and Cimel) were used to study, in the optical domain, the relationship between soil mineralogical and granulometric compositions and radiometric indices: redness index, colour index and texture index. The results show that the granulometric distribution and the mineralogical composition have both important influence on these indices.In this Sahelian area, for soils having more than 1% of hematite, the redness index presents a high correlation with hematite content. For soils with less than 1% of hematite, the redness index depends on the size of the particles and their distribution at the surface.For eolian sandy and ferricrust soils, the colour and texture indices present good correlations, respectively, with iron oxide and kaolinite content. No such correlation is observed for ferruginous soils because of the complex interactions existing between iron oxides, kaolinite and the size of particles.     

Evaluation of grasslands and spectral reflectance relationship to its biomass in Kanha National Park (M.P.), India

Grasslands play an important role in a National Park environment. Its evaluation through remote sensing satellite data has been carried out in the present study. In context of Kanha National Park low grasslands are having significant importance to support increasing grassier her‐bivore population. Low grasslands occur both at hill tops and valley flat area. Field spectral measurements have been carried out in both the low grassland sites to estimate biomass (above ground). The present study reveals that there exist strong correlation between dry weight of green biomass and vegetation index (IR/R) at both the sites.     

新疆焉耆盆地土壤湿度时空分布特征

本文以新疆焉耆盆地为研究区,首先利用实测数据和Landsat 8 OLI遥感数据获取土壤调查植被指数（MSAVI）和地表温度（Ts）,构建Ts-MSAVI特征空间,拟合特征空间的干湿方程;然后利用该特征空间计算温度植被干旱指数（TVDIm）,反演9-11月的土壤湿度,探讨土壤湿度时空分布特征。试验结果表明：①遥感影像反演的TVDI与实地考察的土壤湿度显著相关（a=0.05）;不同土层中,TVDIm与10~20 cm土层湿度相关性最高（R=0.588）;②焉耆盆地湿度总体以半干旱为主（0.60.8）;土壤湿度空间分布上,焉耆盆地南侧为干旱区,西部和北部地区偏干旱,中部为湿润区域,对于该地区滨湖湿地和博斯腾湖附近小湖土壤湿度最高,博斯腾湖南部的沙地区土壤湿度最低,Ts与土壤湿度呈负相关;③10月湿地的TVDIm值最低,9月沙地的TVDIm值最高。TVDI模型应用于焉耆盆地取得较好的结果,可用于正确地估算土壤湿度,研究结果可为焉耆盆地生态环境和水资源提供重要的参数。     

The refined spatiotemporal representation of soil organic matter based on remote images fusion of Sentinel-2 and Sentinel-3

Remote sensing technology is important for soil organic matter (SOM) estimation, but existing studies have mainly relied on a single data source. This limitation makes it difficult to simultaneously ensure high spatial resolution, high spectral accuracy and refined temporal granularity simultaneously, which cannot meet the requirements of the spatiotemporal dynamics representation. This study aimed to introduce a new remote sensing image source into SOM modeling and spatiotemporal estimation generated by fusing together Sentinel-2 and Sentinel-3 remote sensing images that have a 5-day revisit cycle; 10 m spatial resolution; and 21 different bands in blue, green, red and NIR spectral ranges. According to the image fusion process, a total of 52 available images were acquired between November 2016 and December 2018 in Donghai County, China. The fused images were used for SOM estimation model associated with 107 field samples. The results indicated that, first, the optimal model consisted of the band reflectivity (B20) and RVI (B18/B9), which were derived from the fused images, and the R² approached 0.7 in the two phases of the synchronized data. Second, the modeling accuracy was influenced to some extent by the actual SOM content. The R² values exceeded 0.75 when the SOM content was higher than 24 g/kg, while the R² was even lower than 0.35 when the SOM content was lower. Third, the averaged SOM contents remained stable in general, while the seasonal variances can also be found during the two-year interval. The SOM contents maintained a low level during autumn and winter, while higher SOM levels were found in the spring and summer. Finally, the spatial variations could be described as ‘low in the west and high in the east’. In summary, the spatiotemporal dynamics of SOM highlighted the necessity of modeling with fused remote sensing images, and more effective modeling could be expected with the continued increase in SOM in future.     

Sensitivity of spectral reflectance values to different burn and vegetation ratios: A multi-scale approach applied in a fire affected area

The aim of our study was to explore the spectral properties of fire-scorched (burned) and non fire-scorched (vegetation) areas, as well as areas with different burn/vegetation ratios, using a multisource multiresolution satellite data set. A case study was undertaken following a very destructive wildfire that occurred in Parnitha, Greece, July 2007, for which we acquired satellite images from LANDSAT, ASTER, and IKONOS. Additionally, we created spatially degraded satellite data over a range of coarser resolutions using resampling techniques. The panchromatic (1 m) and multispectral component (4 m) of IKONOS were merged using the Gram-Schmidt spectral sharpening method. This very high-resolution imagery served as the basis to estimate the cover percentage of burned areas, bare land and vegetation at pixel level, by applying the maximum likelihood classification algorithm. Finally, multiple linear regression models were fit to estimate each land-cover fraction as a function of surface reflectance values of the original and the spatially degraded satellite images.The main findings of our research were: (a) the Near Infrared (NIR) and Short-wave Infrared (SWIR) are the most important channels to estimate the percentage of burned area, whereas the NIR and red channels are the most important to estimate the percentage of vegetation in fire-affected areas; (b) when the bi-spectral space consists only of NIR and SWIR, then the NIR ground reflectance value plays a more significant role in estimating the percent of burned areas, and the SWIR appears to be more important in estimating the percent of vegetation; and (c) semi-burned areas comprising 45–55% burned area and 45–55% vegetation are spectrally closer to burned areas in the NIR channel, whereas those areas are spectrally closer to vegetation in the SWIR channel. These findings, at least partially, are attributed to the fact that: (i) completely burned pixels present low variance in the NIR and high variance in the SWIR, whereas the opposite is observed in completely vegetated areas where higher variance is observed in the NIR and lower variance in the SWIR, and (ii) bare land modifies the spectral signal of burned areas more than the spectral signal of vegetated areas in the NIR, while the opposite is observed in SWIR region of the spectrum where the bare land modifies the spectral signal of vegetation more than the burned areas because the bare land and the vegetation are spectrally more similar in the NIR, and the bare land and burned areas are spectrally more similar in the SWIR.     

Using high-resolution,multispectral imagery to assess the effect of soil properties on vegetation reflectance at an abandoned feedlot

Remotely sensed multispectral imagery, soils and graminoid samples from an abandoned cattle feedlot and adjacent wetlands were used to characterize plant vigour and soil nutrient distribution and evaluate the relationship between soil properties and vegetation reflectance. The feedlot lies on a sandy beach ridge, which likely mitigates the mobility of soil phosphorus. Soil phosphorus remains concentrated directly beneath the feedlot pens, where vegetation indices are low. In contrast, nitrate is transported through preferential pathways into the wetlands, where vegetation indices and plant vigour are high. Although spectral vegetation indices did not show any significant relationship with plant tissue nutrient concentration, the indices showed statistically significant relationships to some soil properties. Results of this study indicate that the abundance of nutrients in the soil does not necessarily enhance plant growth. This can limit the extent that remotely sensed vegetation indices can be used to evaluate soil nutrients concentrations.     

Considering combined or separated roughness and vegetation effects in soil moisture retrievals

For more than six years, the Soil Moisture and Ocean Salinity (SMOS) mission has provided multi angular and full-polarization brightness temperature (TB) measurements at L-band. Geophysical products such as soil moisture (SM) and vegetation optical depth at nadir (τ_nad) are retrieved by an operational algorithm using TB observations at different angles of incidence and polarizations. However, the quality of the retrievals depends on several surface effects, such as vegetation, soil roughness and texture, etc. In the microwave forward emission model used in the retrievals (L-band Microwave Emission Model, L-MEB), soil roughness is modelled with a semi-empirical equation using four main parameters (Q_r, H_r, N_rp, with p = H or V polarizations). At present, these parameters are calibrated with data provided by airborne studies and in situ measurements made at a local scale that is not necessarily representative of the large SMOS footprints (43 km on average) at global scale. In this study, we evaluate the impact of the calibrated values of N_rp and H_r on the SM and τ_nad retrievals based on SMOS TB measurements (SMOS Level 3 product) over the Soil Climate Analysis Network (SCAN) network located in North America over five years (2011–2015). In this study, Q_r was set equal to zero and we assumed that N_rH = N_rV. The retrievals were performed by varying N_rp from −1 to 2 by steps of 1 and H_r from 0 to 0.6 by steps of 0.1. At satellite scale, the results show that combining vegetation and roughness effects in a single parameter provides the best results in terms of soil moisture retrievals, as evaluated against the in situ SM data. Even though our retrieval approach was very simplified, as we did not account for pixel heterogeneity, the accuracy we obtained in the SM retrievals was almost systematically better than those of the Level 3 product. Improved results were also obtained in terms of optical depth retrievals. These new results may have key consequences in terms of calibration of roughness effects within the algorithms of the SMOS (ESA) and the SMAP (NASA) space missions.     

Integration of VNIR and SWIR spectral reflectance for mapping mineral resources; A case study,north east of Hajjah,Yemen

Laboratory reflectance spectra of 18 rock samples from the Precambrian basement of north east of Hajjah were measured and analyzed using the instrument of FieldSpec3 with spectral range 0.250–2.500 μm. The aim of this study is to use the spectral reflectance of rocks for mapping the mineral resources in the north east of Hajjah. The altered system in the study area comprises of silicification, sericitification, oxidation, clay minerals and carbonatization. Silicified alteration is not distinguishable in the regions of Visible-Near Infrared (VNIR) and Short wave Infrared (SWIR) of the electromagnetic spectrum, because of lack of diagnostic spectral absorption features in silica in this wavelength. Although the arsenopyrite and pyrite are wide spread in the whole study area their features do not appear in any range of spectra because they exhibit trans-opaque behavior and often lack distinction in VNIR and SWIR. The entire spectral reflectance curves of samples show alteration. Based on the examination of laboratory spectra all samples in the study area show promise in the field of mineral resources.     

Using multi-polarization C- and L-band synthetic aperture radar to estimate biomass and soil moisture of wheat fields

Biomass and soil moisture are two important parameters for agricultural crop monitoring and yield estimation. In this study, the Water Cloud Model (WCM) was coupled with the Ulaby soil moisture model to estimate both biomass and soil moisture for spring wheat fields in a test site in western Canada. This study exploited both C-band (RADARSAT-2) and L-band (UAVSAR) Synthetic Aperture Radars (SARs) for this purpose. The WCM-Ulaby model was calibrated for three polarizations (HH, VV and HV). Subsequently two of these three polarizations were used as inputs to an inversion procedure, to retrieve either soil moisture or biomass without the need for any ancillary data. The model was calibrated for total canopy biomass, the biomass of only the wheat heads, as well as for different wheat growth stages. This resulted in a calibrated WCM-Ulaby model for each sensor-polarization-phenology-biomass combination. Validation of model retrievals led to promising results. RADARSAT-2 (HH-HV) estimated total wheat biomass with root mean square (RMSE) and mean average (MAE) errors of 78.834 g/m² and 58.438 g/m²; soil moisture with errors of 0.078 m³/m³ (RMSE) and 0.065 m³/m³ (MAE) are reported. During the period of crop ripening, L-band estimates of soil moisture had accuracies of 0.064 m³/m³ (RMSE) and 0.057 m³/m³ (MAE). RADARSAT-2 (VV-HV) produced interesting results for retrieval of the biomass of the wheat heads. In this particular case, the biomass of the heads was estimated with accuracies of 38.757 g/m² (RSME) and 33.152 g/m² (MAE). For wider implementation this model will require additional data to strengthen the model accuracy and confirm estimation performance. Nevertheless this study encourages further research given the importance of wheat as a global commodity, the challenge of cloud cover in optical monitoring and the potential of direct estimation of the weight of heads where wheat production lies.     

遥感监测土壤湿度综述及其在新疆的应用展望

土壤湿度在全球水循环运动中扮演着非常重要的角色,是水文、气象和农业研究中的重要参数,国内外都极为重视对土壤湿度的研究。国外利用可见光、红外、热红外、微波遥感监测土壤水分已有三、四十年的历史,随着研究的深入和技术的发展,现已形成地面、航空、航天、多星的立体干旱遥感监测格局。国内遥感监测土壤湿度的方法主要有微波遥感、热红外遥感、距平植被指数法、植被供水指数、作物缺水指数等方法。本文通过对国内外已有的土壤湿度遥感监测方法的介绍和总结,对比分析了各种方法的原理、适用领域及其研究进展,并针对新疆的具体情况,认为借助Mod is影像进行新疆地区土壤湿度的监测是较为可行的一种方法。     

A comparison of ASCAT and SMOS soil moisture retrievals over Europe and Northern Africa from 2010 to 2013

A comparison between ASCAT/H-SAF and SMOS soil moisture products was performed in the frame of the EUMETSAT H-SAF project. The analysis was extended to the whole H-SAF region of interest, including Europe and North Africa, and the period between January 2010 and November 2013 was considered. Since SMOS and ASCAT soil moisture data are expressed in terms of absolute and relative values, respectively, different approaches were adopted to scale ASCAT data to use the same volumetric soil moisture unit. Effects of land cover, quality index filtering, season and geographical area on the matching between the two products were also analyzed. The two satellite retrievals were also compared with other independent datasets, namely the NCEP/NCAR volumetric soil moisture content reanalysis developed by NOAA and the ERA-Interim/Land soil moisture produced by ECMWF. In situ data, available through the International Soil Moisture Network, were also considered as benchmark. The results turned out to be influenced by the way ASCAT data was scaled. Correlation between the two products exceeded 0.6, while the root mean square difference did not decrease below 8%. ASCAT generally showed a fairly good degree of correlation with ERA, while, as expected considering the different kinds of measurement, the discrepancies with respect to local in situ data were large for both satellite products.     

Study on penetration depth and its dependence on frequency,soil moisture,texture and temperature in the context of microwave remote sensing

A theoretical study has been made to see the influence of microwave frequency, soil moisture, soil texture and soil temperature on penetration depth in the context of microwave remote sensing. The results are presented in the form of figures and also coefficients of least square fitting. The study reveals that there is a definite dependence of penetration depth on the above parameters. The range of penetration depth has been found to be 0 to 10 cms and varies as a function of several parameters. These results are in agreement with experimental observations.