Dioscorea PolystachyaTurczaninow is one of the most famous traditional Chinese Materia Medica. However, there is lack of large-scale classification method which is crucial for its growth status monitoring and yield estimation. This study proposed a reliableDioscorea PolystachyaTurczaninow classification model based on spectral-temporal-spatial feature optimization using time-series Sentinel-2 data. Firstly, 16 mono-temporal classification models were developed using five vegetation indices (VIs) and random forest algorithm. Then, temporal feature optimization was conducted by identifying the most effective time phase combinations based on Sentinel-2 time-series normalized difference vegetation index (NDVI) data, gaussian mixture modeling algorithm and F1 score ofDioscorea PolystachyaTurczaninow in each mono-temporal model. Next, spectral features were optimized by replacing NDVI with the optimal VI corresponding to each time phase, thus constructing a multi-VIs-based time-series dataset. Finally, the spatial feature optimization was conducted using the three-dimensional convolutional neural network (3-D CNN) algorithm and the multi-VIs-based time series Sentinel-2 data. Following the comprehensive feature optimization, the finalDioscorea PolystachyaTurczaninow classification model was determined. The results found that Sentinel-2 data acquired during the rhizome enlargement stage played a crucial role in classifying theDioscorea PolystachyaTurczaninow. By using the optimized features, the classification model achieved theDioscorea PolystachyaTurczaninow F1 score of 95.00%, which improved by 11.49% compared to only using the time series NDVI data. This spectral, temporal and spatial feature optimization method also has the potential to the development of large-scale, dynamic, and accurate mapping for other crops.

Timely and accurate detection of Earth surface anomalies (ESAs) using single-temporal remote sensing data is critical for early warning and rapid emergency response. However, existing algorithms are designed mostly for specific ESA categories and suffer from ambiguous decision boundaries between anomalous and normal instances. Therefore, this study proposes a novel unified ESA detection model based on historical steady-state priors that quantify feature deviations as detection indicators, with enhanced discrimination achieved by amplifying the separation between anomalies and the normal Earth surface. First, an enhanced hyperbolic space network is developed, which can represent high-dimensional complex data in lower-dimensional spaces by capturing hierarchical structures in images, thereby extracting richer features from remote sensing imagery. Then, these extracted features are aggregated under semantic guidance to construct the historical steady-state prior that characterizes normal Earth surface patterns, which is represented by a lightweight prototype set. Next, to further improve the representativeness of the steady-state prior, a semantic augmented contrastive clustering (SACC) module is introduced to refine the prototype set by reducing intra-class variability and increasing inter-class separability. Finally, leveraging the geometric property of hyperbolic space which amplifies feature differences, a hyperbolic distance-based anomaly scoring function is proposed to quantify the anomaly level of test samples by measuring the hyperbolic distance between their feature representations and prototypes in prototype set. This enables accurate ESA detection from single-temporal remote sensing image. The results indicate that the proposed method outperforms the currently popular methods in both accuracy and robustness and has potential for on-orbit deployment and applications. Code and dataset are available at: https://github.com/Jifc1024/H2PNet.

On-orbit rapid detection of land surface anomalies is important for ensuring ecological security and human safety. Land surface anomalies (e.g., fire, industrial heat source, and deforestation, etc.) are often accompanied by different degrees of thermal anomalies. Existing methods for detecting thermal anomalies have focused primarily on high-temperature anomalies, without available approach for detecting widespread low-temperature anomalies. Here, a Novel Method based on Constructed Reference land surface temperatures (LST) for on-orbit remote sensing detection of various Thermal Anomalies (NMCRTA) is proposed and further evaluated using Landsat 8 LST product. In this method, we first construct an fitted reference temperature based on LST spatiotemporal trend surface modeling and a real reference temperature based on contextual averaging. Then, the difference (including the step of removing atmospheric effects) between on-orbit observed LST and fitted reference LST, and the difference between on-orbit observed LST and real reference LST are calculated, respectively. Finally, these two temperature differences are utilized to detect thermal anomalies using corresponding thresholds. The results indicate that the NMCRTA can effectively detect deforestation, newly constructed buildings, and river drying, with an overall F1-score of 0.867, in a 100 × 100 km region scale. Meanwhile, the NMCRTA exhibited excellent accuracy in detecting fires, deforestation, and landslides at the 15 × 15 km scene scale, achieving F1-scores of 0.943, 0.857, and 0.791, respectively. Furthermore, the NMCRTA can continuously capture different thermal anomaly events associated with a newly constructed industrial heat source and perform well in nighttime. The NMCRTA is promising for future on-orbit remote sensing detection of various land surface anomalies, as a valuable supplement to optical on-orbit detection method.

Be aware of the significance of land surface net radiation ($R_{n}$), there is a need for accurate long-term and high spatial resolution global$R_{n}$estimates based on satellite data. Herein, we propose a novel globally applicable, highly effective algorithm for estimating daily$R_{n}$directly from Visible Infrared Imaging Radiometer Suite (VIIRS) top-of-atmosphere (TOA) observations ranging from 2011 to present, using the eXtreme Gradient Boosting (XGBoost) method. This algorithm, named the constraint conditional model (CCM), consists of five conditional models (namely, cases 1–5 model) divided by the combination of the length of daytime (dt), the instantaneous sky condition, and the surface broadband albedo, and the daily downward shortwave radiation (DSR) from ERA5-Land was introduced as a physical constraint when$dt \gt 9$, in which case$R_{n}$is dominated by$R_{\textit {si}}$(incoming solar radiation). The validation accuracy of CCM was satisfactory against the ground measurements, yielding a root-mean-square error (RMSE) of 18.95 Wm−2, a bias of 0.056 Wm−2, and an$R^{2}$of 0.89. The algorithm exhibited superior accuracy and robustness compared to GLASS-MODIS and ERA5-Land under spatiotemporally independent validation samples. This indicates the potential of VIIRS to extent MODIS$R_{n}$products for generating long-term global daily$R_{n}$data.

Deep learning-based classification methods of hyperspectral images (HSIs) have made significant progress recently, catching the attention of academia and industry; however, the existing studies of classification of HSIs mainly focus on the closed-set environment with the assumption that ground classes are fixed and known, ignoring the complexity and diversity of ground objects in the real world. As a result, the unknown classes will be forced into known classes. To solve this problem, we propose a novel spectral-spatial evidential learning (SSEL) network that combines an improved generative adversarial network (GAN) and evidential theory for open-set classification of HSIs. First, a domain adaptation (DA) strategy is embedded into GAN to generate high-quality samples by reducing the distribution discrepancy between generated and real samples. Second, the discriminator is devised to extract spectral-spatial features and output multiclass evidence for closed-set classification and uncertainty estimation. A new classification function called evidence-based loss is designed for the discriminator to guide the evidence-collection process. Additionally, a novel adversarial objective function is defined, where the discriminator loss is devised to predict real samples belonging to the true class and generated samples belonging to “none of the classes. The generator loss is developed to generate samples consistent with the label category. Finally, the class and corresponding uncertainty can be calculated based on the collected evidence and the appropriate open-set classification of HSIs. Extensive experiments on three benchmark HSIs show that our proposed method achieves competitive performance on closed-set and open-set classification of HSIs compared with existing state-of-the-art methods.

The GF-1 wide field view (WFV) data have wide view angles ranging from 0° to 48°, which generate notable angular effects for earth surface monitoring. However, current angular correction method for GF-1 WFV data relies on bidirectional reflectance distribution function (BRDF) parameters derived from coarse resolution data, leading to limited correction accuracy. Therefore, this study aims to develop a general set of BRDF parameters for 16-m WFV data angular effect correction and improving the vegetation parameter estimation accuracy. Firstly, more than 40 high-quality GF-1 WFV data in the North China Plain and Northeast China regions covering the typical vegetation types were collected to construct BRDF parameters. This study took into account three vegetation types (cropland, grassland and forest) and the normalized differential vegetation index (NDVI) magnitude. Through the least square method, a set of BRDF parameters were estimated based on various NDVI levels. Then, the nadir reflectance was calculated to estimate leaf area index (LAI) and fractional vegetation cover (FVC). Finally, the evaluation of the corrected reflectance indicated that the developed BRDF parameters performed best for correcting angular effect of cropland, and followed by grassland. In addition, the validation indicated that the generated BRDF parameters effectively improved the LAI and FVC estimation accuracy.

Early warning systems and emergency management for disasters, environmental pollution, and illegal development require timely and accurate Earth surface anomaly detection (ESAD). Remote sensing, which uses satellites to observe the Earth’s surface, is an emerging approach to address this need. However, current remote sensing methods for ESAD are limited by their focus on specific anomalies, reliance on high-level satellite data, and the demand for significant computational and storage resources. In this article, we present a novel framework for general and on-orbit ESAD, which combines large vision models and lightweight priors. Our method characterizes images with a large vision model that is highly generalizable, reducing the dependency on high-level data, and compressing the prior base with sampling techniques, facilitating transmission and on-orbit storage. For on-orbit detection, we use a dictionary look-up style method for efficient anomaly prediction, enabling detection on satellites with limited computation resources. We evaluate our framework on typical scenarios and compare it with popular change detection (CD)-based and anomaly detection (AD)-based methods. Our results show that our framework achieves good performance while reducing the prior size by at least 95.56 times. Moreover, our framework can handle unpaired data, providing a chance to detect anomalies in the absence of near-term and paired images. Our framework has the potential to support the development and applications of general, on-orbit ESAD. The code and dataset are available at the following site:https://github.com/YummyWaffle/ESAD.

The acquisition of a time series of high spatial resolution terrestrial latent heat flux (LE) is crucial for agricultural water resource management. However, the currently and frequently used spatiotemporal data fusion model fails to capture LE spatial and temporal patterns during periods of vigorous vegetation growth due to the limited availability of cloud-free fine-resolution images. In this study, we proposed a multiphase self-adaptive (MSA) spatiotemporal data fusion model to address this issue. Unlike popular spatiotemporal data fusion models that rely heavily on cloud-free images, MSA utilizes all available fine- and coarse-resolution images, including those with partial cloud contamination, as inputs to the model. The proposed MSA method was tested at six sites representing five major land cover types across China. The results demonstrate that the MSA model, utilizing all available Landsat images, was more accurate than that relies solely on cloud-free Landsat images [coefficient of determination (R2): 0.53 versus 0.38; root mean square error (RMSE): 8.12 versus 9.93 W/m2]. We also compared the proposed method with three widely used models, the spatial and temporal adaptive reflectance fusion model (STARFM), flexible spatiotemporal data fusion (FSDAF), and Fit-FC. The results show that MSA performed better than other models at recognizing LE spatial details. Additionally, MSA produced a high spatial resolution daily LE that was the most similar to ground-observed LE (R2 = 0.34 (p < 0.01), RMSE = 27.23 W/m2, bias = −2.75 W/m2). The proposed strategy provides an alternative approach for monitoring the high spatial resolution dynamic flux of heat and water over various land cover types.

Leaf area index (LAI) and fractional vegetation cover (FVC) are two essential vegetation parameters for ecological and climate studies. The Chinese Gaofen-1(GF-1) wide field view (WFV) satellite data is a valuable data source for LAI and FVC retrieval at high spatio-temporal resolution. Like its name, GF-1 WFV has very large view angle ranging from 0° to 48°, which can impact the accuracy of vegetation parameter retrieval. The primary aim of the study was to develop an angular effect correction (AFX-fix) method that can effectively normalize GF-1 WFV data. Our objective was to enhance the applicability of the corrected data in retrieving LAI and FVC. The AFX-fix method used angular index, anisotropy flat index (AFX), and a fixed set of bidirectional reflectance distribution function (BRDF) parameters. LAI and FVC were retrieved from the GF-1 WFV reflectance data using the PROSAIL model combined with a random forest method. Results showed that the accuracy of LAI and FVC retrieval in wheat and corn from angular corrected GF-1 WFV data was improved with a decrease in root mean square error (RMSE) by 0.66 for LAI and 0.03 for FVC compared to that based on the original data. We anticipate that this new method will help improve the performance of LAI retrieval of these crop types using WFV data.

Dioscorea polystachyaTurczaninow is an herbaceous vine plant distributed in China, and its rhizome named Chinese yam is a famous traditional Chinese medicine for treating diabetes and other diseases. However, a large region monitoring method of its growth status is lacking, which is important for Chinese yam yield estimation. Therefore, this study proposed a leaf area index (LAI) estimation algorithm forDioscorea polystachyaTurczaninow using a hybrid method and Sentinel-2 vegetation indices. First, two feature selection algorithms the gradient boosting regression tree (GBRT) and absolute Pearson correlation coefficient (APCC), were combined with field-measured data and radiation transfer model simulated data to generate four different feature important ranking groups. Then, a hybrid feature selection algorithm was used to determine the best feature subsets under each ranking group, and GBRT regression and least absolute shrinkage and selection operator (LASSO) were used to develop the LAI estimation models. Finally, the best LAI estimation model forDioscorea polystachyaTurczaninow was determined based on validation accuracy. The results indicated that the field-measured data were more reliable than the simulated data for feature selection, and the best LAI estimation model was the LASSO model using nine selected vegetation indices, which achieved the performance with RMSE of 0.391 and MAE of 0.310. The proposed method could provide real-time LAI estimates for future Chinese yam yield prediction.

The presence of clouds and cloud shadows has limited the applications of optical remote sensing data. Currently, most cloud removal methods are focused on reconstructing remote sensing data contaminated by small or thin clouds. This study proposes an improved method based on multitemporal dictionary learning to reconstruct missing information of remote sensing data contaminated by large and thick clouds. First, the contaminated target image is initialized using all available adjacent cloud-free reference images. Second, reconstructed images from each of the reference images are produced using dictionary learning and sparse representation methods. Then, weights are determined for the abovementioned reconstructed images based on their reconstruction errors over uncontaminated regions and are used to generate the preliminary reconstruction result. Finally, an error correction step for the contaminated regions is applied to the preliminary result, which is then combined with the original uncontaminated pixels to produce the final reconstruction result. The proposed method was evaluated on simulated clouds/cloud shadows based on remote sensing data with various sizes and land cover types. Visual and quantitative analyses of the reconstruction results show that the proposed method outperformed the generally used geostatistical neighborhood similar pixel interpolator (GNSPI) and nonnegative matrix factorization and error correction (S-NMF-EC) methods. Therefore, the results indicated that the proposed method was capable of accurately and effectively reconstructing data contaminated by large and thick clouds.

The Chinese GF-1 satellite, the first satellite of the China High-resolution Earth Observation System launched in 2013, can be used to help estimate evapotranspiration (LE), which is important for myriad hydroclimatic and ecosystem science and applications. We propose a novel approach to use the GF-1 visible and near-infrared (VNIR) measurements at 16 m and 4-day resolutions to estimate LE. The NIR (near-infrared)–red spectral-domain (NRSD) model is coupled to a perpendicular soil moisture index (PSI) and a perpendicular vegetation index (PVI). We applied the model to the Huailai agricultural region of China with 55 scenes of GF-1 imagery during 2013–2017 and validated using ground measurements with footprint models for two eddy-covariance (EC) flux tower sites and one large aperture scintillometer (LAS) site. The results illustrate that the terrestrial daily LE can be estimated with squared correlation coefficients ($R^{2}$) of 0.77–0.84 ($p < 0.01$) and root-mean-square error (RMSE) values of 17.9–21.5 W/m2among all three sites. The site-calibrated statistics are improved by 0.14–0.25 for$R^{2}$and decreased by 4.2–8.3 W/m2for RMSE as compared to the commonly used universal PT-JPL model. A satisfactory performance is achieved across all experimental conditions, encouraging the application of the NRSD model to estimate LE for other broad regions.

Surface downward shortwave radiation (DSR) reaching the ocean surface is critical for investigating the Earth's climate and global change issues. General circulation models (GCMs) simulations are one practical approach to obtain long-term global DSR simulations. Previous studies have reported that the GCM DSR simulations exist biases and uncertainties over ocean surface. Thus, this study used the Bayesian model averaging (BMA) method to improve tropical ocean DSR simulations by weighted averaging the 48 GCM simulations. We evaluated the estimated DSR based on the BMA method using the buoy observations from the Global Tropical Moored Buoy Array (GTMBA) from 2000 to 2005. We also compared the BMA results with single GCM simulations, the estimated DSR based on the simple model averaging (SMA) method and the Clouds and the Earth's Radiant Energy System, Energy Balanced and Filled (CERES EBAF) DSR retrievals. The spatial pattern differences of the BMA results and CERES EBAF DSR retrievals were also analyzed. When the buoy observations were used as the validation data, the validation results showed that the root mean squared errors (RMSE), correlation coefficients (R), and bias of the estimated DSR based on the BMA method are 22.51 W m-2(10.27 %), 0.66, 1.79 W m-2(0.82 %), respectively. Moreover, the estimated DSR based on the BMA method were better than any individual GCM DSR simulations and the estimated DSR based on the SMA method.

Fractional vegetation cover (FVC) is an important land surface parameter for many environmental and climate-related modeling and agricultural applications. Incorporating vegetation growth information into FVC estimation process could effectively improve FVC estimation accuracy. Methods utilizing vegetation growth information from field measurement and coarse resolution FVC product have been developed recently to estimate site-scale and finer spatial resolution FVC, and achieved satisfactory performances. However, the computational efficiency of these methods is not satisfactory and they are only feasible for analyzing historical data containing a complete vegetation growth cycle. This letter developed a time-efficient FVC estimation method at Landsat scale based on temporally rich data from coarse spatial resolution Global LAnd Surface Satellite (GLASS) FVC, which facilitates development of a time-efficient dynamic vegetation growth model, and radiative transfer models linking Landsat 7 reflectance to FVC, and all combined in a probabilistic dynamic Bayesian network (DBN) framework. In addition, the proposed method is also suitable for real-time FVC estimation and has the potential to be applied on a larger scale. Validation results indicate that the performance of the proposed method is satisfactory (R2= 0.889, RMSE = 0.0917) and comparable to previously developed inefficient but well-established FVC estimation method incorporating the vegetation growth model represented by modified Verhulst logistic equation (R2= 0.884, RMSE = 0.0913).

The daytime surface net radiation (Rn) product from version 4.0 Global LAnd Surface Satellite (GLASS) product suite was recently generated from Moderate Resolution Imaging Spectroradiometer data. It is the daytime average product of Rnderived from 2000 to 2015 at a spatial resolution of 0.05°. This letter describes the results of validation of this new Rn product using ground measurements collected from 142 sites distributed worldwide. The overall accuracy of the GLASS daytime Rnproduct was satisfactory, with an R2of 0.80, root-mean-square error of 51.35 Wm-2, and mean bias error of 0.11 Wm-2. Its accuracy and quality were highly consistent for different land cover classes and elevation zones.

The accurate estimation of the terrestrial latent heat flux (LE) from satellite observations at high spatial and temporal scales plays an important role in the assessment of the water and heat exchange between the earth's surface and the atmosphere. Although a variety of data fusion methods have been proposed to merge different LE products for more reliable estimates, most of them have ignored the spatiotemporal consistency of LE products across different resolutions. In this paper, we apply the multiresolution tree (MRT) method to improve the accuracy and reduce the inconsistency between the Moderate Resolution Imaging Spectroradiometer (MODIS) LE (MOD16) product and the Landsat-based LE product at different resolutions. Eddy covariance (EC) ground measurements at five sites, MODIS and Landsat images from January 2005 to December 2005 in the north central USA, are used to evaluate the performance of the MRT method. The results show that the MRT method can improve the accuracy of the original LE products (MOD16 and Landsat), and it has the potential to significantly reduce the uncertainty and inconsistency of these products. The bias decreased by 38.3% on average, and the root-mean-square error (RMSE) decreased by approximately 49.2% after the MRT was applied at each scale. Further studies are still required to make the MRT method more universal on a variety of land cover types for long-time periods.

Surface shortwave net radiation (SSNR) and surface downward shortwave radiation (DSR) are the two surface shortwave radiation components in earth's radiation budget and the fundamental quantities of energy available at the earth's surface. Although several global radiation products from global circulation models, global reanalyses, and satellite observations have been released, their coarse spatial resolutions and low accuracies limit their application. In this paper, the Global LAnd Surface Satellite (GLASS) DSR product was generated from the Moderate Resolution Imaging Spectroradiometer top-of-atmosphere (TOA) spectral reflectance based on a direct-estimation method. First, the TOA reflectances were derived based on the atmospheric radiative transfer simulations under different solar/view geometries; second, a linear regression relationship between the TOA reflectance and SSNR was developed under various atmospheric conditions and surface properties for different solar/view geometries; third, the coefficients derived from the linear regression were used to compute the SSNR; and finally, the DSR was estimated using the SSNR estimates and broadband albedo at the surface. A 13-year (2003-2015) GLASS DSR product was generated at a 5-km spatial resolution and 1-day temporal resolution. Compared with the ground measurements collected from 525 stations from 2003 to 2005 around the world, the model-computed SSNR (DSR) had an overall bias of 8.82 (3.72) W/m2and a root mean square error of 28.83 (32.84) W/m2at the daily time scale. Moreover, the global land annual mean of the DSR was determined to be 184.8 W/m2with a standard deviation of 0.8 W/m2over a 13-year (2003-2015) period.

Fractional vegetation cover (FVC) is an important parameter for earth surface process simulations, climate modeling, and global change studies. Currently, several FVC products have been generated from coarse resolution (~1 km) remote sensing data, and have been widely used. However, coarse resolution FVC products are not appropriate for precise land surface monitoring at regional scales, and finer spatial resolution FVC products are needed. Time-series coarse spatial resolution FVC products at high temporal resolutions contain vegetation growth information. Incorporating such information into the finer spatial resolution FVC estimation may improve the accuracy of FVC estimation. Therefore, a method for estimating finer spatial resolution FVC from coarse resolution FVC products and finer spatial resolution satellite reflectance data is proposed in this paper. This method relies on the coupled PROSAIL radiative transfer model and a statistical crop growth model built from the coarse resolution FVC product. The performance of the proposed method is investigated using the time-series Global LAnd Surface Satellite FVC product and Landsat-7 Enhanced Thematic Mapper Plus reflectance data in a cropland area of the Heihe River Basin. The direct validation of the FVC estimated using the proposed method with the ground measured FVC data (R2 = 0.6942, RMSE = 0.0884), compared with the widely used dimidiate pixel model (R2 = 0.7034, RMSE = 0.1575), shows that the proposed method is feasible for estimating finer spatial resolution FVC with satisfactory accuracy, and it has the potential to be applied at a large scale.

Fractional vegetation cover (FVC) is an important parameter for describing the conditions of land surface vegetation and is widely used for Earth surface process simulations and global change studies. Regional FVC is primarily derived from remotely sensed data. However, current FVC estimation methods are mainly employed on remotely sensed data at a single time point, which can only reflect the instantaneous physical state of the land surface and ignore the important information from the vegetation growing characteristics. The vegetation growing characteristics have great potential to capture the temporal variations of FVC and, thus, can provide complementary information to improve the FVC estimation accuracy. In this paper, a dynamic Bayesian network method was proposed to estimate FVC from Moderate Resolution Imaging Spectroradiometer (MODIS) reflectance data through combining a radiative transfer model and a statistical crop growth model, which could synthetically use information from both remote sensing data and crop growing characteristics. The performance of the proposed method was investigated in a cropland area of the Heihe River Basin, covering the whole growing season of maize in 2012. The time series field FVC data were quantitatively measured using digital photography and then used to generate high-spatial-resolution FVC maps using the Advanced Spaceborne Thermal Emission and Reflection Radiometer and Compact Airborne Imaging Spectrometer data for evaluating the accuracy of FVC estimates from MODIS data. The validation results showed a satisfactory performance with a coefficient of determination R2of 0.956 and a root-mean-square error (RMSE) of 0.057, as compared with the performance ( R2= 0.817, RMSE = 0.106 ) of the FVC estimates using the lookup table method, which utilized the information from remote sensing data. These results indicated that the proposed method could effectively utilize the vegetation growth information and achieve reliable FVC estimates in the cropland area.

Maize is a widely planted crop in China and in other areas of the world and plays an important role in grain production. Monitoring the growth status of maize using remote sensing technology is an important component of precision agriculture and height, as a crucial growth indicator for maize, can be retrieved from light detection and ranging (LIDAR) data. However, height extraction for crops, such as maize using airborne laser scanning point clouds results in a great number of uncertainties and challenges. Here, airborne full-waveform LIDAR data were used to extract maize height. In the first step, a workflow was designed based on the Gold deconvolution algorithm combined with a basic data process technique. The method was then tested and was determined to be effective for capturing the portion of the waveform interacting with the tops of vegetation, characterized by lower amplitude stemming from the ground. Therefore, the number of second returns from point clouds was dramatically increased. During the experiment, the number of point clouds increased nearly 50% for three of the four maize plots, as compared with the original point clouds. Compared with the commonly used Gaussian fitting algorithm, the deconvolution algorithm had the advantage of extracting an accurate position for overlapping weak signals. The height percentiles indicated that the original and Gaussian decomposition derived point clouds data underestimated and deconvolution algorithm can accurately reflect the true height of maize, particularly for the 75% and 95% height percentiles.

Fractional vegetation cover (FVC) plays an important role in earth surface process simulations, climate modeling, and global change studies. Several global FVC products have been generated using medium spatial resolution satellite data. However, the validation results indicate inconsistencies, as well as spatial and temporal discontinuities of the current FVC products. The objective of this paper is to develop a reliable estimation algorithm to operationally produce a high-quality global FVC product from the Moderate Resolution Imaging Spectroradiometer (MODIS) surface reflectance. The high-spatial-resolution FVC data were first generated using Landsat TM/ETM+ data at the global sampling locations, and then, the general regression neural networks (GRNNs) were trained using the high-spatial-resolution FVC data and the reprocessed MODIS surface reflectance data. The direct validation using ground reference data from validation of land European Remote Sensing instruments sites indicated that the performance of the proposed method (R2=0.809, RMSE =0.157) was comparable with that of the GEOV1 FVC product (R2=0.775, RMSE =0.166), which is currently considered to be the best global FVC product from SPOT VEGETATION data. Further comparison indicated that the spatial and temporal continuity of the estimates from the proposed method was superior to that of the GEOV1 FVC product.

Opium is a narcotic obtained from opium poppy and is the raw material of heroin for the illegal drug trade. Monitoring the illegal concentrated cultivation of opium poppy in major regions is critical for the understanding by governments and international communities of the scale of illegal drug trade. This paper investigates whether opium poppy can be discriminated from its coexisting plants using analytical-spectral-device field spectrometer data in the visible to short-wave infrared spectral range. Canopy spectral measurements were conducted during three different growth periods of opium poppy. A synthetic method with three analysis levels was applied to discriminate opium poppy from other species and to select optimal bands for opium poppy discrimination. First, the Mann-WhitneyU-test method was used to test the spectral reflectance difference between opium poppy and coexisting crops at each wavelength. Then, the Jeffries-Matusita distance and band correlation analysis were conducted to select the optimal wavebands for discriminating opium poppy using the significant wavebands from the test results. Finally, classification and regression tree analysis was employed to validate the classification accuracy based on the selected optimal wavebands. The results indicated that the spectral reflectance of opium poppy was significantly different from that of coexisting crops in many surveyed wavebands, and opium poppy could be discriminated using a field survey spectrum at canopy level. The best time for discriminating opium poppy from coexisting crops was around flowering time. This paper provided the prerequisite for monitoring opium poppy using satellite remote sensing data in some regions of concern.