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.

Aboveground biomass (AGB) of rice is crucial for monitoring growth and predicting yields. While deep learning algorithms, such as deep convolutional neural networks (DCNNs), show compelling performance in estimating crop parameters, gathering sufficient ground-truth samples for model training poses a significant challenge, leading to the “small sample problem.” To address this, we propose a framework that utilizes a hybrid inversion model based on the PROSAIL-PRO radiative transfer model (RTM) combined with machine learning techniques [XGBoost and random forest (RF)]. This framework incorporates active learning optimization and the spectral angle mapper (SAM) method to select simulated samples that closely match real-world conditions, simultaneously assigning geographic location information to the samples. Using these qualified samples, we constructed both single-branch and multibranch DCNN models that integrate uncrewed aerial vehicle (UAV)-based hyperspectral principal components (PCs), canopy height (CH) information from the canopy surface model (CSM), and canopy temperature derived from thermal infrared (TIR) images. The effectiveness of this approach was validated across two experimental sites. The single-branch DCNN achieved the highest accuracy at site A ($R^{2} =0.816$and root-mean-square error (RMSE) =61.608 g/m2) with PCs, TIR, and CSM as inputs, while the multibranch DCNN performed best at site B ($R^{2} =0.784$and RMSE =65.533 g/m2), using PCs and TIR as inputs. Results indicate that simulated samples have considerable potential for practical applications. PCs were the primary contributors to the model, with TIR playing a more significant role than CSM. Overall, this study demonstrates high-precision estimation of rice AGB despite limited measured samples, offering valuable insights for crop monitoring under small sample conditions.

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.

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.

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.