Using spectral information from the NIR water absorption features for the retrieval of canopy water content

Canopy water content (CWC) is important for mapping and monitoring the condition of the terrestrial ecosystem. Spectral information related to the water absorption features at 970 nm and 1200 nm offers possibilities for deriving information on CWC. In this study, we compare the use of derivative spectra, spectral indices and continuum removal techniques for these regions. Hyperspectral reflectance data representing a range of canopies were simulated using the combined PROSPECT + SAILH model. Best results in estimating CWC were obtained by using spectral derivatives at the slopes of the 970 nm and 1200 nm water absorption features. Real data from two different test sites were analysed. Spectral information at both test sites was obtained with an ASD FieldSpec spectrometer, whereas at the second site HyMap airborne imaging spectrometer data were also acquired. Best results were obtained for the derivative spectra. In order to avoid the potential influence of atmospheric water vapour absorption bands the derivative of the reflectance on the right slope of the canopy water absorption feature at 970 nm can best be used for estimating CWC.     

Predicting grain protein content of winter wheat using remote sensing data based on nitrogen status and water stress

Advanced site-specific knowledge of grain protein content of winter wheat from remote sensing data would provide opportunities to manage grain harvest differently, and to maximize output by adjusting input in fields. In this study, remote sensing data were utilized to predict grain protein content. Firstly, the leaf nitrogen content at winter wheat anthesis stage was proved to be significantly correlated with grain protein content (R² = 0.36), and spectral indices significantly correlated to leaf nitrogen content at anthesis stage were potential indicators for grain protein content. The vegetation index, VI_green, derived from the canopy spectral reflectance at green and red bands, was significantly correlated to the leaf nitrogen content at anthesis stage, and also highly significantly correlated to the final grain protein content (R² = 0.46). Secondly, the external conditions, such as irrigation, fertilization and temperature, had important influence on grain quality. Water stress at grain filling stage can increase grain protein content, and leaf water content is closely related to irrigation levels, therefore, the spectral indices correlated to leaf water content can be potential indicators for grain protein content. The spectral reflectance of TM channel 5 derived from canopy spectra or image data at grain filling stage was all significantly correlated to grain protein content (R² = 0.31 and 0.37, respectively). Finally, not only this study proved the feasibility of using remote sensing data to predict grain protein content, but it also provided a tentative prediction of the grain protein content in Beijing area using the reflectance image of TM channel 5.     

Detection of stress in tomatoes induced by late blight disease in California, USA, using hyperspectral remote sensing

Large-scale farming of agricultural crops requires on-time detection of diseases for pest management. Hyperspectral remote sensing data taken from low-altitude flights usually have high spectral and spatial resolutions, which can be very useful in detecting stress in green vegetation. In this study, we used late blight in tomatoes to illustrate the capability of applying hyperspectral remote sensing to monitor crop disease in the field scale and to develop the methodologies for the purpose. A series of field experiments was conducted to collect the canopy spectral reflectance of tomato plants in a diseased tomato field in Salinas Valley of California. The disease severity varied from stage 1 (the light symptom), to stage 4 (the sever damage). The economic damage of the crop caused by the disease is around the disease stage 3. An airborne visible infrared imaging spectrometer (AVIRIS) image with 224 bands within the wavelength range of 0.4–2.5 μm was acquired during the growing season when the field data were collected. The spectral reflectance of the field samples indicated that the near infrared (NIR) region, especially 0.7–1.3 μm, was much more valuable than the visible range to detect crop disease. The difference of spectral reflectance in visible range between health plants and the infected ones at stage 3 was only 1.19%, while the difference in the NIR region was high, 10%. We developed an approach including the minimum noise fraction (MNF) transformation, multi-dimensional visualization, pure pixels endmember selection and spectral angle mapping (SAM) to process the hyperspectral image for identification of diseased tomato plants. The results of MNF transformation indicated that the first 28 eigenimages contain useful information for classification of the pixels and the rest were mainly noise-dominated due to their low eigenvalues that had few signals. Therefore, the 28 signal eigenimages were used to generate a multi-dimensional visualization space for endmember spectra selection and SAM. Classification with the SAM technique of plants’ spectra showed that the late blight diseased tomatoes at stage 3 or above could be separated from the healthy plants while the less infected plants (at stage 1 or 2) were difficult to separate from the healthy plants. The results of the image analysis were consistent with the field spectra. The mapped disease distribution at stage 3 or above from the image showed an accurate conformation of late blight occurrence in the field. This result not only confirmed the capability of hyperspectral remote sensing in detecting crop disease for precision disease management in the real world, but also demonstrated that the spectra-based classification approach is an applicable method to crop disease identification.     

Advances in spaceborne hyperspectral remote sensing in China

With the maturation of satellite technology,Hyperspectral Remote Sensing(HRS)platforms have developed from the initial ground-based and airborne platforms into spaceborne plat-forms,which greatly promotes the civil application of HRS imagery in the fields of agriculture,forestry,and environmental monitoring.China is playing an important role in this evolution,especially in recent years,with the successful launch and operation of a series of civil hyper-spectral spacecraft and satellites,including the Shenzhou-3 spacecraft,the Gaofen-5 satellite,the SPARK satellite,the Zhuhai-1 satellite network for environmental and resources monitoring,the FengYun series of satellites for meteorological observation,and the Chang'E series of spacecraft for planetary exploration.The Chinese spaceborne HRS platforms have various new characteristics,such as the wide swath width,high spatial resolution,wide spectral range,hyperspectral satellite networks,and microsatellites.This paper focuses on the recent progress in Chinese spaceborne HRS,from the aspects of the typical satellite systems,the data processing,and the applications.In addition,the future development trends of HRS in China are also discussed and analyzed.     

Remote sensing of water vapor content using ground-based GPS data

Spatial and temporal resolution of water vapor content is useful in improving the accuracy of short-term weather prediction. Dense and continuously tracking regional GPS arrays will play an important role in remote sensing atmospheric water vapor content. In this study, a piecewise linear solution method was proposed to estimate the precipitable water vapor (PWV) content from ground-based GPS observations in Hong Kong. To evaluate the solution accuracy of the water vapor content sensed by GPS, the upper air sounding data (radiosonde) that are collected locally was used to calculate the precipitable water vapor during the same period. One-month results of PWV from both ground-based GPS sensing technique and radiosonde method are in agreement within 1–2 mm. This encouraging result will motivate the GPS meteorology application based on the establishment of a dense GPS array in Hong Kong.     

Remote sensing of water vapor content using ground-based GPS data

Spatial and temporal resolution of water vapor content is useful in improving the accuracy of short-term weather prediction.Dense and continuously tracking regional GPS arrays will play an important role in remote sensing atmospheric water vapor content.In this study,a piecewise linear solution method was proposed to estimate the precipitable water vapor (PWV) content from ground-based GPS observations in Hong Kong.To evaluate the solution accuracy of the water vapor content sensed by GPS,the upper air sounding data (radiosonde) that are collected locally was used to calculate the precipitable water vapor during the same period.One-month results of PWV from both ground-based GPS sensing technique and radiosonde method are in agreement within 1～2 mm.This encouraging result will motivate the GPS meteorology application based on the establishment of a dense GPS array in Hong Kong.     

联合星载高光谱影像和堆栈集成学习回归算法的红树林冠层叶绿素含量遥感反演

红树林是世界上生产力最高、价值最高的湿地生态系统之一。冠层叶绿素含量CCC（Canopy Chlorophyll Content）作为红树林重要的生物物理参量,是估算其生产力和评价其健康状况的重要指标。本文利用珠海一号高光谱卫星（OHS）影像与Sentinel-2A多光谱数据计算传统植被指数与组合植被指数并构建了高维数据集,综合利用正态分布检验、最大相关系数法与变量重要性评价进行数据降维和变量优选;分别基于单一线性回归算法、机器学习回归算法和堆栈集成学习回归算法构建了红树林CCC遥感反演模型,探明北部湾红树林CCC的最佳遥感反演模型,验证OHS高光谱影像与Sentinel-2A数据反演红树林CCC的精度差异,评估SNAP-SL2P算法反演红树林CCC的适用性。研究结果表明：（1）通过数据降维和变量选择处理,从高维度OHS数据集选取了8个特征变量,其中RSI_（12,17）、DSI_（12,18）和NDSI_（6,12）组合植被指数对红树林CCC反演精度的贡献率较高;（2）联合OHS数据和最优堆栈GBRT集成学习回归模型（Score=0.999,RMSE=0.963 μg/cm²）的训练精度优于最优RF机器学习回归模型（RMSE降低了7.531 μg/cm²）,明显优于最优Lasso线性回归模型（RMSE降低了19.383 μg/cm²）;（3）在最优堆栈集成学习回归模型下,OHS数据反演红树林CCC的精度（R²=0.761,RMSE=16.738 μg/cm²）高于Sentinel-2A影像（R²=0.615,RMSE=20.701 μg/cm²）;（4）联合OHS和Sentinel-2A数据的最优堆栈集成学习回归模型反演红树林CCC的精度都明显优于SNAP-SL2P算法（R²=0.356,RMSE=49.419 μg/cm²）。研究结果论证了正态分布检验、最大相关系数法和基于XGBoost的特征选择方法有效降低了高维数据集的维度,并得到了最优特征变量;OHS数据的最优堆栈GBRT集成学习回归模型训练精度最高,是估算红树林CCC的最优反演模型;OHS和Sentinel-2A数据都能有效反演红树林CCC（R²均大于0.61）,而OHS数据的估算精度更高（R²大于0.75）;SNAP-SL2P算法不能有效反演红树林CCC（R²小于0.4）,且对红树林CCC数值存在系统性低估。     

Estimation of forest leaf water content through inversion of a radiative transfer model from LiDAR and hyperspectral data

The accurate estimation of leaf water content (LWC) and knowledge about its spatial variation are important for forest and agricultural management since LWC provides key information for evaluating plant physiology. Hyperspectral data have been widely used to estimate LWC. However, the canopy reflectance can be affected by canopy structure, thereby introducing error to the retrieval of LWC from hyperspectral data alone. Radiative transfer models (RTM) provide a robust approach to combine LiDAR and hyperspectral data in order to address the confounding effects caused by the variation of canopy structure. In this study, the INFORM model was adjusted to retrieve LWC from airborne hyperspectral and LiDAR data. Two structural parameters (i.e. stem density and crown diameter) in the input of the INFORM model that affect canopy reflectance most were replaced by canopy cover which could be directly obtained from LiDAR data. The LiDAR-derived canopy cover was used to constrain in the inversion procedure to alleviate the ill-posed problem. The models were validated against field measurements obtained from 26 forest plots and then used to map LWC in the southern part of the Bavarian Forest National Park in Germany. The results show that with the introduction of prior information of canopy cover obtained from LiDAR data, LWC could be retrieved with a good accuracy (R² = 0.87, RMSE = 0.0022 g/cm², nRMSE = 0.13). The adjustment of the INFORM model facilitated the introduction of prior information over a large extent, as the estimation of canopy cover can be achieved from airborne LiDAR data.     

Estimating chlorophyll content of crops from hyperspectral data using a normalized area over reflectance curve (NAOC)

The Normalized Area Over reflectance Curve (NAOC) is proposed as a new index for remote sensing estimation of the leaf chlorophyll content of heterogeneous areas with different crops, different canopies and different types of bare soil. This index is based on the calculation of the area over the reflectance curve obtained by high spectral resolution reflectance measurements, determined, from the integral of the red–near-infrared interval, divided by the maximum reflectance in that spectral region. For this, use has been made of the experimental data of the SPARC campaigns, where in situ measurements were made of leaf chlorophyll content, LAI and fCOVER of 9 different crops – thus, yielding 300 different values with broad variability of these biophysical parameters. In addition, Proba/CHRIS hyperspectral images were obtained simultaneously to the ground measurements. By comparing the spectra of each pixel with its experimental leaf chlorophyll value, the NAOC was proven to exhibit a linear correlation to chlorophyll content. Calculating the correlation between these variables in the 600–800 nm interval, the best correlation was obtained by computing the integral of the spectral reflectance curve between 643 and 795 nm, which practically covers the spectral range of maximum chlorophyll absorption (at around 670 nm) and maximum leaf reflectance in the infrared (750–800 nm). Based on a Proba/CHRIS image, a chlorophyll map was generated using NAOC and compared with the land-use (crops classification) map. The method yielded a leaf chlorophyll content map of the study area, comprising a large heterogeneous zone. An analysis was made to determine whether the method also serves to estimate the total chlorophyll content of a canopy, multiplying the leaf chlorophyll content by the LAI. To validate the method, use was made of the data from another campaign ((SEN2FLEX), in which measurements were made of different biophysical parameters of 7 crops, and hyperspectral images were obtained with the CASI imaging radiometer from an aircraft. Applying the method to a CASI image, a map of leaf chlorophyll content was obtained, which on, establishing comparisons with the experimental data allowed us to estimate chlorophyll with a root mean square error of 4.2 μg/cm², similar or smaller than other methods but with the improvement of applicability to a large set of different crop types.     

Novel Folded-PCA for improved feature extraction and data reduction with hyperspectral imaging and SAR in remote sensing

As a widely used approach for feature extraction and data reduction, Principal Components Analysis (PCA) suffers from high computational cost, large memory requirement and low efficacy in dealing with large dimensional datasets such as Hyperspectral Imaging (HSI). Consequently, a novel Folded-PCA is proposed, where the spectral vector is folded into a matrix to allow the covariance matrix to be determined more efficiently. With this matrix-based representation, both global and local structures are extracted to provide additional information for data classification. Moreover, both the computational cost and the memory requirement have been significantly reduced. Using Support Vector Machine (SVM) for classification on two well-known HSI datasets and one Synthetic Aperture Radar (SAR) dataset in remote sensing, quantitative results are generated for objective evaluations. Comprehensive results have indicated that the proposed Folded-PCA approach not only outperforms the conventional PCA but also the baseline approach where the whole feature sets are used.     

A novel approach to estimate canopy height using ICESat/GLAS data: A case study in the New Forest National Park,UK

The Geoscience Laser Altimeter System (GLAS) aboard Ice, Cloud and land Elevation Satellite (ICESat) is a spaceborne LiDAR sensor. It is the first LiDAR instrument which can digitize the backscattered waveform and offer near global coverage. Among others, scientific objectives of the mission include precise measurement of vegetation canopy heights. Existing approaches of waveform processing for canopy height estimation suggest Gaussian decomposition of the waveform which has the limitation to properly characterize significant peaks and results in discrepant information. Moreover, in most cases, Digital Terrain Models (DTMs) are required for canopy height estimation. This paper presents a new automated method of GLAS waveform processing for extracting vegetation canopy height in the absence of a DTM. Canopy heights retrieved from GLAS waveforms were validated with field measured heights. The newly proposed method was able to explain 79% of variation in canopy heights with an RMSE of 3.18 m, in the study area. The unexplained variation in canopy heights retrieved from GLAS data can be due to errors introduced by footprint eccentricity, decay of energy between emitted and received signals, uncertainty in the field measurements and limited number of sampled footprints.Results achieved with the newly proposed method were encouraging and demonstrated its potential of processing full-waveform LiDAR data for estimating forest canopy height. The study also had implications on future full-waveform spaceborne missions and their utility in vegetation studies.     

Determination of chlorophyll content of small water bodies (kettle holes) using hyperspectral airborne data

This study presents an approach for chlorophyll content determination of small shallow water bodies (kettle holes) from hyperspectral airborne ROSIS and HyMap data (acquired on 15 May and 29 July 2008 respectively). Investigated field and airborne spectra for almost all kettle holes do not correspond to each other due to differences in ground sampling distance. Field spectra were collected from the height of 30–35 cm (i.e. area of 0.01–0.015 m²). Airborne pixels of ROSIS and HyMap imageries cover an area of 4 m² and 16 m² respectively and their spectra are highly influenced by algae or bottom properties of the kettle holes. Analysis of airborne spectra revealed that chlorophyll absorption near 677 nm is the same for both datasets. In order to enhance absorption properties, both airborne hyperspectral datasets were normalized by the continuum removal approach. Linear regression algorithms for ROSIS and HyMap datasets were derived using normalized average chlorophyll absorption spectra for each kettle hole. Overall accuracy of biomass mapping for ROSIS data was 71%, and for HyMap 64%. Biomass mapping results showed that, depending on the type of kettle hole, algae distribution, the ‘packaging effect’ and bottom reflection lead to miscalculations of the chlorophyll content using hyperspectral airborne data.     

联合PROSAIL模型和植被水分指数的低矮植被含水量估算

植被冠层含水量CWC(Canopy Water Content)和植被地上部分含水量VWC(Vegetation Water Content)对于植被健康状况和土壤干旱监测具有重要意义.本文联合PROSAIL辐射传输模型和植被水分指数NDWI(Normalized Difference Water Index),发展了...     

Estimating fractional cover of crop,crop residue,and soil in cropland using broadband remote sensing data and machine learning

The fractional vegetation cover (FVC), crop residue cover (CRC), and bare soil (BS) are three important parameters in vegetation–soil ecosystems, and their correct and timely estimation can improve crop monitoring and environmental monitoring. The triangular space method uses one CRC index and one vegetation index to create a triangular space in which the three vertices represent pure vegetation, crop residue, and bare soil. Subsequently, the CRC, FVC, and BS of mixed remote sensing pixels can be distinguished by their spatial locations in the triangular space. However, soil moisture and crop-residue moisture (SM-CRM) significantly reduce the performance of broadband remote sensing CRC indices and can thus decrease the accuracy of the remote estimation and mapping of CRC, FVC, and BS. This study evaluated the use of broadband remote sensing, the triangular space method, and the random forest (RF) technique to estimate and map the FVC, CRC, and BS of cropland in which SM-CRM changes dramatically. A spectral dataset was obtained using: (1) from a field-based experiment with a field spectrometer; and (2) from a laboratory-based simulation that included four distinct soil types, three types of crop residue (winter-wheat, maize, and rice), one crop (winter wheat), and varying SM-CRM. We trained an RF model [designated the broadband crop-residue index from random forest (CRRF)] that can magnify spectral features of crop residue and soil by using the broadband remote sensing angle indices as input, and uses a moisture-resistant hyperspectral index as the target. The effects of moisture on crop residue and soil were minimized by using the broadband CRRF. Then, the CRRF-NDVI triangular space method was used to estimate and map CRC, FVC, and BS. Our method was validated by using both laboratory- and field-based experiments and Sentinel-2 broadband remote-sensing images. Our results indicate that the CRRF-NDVI triangular space method can reduce the effect of moisture on the broadband remote-sensing of CRC, and may also help to obtain laboratory and field CRC, FVC, and BS. Thus, the proposed method has great potential for application to croplands in which the SM-CRM content changes dramatically.     

Evaluating the feasibility of multitemporal hyperspectral remote sensing for monitoring bioremediation

In recent years, several studies focused on the detection of hydrocarbon pollution in the environment using hyperspectral remote sensing. Particularly the indirect detection of hydrocarbon pollution, using vegetation reflectance in the red edge region, has been studied extensively. Bioremediation is one of the methods that can be applied to clean up polluted sites. So far, there have been no studies on monitoring of bioremediation using (hyperspectral) remote sensing. This study evaluates the feasibility of hyperspectral remote sensing for monitoring the effect of bioremediation over time. Benzene leakage at connection points along a pipeline was monitored by comparing the red edge position (REP) in 2005 and 2008 using HyMap airborne hyperspectral images. REP values were normalized in order to enhance local variations caused by a change in benzene concentrations. 11 out of 17 locations were classified correctly as remediated, still polluted, or still clean, with a total accuracy of 65%. When only polluted locations that were remediated were taken into account, the (user's) accuracy was 71%.     

A hyperspectral index sensitive to subtle changes in the canopy chlorophyll content under arsenic stress

Arsenic stress induces in subtle changes in the canopy chlorophyll content (CCC). Therefore, the establishment of a spectral index that is sensitive to subtle changes in the CCC is important for monitoring crop arsenic contamination in large areas by remote sensing. Experimental sites with three contamination levels were selected and were located in Chang Chun City, Jilin City, Jilin Province, China. Arsenic stress can induce small changes in the CCC, reflecting in the crop spectrum. This study created a new index to monitor the CCC. Then, the results from the index were compared with these from other indices and the random forest model, respectively. The final purpose of this study is to find an optimal index, which is sensitive to small changes in the CCC under arsenic stress for monitoring regional CCC in rice. The results indicate that the distribution of the CCC is aligned with the distribution of the arsenic stress level and that NVI (R₆₄₀, R₇₃₂, and R₇₅₂) is the best index for monitoring CCC. The correlation coefficient R² between the predicated values using NVI and the measured values of canopy chlorophyll content is 0.898, which performs better than the random forest model and other indices.     

Non-destructive estimation of potato leaf chlorophyll from canopy hyperspectral reflectance using the inverted PROSAIL model

Optimizing nitrogen (N) fertilization in crop production by in-season measurements of crop N status may improve fertilizer N use efficiency. Hyperspectral measurements may be used to assess crop N status by estimating leaf chlorophyll content. This study evaluated the ability of the PROSAIL canopy-level reflectance model to predict leaf chlorophyll content. Trials were conducted with two potato cultivars under different N fertility rates (0–300 kg N ha⁻¹). Canopy reflectance, leaf area index (LAI) and leaf chlorophyll and N contents were measured. The PROSAIL model was able to predict leaf chlorophyll content with reasonable accuracy later in the growing season. The low estimation accuracy earlier in the growing season could be due to model sensitivity to non-homogenous canopy architecture and soil background interference before full canopy closure. Canopy chlorophyll content (leaf chlorophyll content × LAI) was predicted less accurately than leaf chlrophyll content due to the low estimation accuracy of LAI for values higher than 4.5.     

Estimating the parameters of forest inventory using machine learning and the reduction of remote sensing features

Locally computed statistics of image texture and a case-based reasoning (CBR) system were evaluated for mapping of forest attributes. Cluster analysis was preferred to regression models, as a pre-selection method of features. The best stand-based accuracy using satellite sensor images was 74.64 m⁻³ ha⁻¹ (36%) RMSE for stand volume, 1.98 m⁻³ ha⁻¹ a⁻¹ (49%) for annual increase in stand volume, where κ = 0.23 for stand growth classes and κ = 0.41 for dominant tree species in stands. The top pixel-based accuracy using orthophotos was 76.54 m⁻³ ha⁻¹ (41%) RMSE for stand volume, 1.87 m⁻³ ha⁻¹ a⁻¹ (44%) for annual increase in stand volume, where κ = 0.24 for stand growth classes and κ = 0.38 for dominant tree species in stands. Mean saturation in 30 m radius was the most useful feature when orthophotos were used, and standard deviation of Landsat ETM 6.2 values in 80 m radius was the best when satellite sensor images were used. The most valuable feature components (radii, channels and local statistics) for orthophotos were: 30 m kernel radius, lightness and the mean of pixel values; for satellite sensor images: 80 m kernel radius, near-infrared channel (ETM 4) and the mean of pixel values. Locally computed statistics.     

结合NSCT和KPCA的高光谱遥感图像去噪

针对高光谱遥感图像易受噪声干扰,本文提出了一种基于非下采样Contourlet变换NSCT(Nonsubsampled Contourlet Transform)和核主成分分析KPCA(Kernel Principal Component Analysis)的去噪方法。首先对高光谱各波段图像进行NSCT分解;然后利用KPCA对NSCT系数进行处理,并在KPCA重构时依据各类噪声的特性选取合适的主成分;最后用处理过的系数进行逆变换得到去噪图像。实验结果表明,本文方法抑制了高光谱遥感图像中的噪声干扰,较完整地保留了原始数据的有效信息。     

Estimating dissolved organic carbon concentration in turbid coastal waters using optical remote sensing observations

Dissolved Organic Carbon (DOC) is an important component in the global carbon cycle. It also plays an important role in influencing the coastal ocean biogeochemical (BGC) cycles and light environment. Studies focussing on DOC dynamics in coastal waters are data constrained due to the high costs associated with in situ water sampling campaigns. Satellite optical remote sensing has the potential to provide continuous, cost-effective DOC estimates. In this study we used a bio-optics dataset collected in turbid coastal waters of Moreton Bay (MB), Australia, during 2011 to develop a remote sensing algorithm to estimate DOC. This dataset includes data from flood and non-flood conditions. In MB, DOC concentration varied over a wide range (20–520 μM C) and had a good correlation (R² = 0.78) with absorption due to coloured dissolved organic matter (CDOM) and remote sensing reflectance. Using this data set we developed an empirical algorithm to derive DOC concentrations from the ratio of R_rs(412)/R_rs(488) and tested it with independent datasets. In this study, we demonstrate the ability to estimate DOC using remotely sensed optical observations in turbid coastal waters.