Full-waveform hyperspectral LiDAR (HSL) provides multi-channel echo signals that capture both geometric and spectral information. However, the instrument’s optical system introduces challenges like inter-channel time delay effects and multi-channel response heterogeneity, leading to uncertainties in HSL waveform parameter extraction and quantitative applications. Therefore, we propose a Dynamic Warping Fusion (DWF) method for HSL signal correction and decomposition. The DWF method incorporates derivative dynamic time warping (DDTW) for multi-channel signal alignment and panchromatic band waveform synthesis, eliminating uncertainties in target position extraction. Subsequently, inverse DDTW is applied to generate unique initialization parameters for each channel, reducing the risks of missed detection and over-segmentation. The results suggest that (1) the DWF method demonstrates excellent decomposition performance on simulated datasets, exhibiting robustness across varying inter-channel time delay levels (R² > 0.97, RMSE < 0.24); (2) the synthesized panchromatic band demonstrates superior spatial information extraction capabilities, with an average relative neighbor distance error (RNDE) < 2.51%; (3) a new multiband LiDAR spectral similarity index (MLSI) is introduced, showing that spectral curves based on waveform integrated areas are closer to the true spectrum than those based on peak intensities; (4) performance validation on measured datasets confirms the superiority of DWF over the previous MIWD approach, reducing RNDE from 5.11% to 2.27%. The proposed approach leverages waveform shape features for inter-channel time delay correction and waveform decomposition, provides a valuable reference for signal decomposition across various HSL devices and similar data processing tasks.

The Sustainable Development Science Satellite-1 (SDGSAT-1) is a new-generation night-light satellite equipped with an advanced Glimmer Imager (GLI) sensor capable of acquiring multispectral, high-resolution night-time light (NTL) images. It has been widely applied to various fields, yield valuable insights in supporting human activity monitoring and sustainable development research. However, the Level 4A GLI images still suffer from spatial misalignment issues, including seam misalignment between the images captured by different cameras in the same scene; registration errors between images and basic geographic information data (i.e. OpenStreetMap road networks) and spatial inconsistencies between the panchromatic (Pan) and RGB bands. The existing studies mainly relied on manual registration or algorithms to mitigate misalignment effects, lacking efficient and systematic solutions. To effectively address this issue, in this work, an automatic geometric registration algorithm called road-guided image registration (RGIR), which uses the OSM road network as a spatial reference. Ground control points (GCPs) are selected via a three-step process: point match, line match and precise match. The RGIR algorithm could achieve high-precision registration and effectively correct the three typical spatial misalignment problems commonly found in GLI images. The experimental results demonstrated that RGIR can achieve sub-pixel accuracy across various scenarios and exhibits strong robustness. Overall, our work provides a workable technical approach to solving the existing spatial misalignment problem in GLI images, laying a solid foundation for its reliable application in multi-temporal analyses and large-scale sustainable development analyses.

Full-waveform hyperspectral LiDAR (HSL) generates comprehensive hyperspectral waveforms for scenes to reveal the shape and spectral heterogeneity of multiple natural targets. Nevertheless, current waveform processing methods are primarily designed for single-wavelength LiDAR systems, resulting in a shortage of methods tailored for full-waveform HSL data processing and in a restriction to further quantitative applications for HSL. This study is designed to extract targets’ physical and spectral characteristics by integrating spectral-dimension features into the HSL waveform processing. The core idea of the method involves a rigorous processing technique consisting of parameter initialization, parameter optimization, and re-optimization over calculating the median (M) after ranking central locations of natural target echoes (Rclonte). The medians in the re-optimization step serve as the reference parameter sets for supplementing the hidden or weak components at some wavelengths for HSL. Two groups of datasets, the simulated and measured datasets, were utilized to evaluate the component detection ability of the proposed Rclonte-M method. The results suggest that the Rclonte-M method demonstrates excellent component detection performance on both simulated and measured data, outperforming the multispectral waveform decomposition (MSWD) method. The HSL system designed by us owns an overall ranging error of about 7 cm for adjacent components, with the relative neighbor distance error (RNDE) limited to 0.160. Besides, spectra retrieval results from HSL easily distinguish the natural targets along the laser path. This study enriches the full-waveform HSL data processing algorithm library and could be considered in other full-waveform HSL systems and the simulated airborne or space-borne HSL waveforms. Codes are freely available on https://github.com/Jie-Bai/Rclonte-M-TGRS.

Landsat time series, as the longest fine resolution dataset, has the most significant limitation of relatively low temporal frequency (16 days). However, the presence of clouds, cloud shadows, snow, and the failure of the sensor further reduces the amount of clear data, resulting in insufficient observations, which can easily lead to biases in statistical metrics (i.e., maximum, mean, and percentiles). In this study, we took advantage of Google Earth Engine (GEE) and proposed a Statistical Time-series biAs Modification Model (STAMM) that can generate real Landsat statistical metrics (i.e., Landsat statistical metrics based on sufficient observations) in large regions. STAMM can also quantitatively evaluate the bias of statistical metrics due to insufficient observations. The results show that the original Landsat NDVI 75th percentiles over both the northeast and northwest of China overestimated about 10%–30% compared with real Landsat percentiles, while in the southwest of China, it underestimated about 10%. This issue has persisted for the past 20 years. As for other percentiles, the amount of bias depends on the data distribution. The bias of the 95th percentile is relatively small when there are more clear observations in the high-value period. However, at this time, the bias of the 50th percentile bias is relatively large. Taking the Sentinel percentiles as references, STAMM effectively improves the accuracy of Landsat percentiles (i.e., the root-mean-square error (RMSE) decreased from 0.065 to 0.045). It is supposed to be able to provide consistent and accurate Landsat percentiles in large region and long time series for better studying the interannual change of Earth’s surface.

Hyperspectral light detection and ranging (LiDAR) (HSL) can be used to acquire backscattered full-waveform data with abundant spectral information, providing a new technique for the remote sensing of plant 3-D properties monitoring. HSL 3-D point cloud of twoKniphofia uvariaplants was acquired by HSL scanning and data processing. Then, chlorophyll concentration could be retrieved at any 3-D position using a constructed partial least squares regression (PLSR) model. At the same time, the vegetation canopy structural parameters (canopy height, maximum canopy width, and projected canopy area) were retrieved based on the geometric information of the HSL data. The results of this study showed that the HSL system could be used to realize the simultaneous extraction of both the 3-D biochemical and structural parameters, which has great potential in the field of quantitative remote sensing.

As a novel remote sensor, hyperspectral LiDAR is faced with the incidence angle effect, which restricts its quantitative applications. However, the current radiometric correction algorithms have some limitations, concentrating on: 1) the mathematically polynomial fitting; 2) adjacent wavelength ratio such as ratio vegetation index; and 3) perfect Lambertian assumption and using the Lambert cosine law to correct the effect. In this study, a practical and proper correction algorithm is proposed to overcome these limitations. First, to better characterize the complex reflection characteristics of the object surface, a combination of the Lambert law and Beckmann law is applied to represent the object surface. Then, it considers the impact of both wavelength and incidence angle on describing the surface roughness factor and diffuse fraction. Finally, a modified and physically based radiometric correction algorithm is generated. It provides the detailed correction equations for intensity and reflectance data recorded by hyperspectral LiDAR. To obtain its parameters and verify its reliability, leaves (ten samples per species, 30 in total) were stochastically collected from three broadleaf trees to experiment. The results showed that the algorithm achieved good performances by comparing the intensity and reflectance changes before and after removing the leaf incidence angle effect. Since it is physically based, the algorithm could promisingly be a fundamental solution to eliminate the incidence angle effect for hyperspectral LiDAR.

The detection of vertical heterogeneity in vegetation has attracted an increasing attention as it has a great significance for precise agriculture. The hyperspectral light detection and ranging (LiDAR) (HSL) can obtain the spectral and spatial information simultaneously. However, its ability to monitor the vertical distribution of biochemical parameters in plants has not been fully explored. In this article, the applicability of empirical ratio and normalized spectral indices for HSL channels in chlorophyll (Chl) detection was investigated using three data sets: the PROSPECT-5 synthetic data set, the ANGERS public data set, and an HSL-measured data set. A linear regression model of the best performing index against measured Chl values was constructed so as to build 3-D Chl point clouds of maize. The performance of HSL in Chl detection at the upper and lower layers was also tested based on the selected spectral index. The result showed that the CIred edge index was most compatible with the HSL channels. The estimated Chl concentrations of the upper and lower layers showed the close relationships with HSL measurements (R2= 0.73 and 0.91, respectively). The vertical Chl profiles in maize were also presented, indicating that the HSL system has a strong ability to monitor the vertical distribution of maize Chl concentrations. This article provides a basis for the vertical detection of vegetation biochemical parameters directly from HSL measurements.

The vertical distribution of plant physiological composition plays an important role in vegetation growth and carbon stock. The hyperspectral LiDAR was thought to be the most promising solution to assess the vertical distribution of vegetation physiological parameters, such as LAI (Leaf Area Index) and chlorophyll content. However, the instrument was not fully feasible, so the simulation and experiment of its response to these parameters was necessary. In this paper, the hyperspectral LiDAR waveform was simulated with the consideration of single scatter of plant radiative transfer model. The variation of LAI and chlorophyll along plant height was investigated and tree scenarios were assumed in the simulation. The hyperspectral lidar data experiment was also carried out to validate the simulation and method. The result showed that the hyperspectral LiDAR waveform could reflect the variation of plant physiological composition and facilitate the inversion algorithm design and it could play a significant role in parameter estimation and precision agriculture application in future.

Most crop classification work use the ground reference data to training the classifier; but sometimes, the ground reference data cannot be obtained. In this paper, we tried to use the NDVI time series obtained during 2006 and 2013 to classify crop types in 2014 at 30 m spatial resolution. The experiment was conducted in Southeast Kansas, USA. Firstly, we extracted the NDVI time series using ground reference data between 2006 and 2013 from MODIS NDVI time series. Then, the composed Landsat NDVI data were transformed to MODIS NDVI using the linear correlation between the two data sets. Next, Random Forest (RF) was employed to classify crop types at 30 m resolution. The result showed that this procedure could accurately identify the major crops in the study area as the overall accuracy was 92.22% and the Kappa coefficient was 0.8758. In addition, two subsets of the study area showed that the result obtained in this study was similar to that of Crop Data Layer (CDL) provided by National Agricultural Statistics Service (NASS). Thus, the method proposed in this study could be an alternative way for crop classification when ground reference data cannot be acquired.

Soil respiration (Rs) is a major CO2flux within terrestrial ecosystems. This study examine the feasibility of applying HJ-1 CCD data to analyze the growing-season variations of Rsin two irrigated cropland ecosystems. At growing season time scale, crop biophysical parameters, such as leaf chlorophyll content (Chlleaf) and green leaf area index (GLAI), explained most Rsvariation in the maize and winter wheat fields. Among the selected vegetation indices from HJ-1 CCD data, enhanced vegetation index (EVI) and green chlorophyll index (CIgreen) showed stronger correlations with Chlleaffor maize or GLAI for winter wheat than normalized difference vegetation index (NDVI). Moreover, for both fields, the relationships between daily mean Rsand either EVI or CIgreenwas consistently stronger than the relationship between daily mean Rsand NDVI.

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.

A multispectral full-waveform light detection and ranging (LiDAR) instrument prototype with four wavelengths and a supercontinuum laser as a light source was designed to monitor the fine structure and the biochemical parameters of vegetation. Components of the instrument included a 2-D scanning platform, a supercontinuum laser source, a receiving optical system, and a multichannel full-waveform measurement module. The LiDAR instrument can simultaneously measure multichannel-returned full-waveform laser signals. Position information in the recorded waveform allowed us to compute the distance from the target, whereas the intensity of the signal provided the spectral reflectance. Performance for the measuring distance and the spectrum was evaluated. Experiments indicated that the instrument has high measurement accuracy and has the ability to detect the biochemical characteristics of vegetation via construction of the normalized difference vegetation index and the photochemical reflectance index. The experiment also indicated that the instrument has the potential to generate spectral 3-D point clouds. Therefore, the instrument could play a significant role in detecting the vertical distribution of structural and biochemical characteristics of vegetation.

A new GPP/NPP model driven by the satellite SAR data was introduced in this paper. It was based on the light use efficiency theory and was referred to the MODIS model for the value of the maximum light use efficiency of different vegetation types. The model was testified in the HEIHE area with ENVISAT-SAR data and showed its feasibility to estimate GPP/NPP. Firstly, the driving factors such as PAR, T_scalar, W_scalar were calculated based on the algorithm and meteorological observation data. Then, the LUT algorithm using the MIMICS model was introduced and validated for its effectiveness to estimate LAI. Finally, the GPP was obtained based on the model and compared with the ground flux observation and MODIS product. The results reveal a potential possibility that the satellite SAR data could be used for the GPP/NPP estimation.

A method based on radiative transfer models (RTM) incorporating multi-angle data is used to estimate LAI. The estimation of LAI from inversion will be based on Look Up Tables (LUT) approach. The usefulness of the method was verified using the data of Spectral database of Chinese typical features. The determination coefficient R2between estimated result and measurement data is high to 0.6046. The outcome of the method in this article is acceptable.

It is vital to reconstruct a three-dimensional terrain by a few sparse points in the digital elevation model (DEM). However, the reconstructed surfaces from elementary functions(e.g. lines and cubics) appear too smooth to represent a terrain with self-affinity property. The conventional fractal reconstruction methods are time-consuming and require too many parameters to set. In this paper we propose a probability-based method to speed up the fractal interpolation execution. In addition, the topography curvature is used to determine contraction factor indirectly in the study. The experimental results indicate that the proposed scheme is excellent in computational efficiency and features preserving.

The effect of crop leaf angle on the canopy-reflected spectrum cannot be ignored in the inversion of leaf area index (LAI) and the monitoring of the crop-growth condition using remote-sensing technology. In this paper, experiments on winter wheat (Triticum aestivumL.) were conducted to identify the crop leaf angle distribution (LAD) by two-temporal (erecting and elongation stages) and bidirectionalin situreflected spectrum and the Airborne Multiangle Thermal Infrared (TIR) Visible Near-Infrared (VNIR) Imaging System (AMTIS) images. The distribution characters of the leaf angle for different LAD varieties were expressed using the beta-distribution function and the SAILTH radiative transfer models. The proportion of the leaf angle in 5deg angle classes (from 5deg to 90deg) for erectophile, planophile, and horizontal varieties was dominated by 75deg, 55deg, and 35deg. The different LAD varieties had a similar canopy reflectance in 680 nm (red) and 800 nm (near-infrared band) at the erecting stage, while they had significant differences at the elongation stage. The ratio of the canopy reflectance of 800 nm at the erecting stage [R800(B)] to the canopy reflectance of 800 nm at the elongation stage [R800(A)] was used to identify the different LAD varieties through the selected two-temporal canopy reflectance. A method based on the semiempirical model of the bidirectional reflectance distribution function (BRDF) was also introduced in this paper. The structural parameter-sensitive index (SPEI) was used in this paper for crop LAD identification. SPEI is proved to be more sensitive to identify erectophile, planophile, and horizontal LAD varieties than the structural scattering index and the normalized difference f-index. We found that it is feasible to identify horizontal, planophile, and erectophile LAD varieties of wheat by studying two-temporal and bidirectional canopy-reflected spectrum

Leaf biochemical concentrations influence both leaf and canopy spectra at their absorption bands. Leaf scale model PROSPECT, canopy scale model SAIL and their coupled model PROSAIL are used to generate simulated reflectance an transmittance spectra to study the influence of biochemical concentrations. Three biochemical input variables of PROSPECT model, concentration of chlorophyll, leaf water and dry matters, are studied in this paper. Figures show that their influences on both leaf reflectance spectra and canopy reflectance spectra are similar. The influence of chlorophyll is located at visible region, leaf water at near infrared region especially centered at 1400 nm and 1900 nm, and dry matters at near infrared region especially at some reflectance peaks. Reflectance at canopy scale is generally less than that at leaf scale. Other non-biochemical factors also influence the spectra at both scales and some of them may cover biochemical signals in canopy reflectance spectra to some extent.

Remote sensing and GIS technologies were integrated to estimate runoff for Binjiang basin, China. Remotely sensed images from the Landsat satellites were used to develop land cover maps of the study area for the years 1990, 1995 and 2000. GIS analysis, based on land-cover and soil map data, was used to estimate SCS curve numbers on a 30-meter grid and to compute runoff depths for a 10 -year maximum rainfall event. Runoff coefficients were computed by the Rational method for each year of the study. Temporal changes in spatial distribution of land cover, runoff coefficients, and runoff volume were estimated. A runoff hydrograph, based on the above information and digital elevation model, was developed for the study area. The land cover for the study region showed an increase in urban area and a corresponding decrease in vegetation area. A increase in modeled runoff volume and peak flow is attributed to this change in land cover. The results of this study indicate a need for protecting vegetation to reduce the effect of floods in this basin.

This paper examines physical bases for biochemical prediction with hyperspectral data. A distinctive absorption feature centered at 2100 nm in dry leaves is focused on. Continuum removal is employed to the original reflectance spectrum from 2030 nm to 2220 nm to eliminate the influence of background. After comparing some representative samples selected from NASA ACCP data set, two features due to different carbon and nitrogen concentrations are discovered in the continuum-removed spectra. (1) The continuum-removed spectrum curve from 2030 nm to 2050 nm are nearly straight, of which the slope is highly correlated with the carbon concentration. (2) An inflexion located at 2054 nm indicates the nitrogen concentration, which is caused by an absorption pit of protein. The slope change is obvious when the nitrogen concentration is high. Both of the features are caused by essential and unique absorption features of cellulose, lignin and protein in this region, which make them physically based and widely applicable.

Vegetation chlorophyll is a key indicator in ecosystem. Operational chlorophyll content estimation models by remote sensing are needed. In this paper, some modifications were made to an existing semi-empirical chlorophyll content estimation model, a new estimation model was given and validation results are promising

Taking place and development of desertification in the arid and semiarid regions directly influence the density and growth status of vegetation, making surface vegetation a most important indicator for desertification assessment. The primary purpose of this study was to assess the condition of desertification in central Asia and western China located in arid and semiarid regions. Remote sensing data used in this study were a time-series of 10-day maximum Normalized Difference Vegetation Index (NDVI) composites derived from Global Area Coverage of Advanced Very High Resolution Radiometer (AVHRR) from 1982 to 2000. The coefficient of variation (CoV) of the monthly NDVI (maximum-value composite) was used as a parameter to characterize the changes of vegetation in this work. The CoV can be used to compare the amount of variation in different sets of sample data. Changes in the value of the pixel-level CoV over time can be interpreted as a measure of vegetative biomass change over that time. The method to detect and quantify changes in CoV values for each pixel over a 20-year period for which data were available is based on linear regression. If the CoV values exhibit a statistically significant decrease over time, it is possible to conclude that the area imaged in that pixel is under desertification. The result was validated by comparison of the theoretical results to land cover maps in different years. This experiment demonstrated the feasibility of applying the CoV regression methodology and long term NOAA-AVHRR NDVI time-series data for desertification monitoring in central Asia and western China.

Crop water requirement was important in the irrigation scheduling. The crop coefficient is a parameter for estimating crop water requirement by multiplying with the reference crop evapotranspiration. Crop coefficient used to be approximated by crop developing days. The method must have some defect because the crop coefficient is a parameter related to crop status, climate condition and surface albedo. All the factors relating to the crop coefficient are spatially diverse and remote sensing has advantages in obtaining the distributing parameters for vegetation and climate factor. Based on the Penman-Monteith equation, the reference crop evapotranspiration and potential evapotranspiration for cotton under different growth status was estimated with measured meteorological data, then the crop coefficient for cotton was retrieved from a Landsat ETM+ image. And the sensitivity of crop coefficient to the influence factors were analysed. The results showed that the crop coefficient retrieved from the ETM+ image was greater than those suggested by FAO and the crop coefficient was influenced and decided by NDVI that represents crop growth status, while surface albedo that has a very larger variance for the sparse vegetation cover has scarcely any effect on crop coefficient and the climate factors has litter influence on crop coefficient too; with the vegetation cover fraction developing, the climate factor has a much more positive effect on the crop coefficient.

Net ecosystem productive (NEP) is defined as the net carbon dioxide flux to or from an ecosystem without natural or human disturbance, and integrates all ecosystems carbon sources and sinks: NEE=GPP-Ra-Rh. Here NPP was calculated with the light use efficiency model based on NOAA/AVHRR, and the soil respiration of the corresponding ecosystem came from references. On the whole, the NEP shows the terrestrial ecosystem in China is a carbon sink though there are great uncertainties. The geographic gradient of the NEP clearly shows more correlation with temperature on latitude gradient, and with precipitation on longitude. Three main sources of uncertainties were analyzed: (1) land cover classification based on remote sensing; (2) NPP modelling; (3) soil respiration modelling. The observation and modelling integrated with remote sensing will be a very important solution to the carbon source/sink at large spatial sale.

This paper will investigate the effectiveness of the genetic algorithm evolved neural network classifier and its application on the land cover classification of multi-spectral remotely sensed imagery. First, the key issues of the algorithms and the procedures are described in detail. Second, SPOT XS imagery is employed to evaluate its accuracy. Traditional classification algorithms, such as maximum likelihood classifier, back propagation neural network classifier, are also incorporated for a comparison purpose. Based on an evaluation of the user's accuracy and kappa statistic of different classifiers, the superiority of applying the discussed genetic algorithm-based classifier for land cover classification using multi-spectral imagery data is established. Finally, some concluding remarks and suggestions are also presented.