Jinxiu Liu

Canopy directional reflectance (CDR) models are critical for linking remote sensed signals to vegetation biophysical parameters. The stochastic radiative transfer (SRT) model, balancing the efficiency of one-dimensional (1D) models with the accuracy of three-dimensional (3D) models, excels at simulating both continuous and discontinuous canopy structures. However, existing SRT studies focus primarily on flat surfaces, neglecting the impact of topographic relief on radiative transfer processes. To address this issue, we propose a Topographic Stochastic Radiative Transfer (TopoSRT) model for sloping terrain with discontinuous canopies based on SRT theory. First, the TopoSRT model is constructed by implementing coordinate system transformation between flat and sloping terrain, modifying key variables such as the conditional pair‐correlation function (PCF) and leaf‐angle distribution (LAD) function, and incorporating the geotropic growth of vegetation. Second, the topographic effects on the model outputs and intermediate variables are comprehensively evaluated. The findings reveal that terrain exerts a wavelength-specific influence on radiative transfer, whereas several key parameters including PCF and recollision probability are spectrally invariant yet vary regularly with terrain. Finally, the new model is validated using bidirectional reflectance simulated by a 3D-RT model and field-measured using an unmanned aerial vehicle (UAV). For different slopes and canopy types, the coefficients of determination (R²) between TopoSRT and computer simulations are all ≥ 0.85, with R² reaching 0.94 for the near-infrared (NIR) band and 0.97 for the red band in spherical canopies, significantly outperforming the SRT model with flat-terrain assumption. The root mean square errors (RMSE) are 0.063 and 0.068 for the NIR band, and 0.012 and 0.017 for the red band at two UAV observation sites, respectively. We note that the TopoSRT model extends the application of SRT theory to sloping terrain, offering a more reliable modeling tool for quantitatively retrieving vegetation parameters including the leaf area index (LAI) in mountainous ecosystems.

Timely and accurate wildfire detection is critical for ecological monitoring and disaster response. Using a model to handle both long-term time series and multi-scale spatial features is challenging. In this study, we propose a novel burned area detection algorithm based on the CL-UNet (ConvLstm-U-Net) model. CL-UNet integrates convolutional long short-term memory (ConvLSTM) units with a multi-scale encoder-decoder architecture to capture both spatial and temporal dependencies inherent in satellite image time series. The model is trained to forecast spectral index values for the target year, and discrepancies between the predicted and observed values are used to identify anomalous changes. Burned areas are detected using a root mean square error (RMSE)-based thresholding method, which provides a robust mathematical foundation for selecting the optimal threshold. Using MODIS products with a temporal coverage spanning up to 9 years, we constructed spectral index time series (SITS) based on multiple spectral indices. Experimental results from two large-scale forest fire demonstrate that Burn Area Index (BAI) exhibits the most qualified and stable performance. Under various conditions, the Recall, Precision and F1 score of the model using the BAI time series as input data all exceed 80%. The results further indicate that CL-UNet achieves outstanding performance in both study areas. When utilizing the BAI time series as input, the Recall, Precision and F1 score of CL-UNet are 1% to 10% higher than those of other models. The proposed algorithm shows strong potential for general spatiotemporal disturbance detection tasks in remote sensing applications beyond wildfire monitoring.

The Three-North Shelter Forest Program (TNSFP) is a large-scale ecological restoration project that has attracted worldwide attention. It covers 4.069 million km² across 13 provinces in northern China, including northwestern, north-central, and northeastern regions. Bibliometric analysis provides a structural overview of the research in this field and offers insights into key research fronts. We conducted a literature review of the Web of Science Core Collection (WoSCC) from 1990 to 2024 using HistCite for a comprehensive literature analysis and CiteSpace for visualizing research trends and co-citation networks. Based on the literature data from the WoSCC, we performed a bibliometric visualization review of the TNSFP. We observe a rising trend in research on the TNSFP, with the number of publications steadily increasing, especially after 2011. Remote Sensing emerged as the leading journal during the study period, accounting for 8.84% of the total publications. China is the leading contributor to research in this field, comprising 99.32% of the publications, with the Chinese Academy of Sciences (CAS) being the primary research institution, accounting for 36.05%. Research on the TNSFP is interdisciplinary, with Environmental Sciences serving as its primary focus. Ecological restoration and climate change are likely to be the main trends in future research. This study provides a comprehensive overview of the TNSFP’s research landscape, offering insights that can inform policy decisions, guide future research directions, and support on-the-ground conservation and afforestation strategies.

As an important component of terrestrial ecosystems, mountain vegetation serves as an indicator of climate change. Due to the sensitivity of the Tibetan Plateau Mountains (TPM) to climate change and their ecological fragility, their vegetation dynamics (greenness and greening) have become a hot spot issue in global environmental change. Topography is a relatively stable environmental factor that shapes vegetation by creating localized microenvironments. However, existing research primarily focuses on the effects of climate change and human activities on vegetation dynamics. Therefore, a more comprehensive understanding of the dependence of vegetation dynamics on topography is needed. To elucidate the relationship between topography and the spatial heterogeneity of vegetation dynamics, we conducted this study using the recently released high-precision Sensor-Independent Leaf Area Index product. Through long-term trend analyses and joint comparisons of multiple topographic variables, this study elucidates key patterns: (1) North-facing slopes exhibit higher vegetation greenness and stronger greening trends than south-facing slopes, whereas east- and west-facing slopes show comparable greenness but stronger greening on west-facing slopes. (2) Vegetation greenness and greening increase with slope steepness. (3) With increasing elevation, greenness decreases progressively, while greening follows a unimodal pattern—initially increasing, then decreasing, and nearing zero at high altitudes. These findings underscore the pivotal role of topography in regulating vegetation responses to climate change. This study provides new insights into the interplay between topography and vegetation dynamics, advancing our understanding of ecological processes on the TPM and informing strategies for ecosystem management under global warming.

Soil moisture (SM) is evidenced to dominate the interannual variability and trend of regional gross primary production (GPP) in the context of increasing drought and heat extremes, yet only a few light-use efficiency (LUE)-based GPP models consider SM stresses in modeling practice. This study utilized high-resolution GPP observational data collected from 16 flux tower sites in the US and Europe, integrating soil moisture and vapor pressure deficit (VPD) data to optimize the parameters of two typical LUE models (TL-LUE and VPM) and perform sensitivity analyses to assess the impact of SM and other moisture indicators on model performance. Our findings reveal that incorporating soil moisture (SM) significantly enhances GPP simulations, particularly in grassland ecosystems, where SM greatly improves model performance. However, in water-stressed forests, alternative indicators like VPD proved more effective, highlighting the challenges of modeling GPP in these ecosystems and the need for refined approaches. The results underscore the importance of adopting ecosystem-specific strategies when enhancing LUE models to better capture the impacts of water stress. This study provides valuable insights into improving GPP simulations under increasing droughts and climate change, emphasizing the necessity of tailored approaches for different ecosystem types.

The Fraction of Absorbed Photosynthetically Active Radiation (FPAR) is essential for assessing vegetation’s photosynthetic efficiency and ecosystem energy balance. While the MODIS FPAR product provides valuable global data, its reliability is compromised by noise, particularly under poor observation conditions like cloud cover. To solve this problem, we developed the Spatio-Temporal Information Composition Algorithm (STICA), which enhances MODIS FPAR by integrating quality control, spatio-temporal correlations, and original FPAR values, resulting in the High-Quality FPAR (HiQ-FPAR) product. HiQ-FPAR shows superior accuracy compared to MODIS FPAR and Sensor-Independent FPAR (SI-FPAR), with RMSE values of 0.130, 0.154, and 0.146, respectively, and R² values of 0.722, 0.630, and 0.717. Additionally, HiQ-FPAR exhibits smoother time series in 52.1% of global areas, compared to 44.2% for MODIS. Available on Google Earth Engine and Zenodo, the HiQ-FPAR dataset offers 500 m and 5 km resolution at an 8-day interval from 2000 to 2023, supporting a wide range of FPAR applications.

Land subsidence is a geological process that occurs at a slow and persistent pace. However, due to the limited archive of high-resolution SAR images, the single-sensor Multi-temporal Interferometric Synthetic Aperture Radar (MTInSAR) technology is unable to satisfy the demands of long-term and high-resolution deformation monitoring. To address this issue, we employed a Small Baseline Subset of InSAR technology, using ALOS-2 and Sentinel-1 data, to detect the deformation characteristics of the Beijing Subway Network (BSN). The two datasets produced consistent results regarding the evolution process and spatial distribution. Subsidence monitoring can be sustained using other datasets when high-resolution images are insufficient. To investigate the differences in deformation characteristics at different stages, we focused on the newly opened section of Beijing Subway Line 17 (L17). Our findings revealed that the southern portion of L17 posed a significant risk of instability with subsidence occurring at Jiahuihu Station, Ciqu Station, and Ciqubei Station. The deformation at these stations was 12.8 mm, 18.7 mm, and 5.6 mm, respectively. Before and after the opening of L17, their deformation rates changed. Specifically, the deformation rate at Jiahuihu Station decreased from -9.4 mm/year to -5.5 mm/year, the deformation rate at Ciqu Station decreased from -14.1 mm/year to -7.5 mm/year, and the deformation rate at Ciqubei Station decreased from -4.9 mm/year to -1.5 mm/year. These results indicate that, after the subway's opening, the deformation rates at these stations are decreasing. The deformation along the subway will intensify during construction and then gradually stabilize once it is operational.

The evaluation and validation of Climate Data Records (CDRs) derived from remote sensing play crucial roles in their generation and applications. However, many existing evaluation schemes rely on simplistic, spatiotemporally invariant metrics to assess the products’ overall quality, which leads to the long-term neglect of intra-product inconsistencies stemming from observation conditions, algorithmic differences, and sensor degradation. Leaf Area Index (LAI) is a crucial variable for land surface and climate modeling, and the intra-product inconsistency will increase the uncertainty in related studies. In order to improve the evaluation scheme of LAI products and ensure their reliability, we propose a new perspective for evaluating global LAI time series. In this study, we utilize the MODIS C6.1 LAI product as an example to infer its internal consistency through cross-comparisons among different sensors and spatiotemporal correlations between two adjacent years of the product. We found that compared to the main algorithm, the backup algorithm of the MODIS LAI product tends to underestimate the retrieval results. This inconsistency is particularly pronounced in tropical regions but relatively minor in most other areas. Additionally, these inconsistencies can lead to unusual fluctuations in the LAI time series, impacting the magnitude and direction of short-term vegetation monitoring. However, the influence on long-term trend analyses are negligible. Therefore, special attention should be given to the intra-product consistency in certain studies. In conclusion, the evaluation perspective proposed in this study is of great significance for improving the LAI evaluation scheme and ensuring the use and improvement of remote sensing products.

Southwestern North America (SWNA) continuously experienced megadroughts and large wildfires in 2020 and 2021. Here, we quantified their impact on the terrestrial carbon budget using net biome production (NBP) estimates from an ensemble of atmospheric inversions assimilating in-situ CO₂ and Carbon Observatory–2 (OCO-2) satellite XCO₂ retrievals (OCO-2 v10 MIP Extension), two satellite-based gross primary production (GPP) datasets, and two fire CO₂ emission datasets. We found that the 2020–2021 drought and associated wildfires in SWNA led to a large CO₂ loss, an ensemble mean of 95.07 TgC estimated by the satellite inversions using both nadir and glint XCO₂ retrievals (LNLG) within the OCO-2 v10 MIP, greater than 80% of SWNA’s annual total carbon sink. Moreover, the carbon loss in 2020 was mainly contributed by fire emissions while in 2021 mainly contributed by drought impacts on terrestrial carbon uptake. In addition, the satellite inversions indicated the huge carbon loss was mainly contributed by fire emissions from forests and grasslands along with carbon uptake reductions due to drought impacts on grasslands and shrublands. This study provides a process understanding of how some droughts and following wildfires affect the terrestrial carbon budget on a regional scale.