MODerate-resolution Imaging Spectroradiometer (MODIS) vegetation index (VI) time-series data are among the most widely utilized remote sensing datasets. To improve the quality of MODIS VI time-series data, most prior methods have focused on correcting negatively biased VI noise by approaching the upper envelope of the VI time series. Such treatment, however, may cause overcorrections on some true local low VI values, resulting in inaccurate simulations of vegetation phenological characteristics. In addition, another challenge in reconstructing MODIS VI time series is to fill temporally continuous gaps. The earlier spatiotemporal Savitzky-Golay (STSG) method tackled this problem by utilizing multiyear VI data, but its performance heavily relies on the consistency of data across different years. In this study, we proposed an improved STSG (iSTSG) method. The new method accounts for the autocorrelation within the VI time series and fills missing values in the VI time series by leveraging spatiotemporal VI data from the current year alone. Furthermore, iSTSG incorporates an indicator to quantify potential overcorrections in the VI time series, aiming to more accurately simulate phenological characteristics. The experiments to reconstruct MODIS normalized difference VI (NDVI) time-series product (MOD13A2) at four typical sites (a million square kilometers for each site) suggest two clear advantages in iSTSG over the iterative SG (called Chen-SG) and STSG methods. First, iSTSG more accurately reconstructs the annual NDVI time series, exhibiting the smallest mean absolute differences (MADs) between the smoothed and the simulated reference NDVI time series (0.012, 0.018, and 0.020 for iSTSG, STSG, and Chen-SG, respectively). Second, iSTSG more effectively simulates phenological characteristics in the NDVI time series, including the onset dates for vegetation greenup and dormancy, as well as the crop harvest period. The advantages of iSTSG were also demonstrated when applied to the successor of MODIS, Visible Infrared Imaging Radiometer Suite (VIIRS) VI time-series product (VNP13A1). iSTSG can be implemented on the Google Earth Engine (GEE), offering significant benefits for various applications, particularly in crop mapping and vegetation/crop phenology studies.

Restricted by the design of satellite sensors, the existing satellite-based Normalized Difference Vegetation Index (NDVI) cannot simultaneously have a high temporal resolution and spatial resolution, which substantially limits its applications. In recent years, several spatiotemporal fusion models have been developed to produce vegetation index datasets with both high spatial and temporal resolutions, but large uncertainties remain. This study proposes a spatiotemporal fusion model (i.e., Integrating ENvironmental VarIable spatiotemporal fusion model, InENVI) based on a machine-learning method by incorporating environmental variables to reconstruct NDVI data. Over 14 study areas covering various vegetation types globally, the InENVI method was validated for reproducing spatiotemporal variations in NDVI. On average, the determining coefficients (R2) of the reconstructed NDVI compared with satellite-based NDVI observations were above 0.90, reflecting the spatiotemporal variations over all study sites. In addition, we compared the performance of the InENVI model with seven other fusion models over two cropland areas with high vegetation heterogeneity. The results showed the newly developed InENVI method had the best performance, and the reconstruction error of the InENVI method decreased about 23.68-59.63% on average over two study areas compared to the other seven methods. Our analyses also highlighted that the integration of environmental variables into spatiotemporal fusion is necessary to improve reconstruction accuracy. The InENVI model provides an alternative approach for reconstructing NDVI datasets with both high spatial and temporal resolutions over large areas.

Hyperspectral imaging (HSI) effectively supports land cover (LC) classification in understanding nature-human interactions. The LC classification quality depends heavily on the input features. Combining convolutional neural network (CNN) and stacked spectral feature space patch (SSFSP) has proved effective in simultaneously exploiting spatial and spectral features for accurate LC classification. SSFSP is a stack of gridded spectral feature space images (GSIs) where the spatial and intensity distributions of LCs are derived and directly learned by CNN. However, the GSI set was not fully optimized for HSI classification. In this paper, we propose an improved SSFSP (ISSFSP) to tackle this problem, by integrating SSFSP with a novel feature selection method called band combination search (BCS). BCS first defines a novel metric to measure the ability of a GSI to distinguish various LCs, and then uses a coarse-to-fine strategy to efficiently identify the optimal GSI set. Furthermore, BCS provides a classifier-free method for determining the appropriate number of GSIs after trading off accuracy and efficiency. We conducted experiments using three HSI datasets, ISSFSP and five state-of-the-art feature selection methods, and six classification models to validate the effectiveness of ISSFSP. ISSFSP obtained consistently highest accuracy (over 98% overall accuracy, 97% average accuracy, and 97% Kappa) using fewer bands and samples, indicating that ISSFSP significantly boosts the performance of CNN while reducing the dependence on model structure and sample quality. In short, ISSFSP can automatically provide promising spectral features for CNN-based HSI classification regarding classification accuracy and model independency, showing great potential for practical applications.

Nighttime light (NTL) products have become emerging instruments for studying human activity patterns in various applications, including demarcating urban areas, estimating the residential population, and monitoring the economic livelihood of cities. However, the deployment of such products, particularly the Defense Meteorological Satellite Program (DMSP)/Operational Line Scan (OLS) stable NTL data, is also subject to issues of data quality and methodological biases, including interannual inconsistency, saturation and blooming effect, and, in particular, the saturation problem, posing a challenge for further applications. This study proposed a novel saturation correction method based on regression model and radiance-calibrated NTL data (SARMRC) to correct the saturation effect in annual stable NTL data. The proposed method was applied to mainland China. The results show that SARMRC can effectively recover the light intensity distribution within the saturated areas of the stable NTL data. The corrected images were proven to be more spatially consistent with the reference National Polar-orbiting Partnership (NPP)/Visible Infrared Imaging Radiometer Suite (VIIRS) data and can better reflect the temporal development of cities compared to existing saturation correction methods (e.g., the index-based vegetation adjusted NTL urban index (VANUI) method and the interpolation method). The superior performance of SARMRC can be attributed to: 1) better utilization of both radiance-calibrated and stable NTL products and 2) the employment of training pixel selection and logarithmic model as well as double-year adjustment. The new method is expected to improve the data quality of DMSP/OLS stable NTL data for analyzing both local and regional socioeconomic activities.

Spatiotemporal fusion (STF) aims at generating remote-sensing data with both high spatial and temporal resolution. In the literature, one of the most widely used strategies to accomplish this goal is to fuse high temporal resolution images collected by the Moderate Resolution Imaging Spectroradiometer (MODIS) with images with finer spatial resolution than those provided by MODIS (e.g., those collected by other satellite instruments such as Landsat or Sentinel-2). Current STF methods generally fuse an upsampled MODIS image with finer spatial resolution images. This leads to two main problems. First of all, the model uncertainty errors (resulting from the ill-posed upsampling problem) will be propagated into the fusion results, leading to spatial and spectral distortion. Furthermore, the spatial details of the upsampled MODIS image may be significantly different from those of the finer spatial resolution images, making the STF problem even more challenging. In order to tackle these issues, in this work, we develop a new linear regression-based STF strategy (LiSTF), which performs the reconstruction from a MODIS-like image (instead of from an upsampled MODIS image), thus reducing the model uncertainty errors and preserving better the spatial information. The MODIS-like images are built from the finer spatial resolution images via downsampling. Our experimental results, conducted using two publicly available datasets of Landsat–MODIS image pairs and one publicly available dataset of Sentinel–MODIS image pairs, reveal that our newly proposed LiSTF approach can significantly enhance the quantitative and qualitative performance of STF, particularly in terms of preserving the spatial information.

Root water content (RWC) is a vital component in water flux in soil–plant–atmosphere continuum. Knowledge of RWC helps to better understand the root function and the soil–root interaction and improves water cycle modeling. However, due to the lack of appropriate methods, field monitoring of RWC is seriously constrained. In this study, we used ground-penetrating radar (GPR), a common geophysical technique, to characterize RWC of coarse roots noninvasively. An automatic GPR data processing framework was proposed to (1) identify hyperbolic root reflections and locate roots in GPR images and (2) extract waveform parameters from the reflected wave of identified roots. These waveform parameters were then used to establish an empirical model and a semiempirical model to determine RWC. We validated the developed models using GPR root data at three antenna center frequencies (500 MHz, 900 MHz, and 2 GHz) that were produced from simulation experiments (with RWC ranging from 70% to 150%) and field experiments in sandy soils (with RWC ranging from 66% to 144%). Our results show that both the empirical and the semiempirical models achieved a good performance in estimating RWC with similar accuracy, i.e., the prediction error [root-mean-square error (RMSE)] was less than 8% for the simulation data and 12% for the field data. For both models, the accuracy of RWC estimation was the highest when applied to 2-GHz data. This study renders a new opportunity to determine RWC under field conditions that enhances the application of GPR for root study and the understanding and modeling of ecohydrology in the rhizosphere.

Deep semantic segmentation networks perform better in cloud detection of satellite imagery than traditional methods due to their ability to extract high-level features over a large receptive field. However, a large receptive field often leads to loss of spatial details and blurring of boundaries. Therefore, it is crucial to understand the role of the receptive field on the segmentation results, which has rarely been investigated for cloud detection tasks. This study, for the first time, explored the relationship between the receptive field size and the performance of a cloud detection network. Six typical networks commonly used for cloud detection and nine modified UNet variants with different depths, dilated convolutions, and skip connections were evaluated based on the Landsat 8 Biome (L8 Biome) dataset. The theoretical receptive field (TRF) and the effective receptive field (ERF) were introduced to measure the receptive field sizes of different networks. The results revealed a negative correlation between the ERF size and cloud segmentation accuracies for different cloud distributions and a relatively weak negative correlation between the TRF size and segmentation accuracies. Furthermore, ERFs were considerably smaller than the corresponding TRFs for most networks, implying that large-scale contextual information was not learned after training. This result indicates the importance of using networks with a small receptive field for cloud detection of Landsat 8 OLI imagery. Moreover, as the boundary accuracies are significantly lower than the region accuracies, future efforts should be devoted to addressing inaccurate boundary localization rather than exploring the contextual information over a large receptive field.

Spatiotemporal data fusion is a methodology to generate images with both high spatial and temporal resolution. Most spatiotemporal data fusion methods generate the fused image at a prediction date based on pairs of input images from other dates. The performance of spatiotemporal data fusion is greatly affected by the selection of the input image pair. There are two criteria for selecting the input image pair: the “similarity” criterion, in which the image at the base date should be as similar as possible to that at the prediction date, and the “consistency” criterion, in which the coarse and fine images at the base date should be consistent in terms of their radiometric characteristics and imaging geometry. Unfortunately, the “consistency” criterion has not been quantitatively considered by previous selection strategies. We thus develop a novel method (called “cross-fusion”) to address the issue of the determination of the base image pair. The new method first chooses several candidate input image pairs according to the “similarity” criterion and then takes the “consistency” criterion into account by employing all of the candidate input image pairs to implement spatiotemporal data fusion between them. We applied the new method to MODIS-Landsat Normalized Difference Vegetation Index (NDVI) data fusion. The results show that the cross-fusion method performs better than four other selection strategies, with lower average absolute difference (AAD) values and higher correlation coefficients in various vegetated regions including a deciduous forest in Northeast China, an evergreen forest in South China, cropland in North China Plain, and grassland in the Tibetan Plateau. We simulated scenarios for the inconsistency between MODIS and Landsat data and found that the simulated inconsistency is successfully quantified by the new method. In addition, the cross-fusion method is less affected by cloud omission errors. The fused NDVI time-series data generated by the new method tracked various vegetation growth trajectories better than previous selection strategies. We expect that the cross-fusion method can advance practical applications of spatiotemporal data fusion technology.

Crop phenology extraction using remote sensing is influenced by satellite data and retrieval method. We conducted a comprehensive evaluation of the accuracy for winter wheat green-up date (GUD) extraction with four VIs (NDVI, EVI, EVI2 and NDPI) and six methods (CCRmax, βmax, RCmax, RT10, RT20 and RT50), comparing with ground-observed GUD in China. We evaluated the performance with correlation coefficient (R), root mean square error (RMSE), and observations (BIAS). The results showed that GUDs extracted from NDPI time series presented the overall highest accuracy. Based on NDPI time series, we also found that GUDs extracted with CCRmaxmethod had the highest consistency with ground-observed GUDs. Our finding suggested caution on the uncertainties in phenology extraction with remote sensing methods.

In recent years, nighttime light (NTL) data has been widely used in the urban associated researches thanks to its ability in characterizing human's activity. NTL data has an advantage on detecting urban areas and reflecting economic conditions. However, NTL data has two main problems: saturation and blooming effect. These two problems will reduce the accuracy of urban detection and social-economic indicators estimations. There have been increasing attentions on saturation effect and correction although new version of VIIRS data has significantly alleviated the saturation effect. While for blooming effect, few researches is existing. Most researches about blooming effect are still qualitative rather than quantitative. This paper takes China as study area to measure blooming distance of NTL data and explore the contributions of three main factors on blooming effect.

Vegetation phenology has an important impact on ecosystem processes and biosphere-atmosphere feedbacks, which is also highly sensitive to the climate change. The observation and extraction of Phenology require the highest possible temporal resolution and spatial resolution. Digital camera photographs can supplement long-term observations, which meet the above conditions. The usual treatment for digital repeat RGB-cameras is to compute compared greenness chromatic coordinate (GCC) series representing the state of vegetation growth. In order to eliminate the effect of solar angle and atmospheric scattering on GCC series, we used the histogram features of the R, G, and B band of each photo to obtain a stable GCC time series through multiple linear regression. The DART model was used to simulate winter wheat scenes under different light and atmospheric conditions. Simulation experiments show that this method can eliminate the instability caused by atmospheric scattering, but the correction of the solar elevation angle is not obvious.

Soil moisture is generally considered to be a major limiting factor for root growth of shrubs in the arid and semi-arid regions. However, as a typical shrub species, the correlation between root distribution and soil moisture of Caragana microphylla has not been well investigated. Ground-penetrating radar (GPR) has the advantage of acquiring locations of roots quickly and simply, therefore, we used GPR to acquire locations of roots in the quadrat of 30m*30m, and soil moisture data was acquired by soil drilling method. After processing data, we analyzed the correlation between root density and soil moisture in vertical and horizontal directions. Following conclusions were obtained through vertical and horizontal analysis: In the vertical direction, root density was positively correlated with soil moisture, but the soil moisture was lower in the dense absorbing roots' distribution region, even the preferential flow could enrich soil moisture. Root density and soil moisture also had a positive correlation in horizontal direction, but they were inversely related in the region of dense absorbing roots.

Due to its in situ and nondestructive nature, ground-penetrating radar (GPR) has recently been applied to the field investigation of plant roots. The discrepancy between the roots and surrounding soils creates a dielectric constant contrast, forming clear hyperbolic reflections on the GPR radargram. The intensity and shape of the reflecting signals from roots are substantially affected by the root orientation as well as the relative geometry between the root in the subsurface and the GPR survey direction on the ground surface. However, no previous study has utilized the information on the intensity and shape of a root's GPR reflection to map its orientation, which is crucial in interpreting radargrams and rebuilding 3-D root system architecture. In this paper, a mathematical formulation of hyperbolic reflection formed by a single root was first deduced based on the principles of electromagnetic wave propagation. Then, using this formulation, curve fitting was conducted on both simulated and field collected data sets by GPR. Information on the horizontal orientation and vertical inclination of a single root was acquired according to the formulation coefficient retrievals. Conditions for this method of application and factors impacting the extraction of root orientation information were analyzed. The results indicated fairly precise root orientation estimations. The proposed method has extended the application of GPR in root investigation, thus advancing the frontier of noninvasive root system architecture mapping.

Remote estimation of fractional vegetation cover (FVC) in arid and semiarid areas is crucial for understanding their roles in global climate changes and maintaining their ecological sustainability. Among the existing algorithms for remote estimation of FVC, the linear spectral mixture analysis (LSMA) has been widely adopted owing to its simplicity and flexibility. However, the spectral variability of endmembers is still a big challenge that would largely decrease the estimation accuracy of LSMA. In this letter, we proposed a novel unmixing algorithm by integrating an orthogonal Fisher transformation into the LSMA (fLSMA). Two evaluation experiments were conducted: one was based on simulations; the other was based on a field survey in Xilingol grassland, China. The proposed fLSMA yielded remarkably higher accuracies and precisions than the conventional LSMA (cLSMA), weighted SMA (wSMA) in the first experiment. In the second experiment, a root-mean-square error (RMSE) of 0.11 was derived for the fLSMA, compared with the RMSE values larger than 0.36 for the cLSMA and wSMA. Although the performance of fLSMA was somehow similar to the multiple endmember SMA (MESMA) in the two evaluation experiments, the fLSMA was much less time-consuming than the MESMA in massive computations. The results indicate the potential of the proposed fLSMA in long-term monitoring of FVC in semiarid areas based on satellite observations.

Cloud removal is significantly needed for enhancing the further utilization of Landsat imagery, since such optical remote sensing satellite images are inevitably contaminated by clouds. Clouds dynamically affect the signal transmission due to their different shapes, heights, and distribution. Generally, pixel replacement is the only and common method used to remove thick opaque clouds, and radiometric correction techniques has been widely adopted to remove the thin clouds. However, no methods can remove both thick and thin clouds at the same time. In this paper, a new method is proposed based on fitting “trajectory” of cloudy pixels with the help of IHOT spatially charactering clouds for pixel correction, which considers signal transmission including not only the additive reflectance from the clouds but also the energy attenuation when solar radiation passes through them. The experimental results show that the proposed approach performs effective removal for thick and thin clouds, and possesses the highest accuracy with the reference image, which can restore land cover information accurately.

Endmember variability associated with impervious layer has been a serious problem in spectral mixture analysis (SMA). A reliable spectral library which ideally models the endmember variability is required for precise SMA. Even though many endmember bundles extraction algorithms have been proposed, there are still some problems in these methods which blur the threshold and endmember numbers. In this paper, an iterative classification extraction endmember bundles algorithm (ICEEA) is proposed. Impervious and pervious training sample are provided with GlobeLand30 product, and Maximum Likelihood Classifier (MLC) is used to conduct iteratively classification. After each classification, the artificial layer pixels which are misclassified as pervious class are excluded from artificial cover, and the impervious sample is selected again in new artificial cover. It stops when there are none misclassified pixels existing in the artificial layer. According to the results of simulated 30m data and real TM data, ICEEA has two advantages over PPI: (1) producing more reliable impervious endmember bundles which can model the endmember variability well; (2) having none threshold setting problem; (2) running much faster than PPI.

Most previous haze/cloud detection methods for Landsat imagery, e.g., haze optimized transformation (HOT), cannot adequately suppress land surface information and, in particular, often overestimate haze thickness over bright surfaces. This paper proposes an iterative HOT (IHOT) for improving haze detection with the help of a corresponding clear image. With an iterative procedure of regressions among HOT, the reflectance difference at the top of atmosphere (TOA) between hazy and clear images, and TOA reflectances of hazy and clear images, the land surface information can be removed, and the iterative HOT (IHOT) result is derived to spatially characterize the haze contamination in the Landsat images. A group of Landsat images that were acquired in different landscapes and seasons were used to test IHOT. Visual comparisons indicate that IHOT performed better than previous haze detection methods for images that were acquired in diverse landscapes and also performed robustly for hazy images that were acquired at different seasons when using the same reference clear image. Additionally, two indirect quantitative validations were used to illustrate that IHOT can provide the best transformation for accurately determining haze information. Therefore, it is expected that the proposed IHOT method will be used for automatic cloud/haze detection for large numbers of Landsat images if data sets of clear Landsat imagery are available.

Remote sensing is a valuable way to retrieve spatially continuous information on vegetation phenological changes, which are widely used as an indicator of climate change. We propose a simple method called weighted cross-correlogram spectral matching—phenology (CCSM-P), which combines CCSM and a weighted correlation system, for detecting vegetation phenological changes by using multiyear vegetation index (VI) time series. In experiments with simulated enhanced VI (EVI) for various scenarios, CCSM-P exhibited high accuracy and robustness to noise and the potential to capture long-term phenological change trends. For a temperate grassland in northern China, CCSM-P retrieved more reasonable vegetation spring phenology from Moderate Resolution Imaging Spectroradiometer (MODIS) EVI images than the MODIS phenology product (MCD12Q2). When validated against field phenological observations in five of the AmeriFlux Network sites in the U.S. (four deciduous broadleaf forest sites and a closed shrublands site), and a cropland site in China, CCSM-P exhibited mean absolute differences (MADs) ranging from 2 to 10 days (median: 4.2 days), whereas MAD of non-CCSM methods showed larger variations, ranging from 5 to 58 days (median: 21.3 days). This is because CCSM-P integrates field phenological observations. Compared with non-CCSM methods, which are widely used to identify phenological events, CCSM-P is more accurate and less dependent on prior knowledge (thresholds or predefined functions), which indicates its effectiveness and applicability for detecting year-to-year variations and long-term change trends in phenology, and should facilitate more reliable assessments of phenological changes in climate change studies.

Spectral mixture analysis (SMA) is widely used to quantify the fraction of each component (endmember) of mixed pixels that contain spectral signals from more than one land surface type. Generally, nonlinear SMA (NSMA) outperforms linear SMA (LSMA) in the vegetation (tree, shrub, crop, and grass) and soil mixture case because NSMA considers the significant multiple scattering that exists for these mixtures. However, compared to LSMA, the bilinear NSMA method, which is a typical physical-based NSMA method, is undermined by its susceptibility to the collinearity effect. In this paper, a two-step constrained NSMA method (referred to as TsC-NSMA) is proposed to mitigate the collinearity effect in the bilinear NSMA method. The theoretical maximum likelihood range is mathematically derived for each endmember fraction, and the ranges are used as additional constraints for the bilinear NSMA method to optimize the unmixing results. Three different data sets, including simulated spectral data, an in situ ground plot spectral measurement, and a Landsat8 Operational Land Imager image, were used to assess the performance of the TsC-NSMA method. The results indicated that TsC-NSMA achieved the highest estimation accuracy for all mixed scenarios which either contain severe endmember collinearity or high noise levels, thereby suggesting its ability to mitigate the collinearity effect in the bilinear NSMA method with the potential to improve the estimation of endmember fractions in practical applications.

Positive and unlabeled learning (PUL) algorithm, an one-class classifier which is trained by positive samples and unlabeled samples, has been used in remote sensing classification. However, the effect of training strategy of PUL has not been investigated. This study tested the performances of PUL-SVM on cropland mapping by Landsat TM data using the training samples with different sizes and different purity levels. It is found that the highest accuracy is achieved when the sizes of positive sample and unlabeled sample are comparable if using the random strategy. In contrast, if using the purer positive samples, it is more difficult to find the optimal unlabeled sample size. Therefore, it is recommended the random strategy for the positive samples, and the balanced sizes for positive and unlabeled samples when using PUL-SVM.

Plant self-thinning power law is regarded as an essential regulation which plays an important role in determining population dynamics and community structure. However, little experimental and theoretical studies on the power law have been developed for shrub, and also little has been done from the perspective of below-ground plant parts to reveal the mechanisms underlying the self-thinning process of natural plant especially in field. Taking the shrub, Caragana microphylla, as example, this study revealed the below-ground root biomass-density relationship of Caragana microphylla and explained how and to what extent do below-ground root competition affect the above-ground self-thinning process. The below-ground root systems were surveyed by Ground- penetrating radar (GPR). The root biomass was estimated based on GPR data. Some of the statistical relationships discussed in this study may be useful for predicting root biomass of shrub community in water-limited ecosystems and will also serve as a frame of reference for future studies.

Bilinear Model (BM) is one of widely used nonlinear spectral unmixing methods, which are developed to deal with nonlinearity resulted from the multiple scattering within mixed pixels such as plant-soil mixtures. In the BM, products of endmember spectra are used to represent multiple scattering effect, and this approximation needs a validation. This study applies a Monte Carlo ray-tracing (MCRT) model to test this approximation by exploring the correlations between multiple scattering reflectances and endmember products. The correlations are found linear, proving the rationality of this approximation. Besides, the correlations between multiple scattering coefficients in the BM and vegetation characters are analyzed. Scattering coefficients are found to have quadratic relationships with vegetation coverage and linear relationships with crown height. The correlations can be used as constraints when solving the BM to retrieve the abundance of each component, to relieve the collinearity problem which impacts the solution precision.

Fractional vegetation cover (FVC) is one of the fundamental parameters for characterizing terrestrial ecosystems, with wide uses in various environmental and climate-related modeling applications. The remote sensing technique provides a unique opportunity for estimating FVC over large geographical areas by employing spectral mixture analysis (SMA). The effectiveness of SMA depends largely on the accurate extraction of representative and pure endmembers. However, in arid and semiarid environments that have sparse vegetation distributions, most current SMA models may produce large biases due to difficulties in obtaining pure vegetation spectra from the satellite images. This letter developed a new approach to estimate FVC from satellite observations by integrating an endmember spectrum purification procedure into a nonlinear SMA model. The proposed method is capable of extracting pure endmember spectra even though pure vegetation endmember is not present in target images in arid and semiarid environments, which improves the accuracy of FVC retrievals. Validation experiments conducted in the Xilingol grassland, Inner Mongolia, China, demonstrate that the proposed method produces more accurate FVC estimates (RMSE <; 0.13, AD <; 0.06) than do current algorithms. The better performance of the proposed method can be attributed to the purified vegetation spectra that more closely resemble the real pure vegetation spectra.

Multiple scattering within a mixed pixel results in a nonlinear effect on the measured spectra in remotely sensed imagery. This study provides a quantitative assessment of multiple scattering in the reflectance of semiarid shrublands and explores its relationship to the characteristics of shrubs (density and height) and imaging parameters (wavelength and viewing angles). Field measurements were conducted at the southern fringe of the Otindag Sandy Land in China. A Monte Carlo ray tracing model, the Forest LIGHT interaction model (FLIGHT), was applied to simulate the multiple scattering results. FLIGHT simulation results were first evaluated against field measurements and then compared with a Landsat-8 OLI image. Results show that: 1) the contribution of multiple scattering to the spectra of a scene increases linearly with the fractional cover of vegetation and crown height; 2) in general, multiple scattering has a stronger effect on the near-infrared (NIR) domain than on the visible bands; 3) shadows significantly strengthen the multiple scattering effect, specifically within the visible bands; and 4) 80 to 100% of the total multiple scattering is caused by the second-order scattering within the visible bands and 60% to 90% within the NIR band. This study helps to improve our understanding of the multiple scattering effect and to select between linear and nonlinear spectral unmixing models to solve the abundances of shrubs and soil in mixed pixels.

Remote estimation of the vertically averaged diffuse attenuation coefficient over a water layer K̅d(λ) is of great importance in understanding and modeling the physical, chemical, and biological processes in water bodies. A semianalytical algorithm was previously proposed to remotely estimate K̅d( λ) based on the retrieval of the total absorption and backscattering coefficients ( a and bb ). The algorithm has been effectively applied to clear and slightly turbid oceanic waters, but its applicability in turbid inland waters is still unknown. In this study, the relationship between a and bb on the one hand with K̅d(λ) used in the semianalytical algorithm was first validated by the measured a and bb. Second, the semianalytical algorithm was combined with the so-called QAA_Turbid (quasi-analytical algorithm for turbid waters) to remotely estimate K̅d(λ) for highly turbid waters. We tested the performance of the combined algorithm at wavelengths of 443, 556, and 669 nm by using a data set collected from a turbid lake in Japan. The validation results demonstrated that it could estimate K̅d(λ) (ranging from 1.94 to 9.47 m-1) with root-mean-square error and relative error values of 0.17 and 12.96%, respectively. These results indicate the great potential of the semianalytical algorithm to accurately monitor the spectra of K̅d(λ) for turbid inland waters from satellite observations.

Shadows in remotely sensed imagery occur when objects totally or partially occlude direct light from a source of illumination, generating great difficulty in land cover interpretation and classification because of the loss of spectral information of shaded pixels. In a mountainous environment with rough terrain, shadows are especially pronounced due to the differentiation of direct illumination between sunny and shady slopes. Topographic correction methods, which are widely used to adjust for differences in solar incidence angles, can partly alleviate the impacts of shadows. However, there are two limitations: one is that the contemporary topographic corrections have little effect on areas that have very low incidence angles and areas that are completely without direct solar illumination (cast shadow); another is that their effectiveness is restricted by the data quality and completeness, spatial resolution, and elevation accuracy of the Digital Elevation Model (DEM) data, which is not currently available in all parts of the world. Thus, noise and errors may be introduced in topographic correction during resampling and geometric registration of the target image. This paper proposes a new approach to restore the radiometric information of mountainous cast shadows using a spectral processing technique called “continuum removal” (CR) without the aid of DEM. The CR-based approach makes full use of the spectral information derived from both the shaded pixels and their neighboring nonshaded pixels of the same land cover type. Several Landsat TM images were used to assess the performance of the proposed method. Results indicated that the proposed method can effectively restore the spectral values of shaded pixels more accurately than the ATCOR_3 correction method, especially for very low incidence angle areas and cast shadows. By comparing data values of shaded pixels with nonshaded pixels (pure reference pixels) of their same class, images processed by the proposed method had the lowest average root mean square error (RMSE) between them in visible, NIR and SWIR bands, followed by the ATCOR_3 correction method and the original image. In addition, the proposed method achieved the best classification accuracy, higher than those from the original test image and the ATCOR_3 corrected image generated using 90 m or 30 m spatial resolution DEM. Therefore, the Continuum Removal method is a better alternative for restoring objects obscured by mountainous shadow when adequate DEM data are unavailable and the quality of DEM cannot satisfy the requirements of topographic correction algorithms.

Spectral mixture analysis is an important technique to extract desired information from the mixed remotely sensed data. However, current spectral mixture analysis techniques suffered from the endmember variability. Quantitative assessment of SMA techniques with simulated data is critical to understand the influence of endmember variability. For that reason, this study has compared five typical spectral mixture analysis addressing endmember variability issue with simulated data. The comparison result shows that MESMA seems to be the best in unmixing accuracy. However, sensitive to noise and large computation loads also made MESMA less satisfactory, while other methods could supersede MESMA at specific situations.

The problem of emergency facility location is a critical component in evacuation planning. The emergence of geographic information systems (GIS) has provided a useful operational platform to assist this issue. A previously overlooked facet is the consideration of a hierarchical structure in the placement of emergency shelters. Due to the fact that survivors' needs change over time during post-disaster evacuations, shelters have now been categorized on a temporal scale based on their functions at different evacuation phases. This article proposes a three-level hierarchical location model for optimizing the placement of earthquake shelters by taking into account this temporal variance. The article not only scrutinizes the modeling procedure but also implements the model in a planning area with many real-world details. Based on the optimization results derived from a GIS context, we have found that the quality of the earthquake response procedure is not only dependent on the placement strategy of shelters, but more importantly on the financial constraints imposed on the planning and construction of these shelters. A discussion has been proposed to balance the trade-off between budget planning and evacuation efficiency. As the first attempt to model the hierarchical configuration of emergency shelters with specific focus on evacuees' escalating sheltering demands, this article will be of great significance in helping policy makers consider both the spatial and financial aspects of the strategic placement of emergency shelters.

The Fraunhofer line discriminator (FLD) principle applied on the atmospheric oxygen absorption feature around 761 nm ( O2-A band) has been widely used to retrieve solar-induced chlorophyll fluorescence (Fs) from remotely sensed data. In this letter, however, we address a violation of the basic assumption caused by O2absorption feature changes, and we evaluate the impact of diminishing O2absorption with the increase in ground altitude on Fsretrieval accuracy. The Fs retrieval accuracy substantially decreases in higher ground altitude areas for the standard FLD and three-band FLD methods, in which relative estimation errors increase approximately 70%-80% and 10%-16%, respectively, with an increase in ground altitude from 0.01 to 4.5 km. However, this increasing trend in Fs retrieval error does not occur with the use of the improved FLD (iFLD) method, which exhibits smaller than 5% of changes in relative estimation errors. Analytical analyses reveal the causes of the changes in Fs retrieval accuracy by the three methods. Based on these findings, the iFLD method is recommended to be used in cross-altitude studies or for Fs estimation in higher ground altitude areas under conditions of low radiometric noise, although its high sensitivity to noise should be taken into account. The investigations in this letter further indicate that the impact of ground altitude should be included in the uncertainty budgets of Fs retrievals and should be considered in the interpretation of Fs signals at the O2-A absorption feature.

Roots have important functions in the ecosystem. Therefore, establishing root-related parameters such as root size, biomass, and 3-D architecture is necessary. Traditional methods for measuring tree roots are labor intensive and destructive to nature, limiting quantitative and repeated assessments in long-term research. Ground-penetrating radar (GPR) provides a nondestructive method for measuring tree roots. This study investigates the feasibility of a GPR system with 500-MHz, 900-MHz, and 2-GHz measurement frequencies for detecting tree roots and estimating root biomass under controlled experimental conditions in a sandy area. After energy attenuation correction and velocity analysis, not only the individual root in subsurface is able to be located but also the parameters that correlate well with root biomass can be extracted from the processed GPR data. The major findings were as follows. First, both the amplitude and amplitude-area indices were confirmed to be more effective for estimating root biomass after attenuation-effect compensation. This result suggests that the calibration of GPR wave-attenuation effects and velocity changes with depth are helpful in estimating root biomass from GPR parameters. Second, the selection of GPR system frequency was mainly dependent on field conditions, particularly soil water content. Lower frequency was recommended for developing root biomass estimation model under varied soil conditions. Third, the new method based on the metal reflector experiment was effective and easy to perform in situ for attenuation-effect correction.

Remote estimation of inherent optical properties (IOPs) for water bodies cannot only provide indicators of water quality, but also be used in the study on biological and biogeochemical processes of waters. The quasi-analytical algorithm (QAA) is a simple and effective method to retrieve IOPs from remote-sensing reflectance (Rrs). The QAA has been widely validated and applied in oceans, but its application in inland waters is far less extensive. In this paper, the QAA was enhanced to retrieve IOPs for turbid inland waters based on the bandwidths of Medium Resolution Imaging Spectrometer (MERIS). The enhancement was achieved by proposing a semi-analytical model to estimate the spectral slope of particle backscattering, as well as a novel estimation model for phytoplankton absorption coefficient at 443 nm. Two data sets (i.e., noise-free synthetic data and in-situ data) were collected to assess the performance of the enhanced algorithm. Results show that the algorithm yields almost error-free estimations for total absorption and backscattering coefficients and estimations for phytoplankton absorption at 443 nm with acceptable accuracy in the case of synthetic data set. For the in-situ data set, the algorithm retrieves the total absorption coefficients (ranging 0.337-8.331 m-1) with root-mean-square-error in log scale (RMSE) and bias in log scale lower than 0.130 and 0.094, respectively, and phytoplankton absorption at 443 nm (ranging 0.378-4.669 m-1) with RMSE and bias in log scale of 0.151 and 0.096, respectively. These results indicate the potential of the enhanced QAA to accurately retrieve the IOPs from MERIS satellite observations for inland waters.

Vegetation-index-based spatial sharpening technologies were developed for improving the spatial resolution of thermal-infrared (TIR) images. Previous studies showed that the relationship between vegetation index and surface temperature is independent with spatial resolution. However, spatial extent is another scale factor which may affect the relationship of vegetation index and surface temperature but was neglected in the previous studies. In this letter, we investigated both of these two aspects of scale effect (spatial resolution and spatial extent) of the relationship of vegetation index and surface temperature based on two scenes of Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) data respectively acquired in grassland and crop-land. The result shows that the relationship of vegetation index and temperature on different spatial extents varies greatly. Therefore, previous methods which apply the relationship established on the whole image with coarse resolution to the small area (pixel size of the TIR image) with fine resolution may produce a large error. We modified the original sharpening method by establishing the vegetation-index-temperature relationship on the minimum available spatial extent (2 × 2 pixels of the TIR image) instead of the whole image. Then, the new method was tested and compared with the original method by simulated data. The result shows that the new method performs more robustly than the original method.

Thick-cloud contamination is a common problem in Landsat images, which limits their utilities in various land surface studies. This letter presents a new method for removing thick clouds based on a modified neighborhood similar pixel interpolator (NSPI) approach that was originally developed for filling gaps due to the Landsat ETM+ Scan Line Corrector (SLC)-off problem. The performance of the proposed method was evaluated with both simulated and real cloudy images and compared with that of a contextual multiple linear prediction (CMLP) method. The results show that the modified NSPI approach can greatly reduce the edge effects by CMLP. The reflectance restored by the modified NSPI approach is more accurate than that by CMLP, especially when the cloud-free auxiliary and cloudy images are acquired from different seasons and have different spectral characteristics.

Vegetation index based thermal sharpening technologies have been studied recently. Previous studies showed that the relationship between vegetation index and surface temperature is independent with spatial resolution; consequently, the relationship established on coarse resolution can be applied on fine resolution. However, in this study, we found that the resolution independence can not grant the effectiveness of the sharpening method. Instead, relationship of vegetation index temperature established on the correct spatial size (size of thermal resolution) should be used for sharpening temperature image. Unfortunately, the relationship of vegetation index temperature on different spatial sizes varies greatly. Therefore, previous methods which apply the relationship established on the whole image to the small spatial size (size of thermal resolution) may produce large error. In this paper, an improved vegetation index based sharpening method considering the effect of spatial size was proposed and tested by a simulation study. The result shows that it can acquire higher sharpening accuracy than original method.

Postclassification comparison (PCC) and change vector analysis (CVA) have been widely used for land use/cover change detection using remotely sensed data. However, PCC suffers from error cumulation stemmed from an individual image classification error, while a strict requirement of radiometric consistency in remotely sensed data is a bottleneck of CVA. This letter proposes a new method named CVA in posterior probability space (CVAPS), which analyzes the posterior probability by using CVA. The CVAPS approach was applied and validated by a case study of land cover change detection in Shunyi District, Beijing, China, based on multitemporal Landsat Thematic Mapper data. Accuracies of “change/no-change” detection and “from-to” types of change were assessed. The results show that error cumulation in PCC was reduced in CVAPS. Furthermore, the main drawbacks in CVA were also alleviated effectively by using CVAPS. Therefore, CVAPS is potentially useful in land use/cover change detection.

In the past decades, spectral unmixing has been studied for deriving the fractions of spectrally pure materials in a mixed pixel. However, limited attention has been given to the collinearity problem in spectral mixture analysis. In this paper, quantitative analysis and detailed simulations are provided, which show that the high correlation between the endmembers, including the virtual endmembers introduced in a nonlinear model, has a strong impact on unmixing errors through inflating the Gaussian noise. While distinctive spectra with low correlations are often selected as true endmembers, the virtual endmembers formed by their product terms can be highly correlated. It is found that a virtual-endmember-based nonlinear model generally suffers more from collinearity problems compared to linear models and may not perform as expected when the Gaussian noise is high, despite its higher modeling power. Experiments were conducted on a set of in situ measured data, and the results show that the linear mixture model performs better in 61.5% of the cases.

The matrix inversion method (MIM) is an effective algorithm for estimating water constituent concentrations in case II waters. To apply this method, appropriate and accurate specific inherent optical properties (SIOPs) for each constituent in water are essential. However, many routine observations of lake water quality do not in fact provide SIOPs, thus limiting the application of the MIM. In this paper, an alternative MIM method based on linear matrix inversion theory was proposed to relax the requirement of SIOPs measurement. For this, so-called ESIOPs (Estimated SIOPs) were first derived by an unusual application of MIM based on adequate calibration samples; then the water constituent concentrations for the whole study area were retrieved by the standard application of MIM based on the derived ESIOPs. For each calibration sample, measurement of the reflectance spectrum and corresponding water constituent concentrations, which can be obtained from periodical satellite data and routine field surveys, is required. The performance of the proposed method was evaluated using the simulation data from Hydrolight and three MEdium Resolution Imaging Spectrometer Instrument (MERIS) images. The results showed that this method yielded satisfactory estimations of the water constituent concentrations for the noise-contaminated simulation data sets. For MERIS data in our study area (Lake Kasumigaura, Japan), the average bias (mean normalized bias or MNB) and relative random uncertainty (normalized root mean square error, or NRMS) were in the range of -11.2% to 3.4% and 4.8% to 29.7% for each water constituent concentration. These findings imply that the algorithm proposed in this study is theoretically reasonable and practically applicable.

A three-band index was previously proposed and successfully utilized to estimate the chlorophyll-a concentration (Chl-a) in case-II waters. However, this index shows uncertainties in highly turbid situations. In this study, an enhanced three-band index is proposed to solve this problem. Since the new index employs bands that are identical to those of the original threeband index, it can be applied to Medium Resolution Imaging Spectrometer (MERIS) data. The performance of the index was evaluated using the data collected from two turbid Asian lakes: Lake Kasumigaura, Japan, and Lake Dianchi, China. The results showed that the Chl-a predicted by the enhanced threeband index was strongly correlated with the measured Chl-a (R2> 0.83), and the root-mean-square error (rmse) and the normalized root-mean-square error (NRMS) were both reduced for the two lakes (for Lake Kasumigaura, rmse from 13.97 to 8.68 mg · m-3and NRMS from 19.01% to 12.30%; for Lake Dianchi, rmse from 41.29 to 15.28 mg · m-3and NRMS from 35.83% to 21.34%). These findings imply that, if accurately atmospheric-corrected MERIS data are available, the enhanced three-band index could be used for mapping Chl-a even in highly turbid case-II waters.

Several spectral unmixing techniques have been developed for subpixel mapping using hyperspectral data in the past two decades, among which the fully constrained least squares method based on the linear spectral mixture model (LSMM) has been widely accepted. However, the shortage of this method is that the Euclidean spectral distance measure is used, and therefore, it is sensitive to the magnitude of the spectra. While other spectral matching criteria are available, such as spectral angle mapping (SAM) and spectral information divergence (SID), the current unmixing algorithm is unable to be extended to these measures. In this paper, we propose a unified subpixel mapping framework that models the unmixing process as a best match of the unknown pixel's spectrum to a weighted sum of the endmembers' spectra. We introduce sequential quadratic programming to solve the nonlinear optimization problem encountered in the implementation of this framework. The main feature of this proposed method is that it is not restricted to any particular similarity measures. Experiments were conducted with both simulated and Hyperion data. The tests demonstrated the proposed framework's advantage in accommodating various spectral similarity measures and provided performance comparisons of the Euclidean distance measure with other spectral matching criteria including SAM, spectral correlation measure, and SID.

Vegetation phenological stages such as the start of season (SOS), the end of season (EOS) and the growing season length (GSL) are key indices to depict vegetation growth. Crop phenological characteristics are generally more complex than natural vegetation because they are affected by both climate variability and human activities. The North China Plain (NCP) is an intensively cultivated agricultural area where winter wheat is widely planted. In this study, the SOS, EOS, and GLS of winter wheat in the NCP from 1982 to 2000 were extracted at daily level with 8km NOAA/AVHRR 10 days MVC NDVI data by considering human activities like harvesting and sowing. The major findings of this study are as the follows: 1) The SOS1 of the winter wheat has large inter-annual variation. The SOS is very sensitive to climate variation. GSL has significant increase. 2) The EOS2 decreased in most areas, which means the winter wheat harvesting date became earlier and earlier. Consequently the GSL has a significant decreasing trend from the year of 1982 to 2000. 3) The decrease of GSL is well connected to the temperature increase and precipitation decrease over the NCP during the past 20 years.

The normalized difference vegetation index (NDVI) time-series database, derived from NOAA/AVHRR, SPOT/VEGETATION, TERRA or AQUA/MODIS, is increasingly being recognized as a valuable data source for extracting land cover and its change information at global, continental and large regional scale. However, existing approaches, such as principal component analysis (PCA) and change vector analysis (CVA) present considerable difficulties in taking full advantage the NDVI dataset for land cover change detection. Based on the assumptions that different land cover types have different NDVI temporal profiles and that the NDVI profile curve can be regarded as a spectrum in which an NDVI value for a certain date corresponds to on band value of this spectrum, we analyzed the existing change detection indexes and develop a new land cover change detection method based on Lance distance and a cross correlogram spectral matching (CCSM) technique. The new method was validated in the simulation experiments and a case study area of Beijing. From the results, we have demonstrated that the new method takes the shape and value features of NDVI profile curve into consideration. The relatively better performance of the new method can be attributed to two advantages: (1) the new method can discriminate long-term land cover changes form other changes by excluding false changes caused by vegetation phenology changes, climate events, atmospheric variability and sensor noise; (2) it is similarly sensitive to all kinds of land cover changes no matter where the changes have occurred. The better results compared with the CVA method suggest that the new method is effective and has potential for land cover change detection using an NDVI time-series dataset. Furthermore, it is worth noting that the method can not only be applied to NDVI datasets but also to other index datasets reflection surface conditions sampled at different time interval. It can also be applied to datasets for different satellites without the need to normalized sensor differences.

A new approach is proposed to remove cloud-fog cover and to enhance satellite imagery based on Laplacian enhancement and histogram transformation. The new approach is also compared with other two methods: homomorphism filter and histogram match. Information entropy and spectral correlation coefficient are used to evaluate the results. A multi- spectral SPOT image, which is contaminated by thin cloud-fog noises, is selected for the research. The results from experimental tests show that the new approach is better than the two others, not only removing cloud-fog cover and enhangcing the spatial information of images but also keeping the true spectral feature of the images.

Grassland deterioration due to climate variability and human disturbance in arid and semi-arid areas is becoming a serious environmental problem in China. Establishing grassland protection area to control the overgrazing is regarded as one of the effective measure to protect grassland. The paper presented a new method that integrates cellular automata (CA) model, geographical information system and remote sensing to zone grassland protection area with the case study in Xilingol "typical steppe" grassland in Inner Mongolia autonomous regions of China. The basic idea of the method is to extract "seed points" of the grassland protection area by using remote sensing techniques and geographical information system (GIS) at first, then simulate the grassland protection area by CA model. Since the method tries to satisfy the zoning requirement of the grassland protection area and utilizes the advantages of remote sensing techniques and CA models, satisfactory results have been produced for governmental officials and planners with manpower saved

Modeling land use scenario changes and its potential impact on the ecosystem structure and functioning in typical region are helpful to understand the reciprocal mechanism between land use system and ecosystem. A land use scenario dynamics model (LUSD) by the integration of system dynamics (SD) model and cellular automata (CA) model is developed with land use scenario changes in China in next 50 years simulated in this paper. The basic idea of LUSD is to model the land use scenario demand by SD model at the national/regional scales at first, then allocate the land use pattern at the local scale with the consideration of land use suitability, inheritance and neighborhood effect by CA model to satisfy the balance of land use demand and supply. The application of LUSD in China suggests that the model have the ability to reflect the complex behaviors of land use system at different scales to some extent and be a useful tool to assess the potential impact of land use system on ecosystem.