Fractional Vegetation Cover (FVC) is a key biophysical parameter for characterizing vegetation dynamics and ecosystem functioning, and is essential for ecological monitoring in arid and semi-arid regions. However, sparse vegetation and strong soil background effects in these regions lead to substantial variability in the applicability and stability of different inversion models, thereby limiting further improvements in FVC estimation accuracy. To address these challenges, this study applied three FVC inversion models—Random Forest (RF), Fully Constrained Least Squares (FCLS), and the Dimidiate Pixel Model (DPM)—to Sentinel-2 data, and further proposed a consistency-constrained ensemble approach. The results show that the three models exhibit pronounced discrepancies in both spatial patterns and value ranges. Specifically, RF performs better in high-coverage areas, whereas FCLS is more sensitive to soil background effects under low-coverage conditions. The proposed ensemble approach effectively integrates the strengths of individual models, improving overall accuracy by approximately 2.1% and reducing estimation uncertainty relative to individual models. These results demonstrate that the proposed approach improves FVC estimation in arid and semi-arid regions and provides a robust framework for large-scale quantitative vegetation monitoring using remote sensing.

Accurately extracting the spatial distribution of shrubs is an important basis for scientific diagnosis, rational prevention, and control of shrub-encroached grasslands (SGs). In SGs, the landscape exhibits a high degree of spatial intermingling among shrubs, herbaceous vegetation, and bare soil, resulting in a serious issue of mixed pixels. The combination of UAV hyperspectral imaging and deep learning provides the most promising technical approach for shrub–grass separation at present. However, issues such as data redundancy and algorithmic adaptability urgently need to be addressed. In this study, Caragana microphylla Lam, a typical shrub species found in the SGs of Inner Mongolia, is the subject for extraction. This work aims to extract sensitive spectral bands from UAV hyperspectral data, construct a deep learning-based framework for shrub identification, and map the distribution of Caragana microphylla Lam in the study area. First, a dataset of hyperspectral images in Xilinhot City, Inner Mongolia, China, was constructed by manual visual interpretation for shrub identification. Second, a novel unsupervised band selection algorithm, US-BS-Net, was proposed to optimize the proxy task of BS-Conv-Net by introducing contrastive learning. It can be used to obtain sensitive spectral bands with discriminative feature representations. Finally, the US-BS-P-Net framework was proposed by combining the US-BS-Net with the prototypical network to construct high-precision shrub identification models in small-sample scenarios. Taking 80 bands as input, the highest overall accuracy reaches 93.15%, which is better than the full spectrum and other deep learning models. The deep synergy between hyperspectral technology and deep learning effectively overcomes traditional challenges such as spectral similarity between shrubs and grasses and their mixed spatial distribution. In particular, the proposed US-BS-P-Net, which combines the advantages of US-BS-Net in sensitive band extraction and the prototypical network in few-shot model construction, provides excellent fundamental data for precise monitoring of SGs.

Leaf Area Index (LAI) is a key biophysical descriptor of crop canopies and is essential for growth monitoring and yield estimation. We present a physics-driven machine-learning framework for operational LAI retrieval and end-to-end uncertainty quantification that couples the PROSAIL radiative transfer model with a genetic-algorithm-optimised multilayer perceptron (NN–GA). PROSAIL is sampled across plausible parameter priors and spectra are convolved with Sentinel-2B spectral response functions to build a 30,000-sample training library; a GA is used to globally optimise network weights and biases. Total retrieval uncertainty is decomposed into a simulation component (PROSAIL parameter variability) and a training component (variability across repeated NN–GA trainings) and combined via the law of propagation of uncertainty. The model was developed in Minqin (modelling/testing area; entirely maize) and transferred to Zhangye (transfer/validation area; predominantly maize, with one sunflower plot). Sentinel-2B validation results were RMSE/R2 = 0.44/0.73 (Minqin) and 0.40/0.56 (Zhangye), indicating reasonable cross-site generalisation. The uncertainty split indicates physical-driven contributions of 11.42% and 11.48% and machine-learning contributions of 18.06% and 12.96%, respectively. The framework improves 10 m LAI retrieval accuracy and supplies a reproducible, per-pixel uncertainty budget to guide product use and refinement.

Drylands, as highly vulnerable ecosystems, support environmental functions and human well-being. Nevertheless, widespread land degradation and desertification present significant global and regional environmental challenges, with limited consensus on their area and degree. This study used time-series vegetation productivity and meteorological data from 2000 to 2020 to quantify global land degradation trends and driving factors in drylands. The results show a notable restoration of land degradation in drylands worldwide, with the area of improved land exceeding the degraded area by 1.4 times, although the threat of degradation persists. India and China emerge as pioneers in effective land improvement strategies, offering valuable experiences for other regions. Combined effects, as quantitatively distinguished by our established model, dominate the degradation and improvement processes. Notably, human activities play a decisive role in influencing land degradation trends, with the potential for either exacerbation or reversal. This study provides new perspectives on environmental health and human activities from global and regional observations. Finally, our research provides scientific support for desertification control and contributes to the overall advancement of the SDGs globally.

Evaluating forest ecosystem services (FES) is crucial for comprehensively recognizing forest value and for formulating targeted forest management plans. However, hurdles persist in traditional FES evaluations that are based on conventional data (e.g., statistical yearbooks and survey data), such as a coarse evaluation scale and difficulty in formulating refined and spatially continuous evaluation results. Forest canopy cover, canopy height, and forest aboveground biomass (AGB) are the core fundamental inputs of a robust FES evaluation. Their accuracy and degree of refinement will influence the final evaluation results obtained. To overcome the above issues, this study first explored accurate estimation methods for all 3 parameters above and then evaluated FES multidimensionally, by using these results combined with other remote sensing products and applying various principles and algorithms. Our results show that a high estimation accuracy (>80%) of the 3 key parameters is achievable for coniferous to broad-leaved forest stands and that FES evaluation results are obtainable with a high resolution and spatial continuity. The service functions, such as nutrient retention, carbon sequestration and oxygen release, and product supply are stronger while others relatively are weaker. It is worth noting that carbon storage by the AGB carbon pool surpasses that of other carbon pools. Finally, the potential of FES varies according to forest type. Compared with broad-leaved forest, coniferous forest has a greater capacity for product supply, windbreak, and sand fixation services. This study offers a methodological reference for the formulation of policies related to the paid use of FES.

Grass yield (GY) is a critical component of the comprehensive analysis of the grass - livestock balance in grassland. Net primary productivity (NPP) conversion methods, such as the Carnegie - Ames - Stanford approach (CASA) model, are an important tool for remote -sensing -based estimations of GY. However, the application of such approaches is limited by the simplification of key vegetation growth processes. In this study, we integrated high spatial and temporal resolution normalized difference vegetation index (NDVI) data collected from Gaofen6 (GF-6) and the Moderate Resolution Imaging Spectroradiometer (MODIS), respectively, in 2020 with the climatic characteristics of grassland vegetation to derive a reasonable expression of the optimum temperature. We then improved the CASA model for the accurate estimation of GY for six different grassland types in Zhenglan Banner (sandy sparse forest grassland, sandy shrub grassland, sandy meadow, low hill steppe, gently sloping steppe, and lowland meadow) at high spatial and temporal resolution. The model estimations were evaluated using field data. The results reveal that adopting the optimum temperature to incorporate vegetation growth characteristics achieves a better theoretical basis and minimizes the influence of anomalous NDVI maxima compared with the original CASA model. This largely avoids the influence of the lagged response of grassland vegetation growth to temperature. The developed GY model has strong applicability, and the correlation between the measured and estimated GY before and after optimization reached 0.75. Moreover, the overall estimation accuracy was improved by nearly 15%. The spatial distribution of GY in Zhenglan Banner was found to be similar to the spatial distribution of grassland types with obvious seasonal differences, and summer was the critical period for GY, accounting for more than 80% of growth. The proposed model aims to provide scientific and technical guidance for the regulation of grassland resources and reasonable grazing utilization in Northern China.

Shrub encroachment in grassland has become an ecological issue of mounting concern. Accordingly, an accurate estimation of aboveground biomass (AGB) of shrub vegetation is the basis for a sound assessment and in-depth understanding of carbon cycling in shrub-encroached grassland ecosystems. Yet the relatively low stature of plants in the shrub community, coupled with the high spatial heterogeneity of their distribution, contributes substantially to greater uncertainty in remote sensing estimation of shrub vegetation’s AGB. This study proposes a space–air-ground integrated approach to accurately estimate the AGB of shrub vegetation in shrub-encroached grassland ecosystems. The results showed that, at the UAV scale, the estimation of AGB for a monoculture shrub was highly dependent on planar geometric features. Based on the orthorectified images obtained from unmanned aerial vehicles (UAVs), four planar geometric features of shrub plants, namely crown area (S), crown perimeter (C), long-to-short crown dimension ratio (A1, A2), were retained as the most crucial predictors for AGB estimation. Among the 102 features related to vertical structure extracted via Light Detection and Ranging (LiDAR), only the crown height variation and the first layer’s density variable were retained. Utilizing the mentioned features and a random forest regression, the AGB prediction model for the shrub Caragana microphylla performed remarkably well, in having an R2 value of 0.84 and an RMSE of 310.14 g/plant. At the satellite scale, there was significant nonlinear relationship between the AGB of the shrubs and the band, texture, and index features extracted from GF-6 imagery. The derived AGB estimation model based on the Random Forest method demonstrates higher accuracy (R2 = 0.81, RMSE = 14.61 g/m2, MAE = 11.26 g/m2) than the linear stepwise regression (SR) and partial least squares regression (PLSR) models. Notably, the green band reflectance was retained in all three modeling approaches despite pronounced differences in their selected features uses. Yet both NDVIre1 and NDREI indices with red-edge bands were more important, suggesting the red-edge bands of GF-6 can serve as an ideal tool for remote sensing investigations of the AGB of shrub in shrub-encroached grasslands. This study provides technical and scientific support for quantitative assessments of shrub AGB in arid and semi-arid grassland regions.

Elm (Ulmus pumila L.) sparse forest plays an vital role in maintaining local ecological stability and security in the Otingdag Sandy Land area. Prior studies on elm canopy extraction have predominantly relied on manual parameter configuration, resulting in unsatisfactory levels of generalization. To meet the needs of high-precision and rapid recognition of elm sparse forests in large areas, this study proposed a recognition method for elm sparse forest that orients to high spatial resolution remote sensing imageries, using deep-learning-based semantic segmentation techniques. It can automatically learn features that are conducive to segmenting the canopy of elm trees, and retains good generalization ability on the Gaofen-2 imageries obtained in different regions. First, we constructed a dataset specialized for elm canopy semantic segmentation task, and annotated over 130,000 elm canopies based on Gaofen-2 imageries. In addition, we trained 7 deep-learning semantic segmentation model candidates. Among them, MANet showed the best performance, with its F1-score reaching 81.44%. Lastly, we applied edge detection to the elm canopy coverage area, and automatically extract the elm canopy. The proposed method can provide technical support for the investigation and monitoring of elm sparse forests, while facilitates local desertification prevention efforts in the entire Otingdag Sandy Region.

Due to the small size, variety, and high degree of mixing of herbaceous vegetation, remote sensing-based identification of grassland types primarily focuses on extracting major grassland categories, lacking detailed depiction. This limitation significantly hampers the development of effective evaluation and fine supervision for the rational utilization of grassland resources. To address this issue, this study concentrates on the representative grassland of Zhenglan Banner in Inner Mongolia as the study area. It integrates the strengths of Sentinel-1 and Sentinel-2 active-passive synergistic observations and introduces innovative object-oriented techniques for grassland type classification, thereby enhancing the accuracy and refinement of grassland classification. The results demonstrate the following: (1) To meet the supervision requirements of grassland resources, we propose a grassland type classification system based on remote sensing and the vegetation-habitat classification method, specifically applicable to natural grasslands in northern China. (2) By utilizing the high-spatial-resolution Normalized Difference Vegetation Index (NDVI) synthesized through the Spatial and Temporal Non-Local Filter-based Fusion Model (STNLFFM), we are able to capture the NDVI time profiles of grassland types, accurately extract vegetation phenological information within the year, and further enhance the temporal resolution. (3) The integration of multi-seasonal spectral, polarization, and phenological characteristics significantly improves the classification accuracy of grassland types. The overall accuracy reaches 82.61%, with a kappa coefficient of 0.79. Compared to using only multi-seasonal spectral features, the accuracy and kappa coefficient have improved by 15.94% and 0.19, respectively. Notably, the accuracy improvement of the gently sloping steppe is the highest, exceeding 38%. (4) Sandy grassland is the most widespread in the study area, and the growth season of grassland vegetation mainly occurs from May to September. The sandy meadow exhibits a longer growing season compared with typical grassland and meadow, and the distinct differences in phenological characteristics contribute to the accurate identification of various grassland types.

The Dragon Program is a cooperation in Earth Observation(EO)between the European Space Agency(ESA)and the Ministry of Science and Technology(MOST)of China.The collaboration aims to promote the application of ESA,ESA Third Party Mission,Copernicus Sentinel and China EO data in scientific and application development,facil-itates scientific exchanges between China and Europe scientists,and provides training for land,ocean,and atmospheric applications of remote sensing technol-ogy.Started in 2004,four phases,each lasting four years,have been successfully completed.The Dragon Program has provided a unique platform for the joint exploitation of EO data for science and application development.Furthermore,it has made remarkable achievements by bringing together top scientists,training young talents,and facilitated satellite data sharing between the Sino-European teams.

As the most extensive temperate grassland in the world, the Eurasian Steppe provides various ecological services that support the environment and human well-being. However, grassland degradation has become a serious environmental issue. Most of the traditional degradation assessments ignore the sensitivity of grassland ecosystems to climatic conditions. In response, our study introduces a new comprehensive identification framework that integrates vegetation growth and climate change, using a novel long-term monitoring methodology to detect grassland degradation and improvement. The framework quantifies the area and degree of degradation and improvement in the Eurasian Steppe using long time-series data from 2000 - 2020. Then, the driving factors of grassland change were analyzed using a quantitative model. Our findings reveal a clear trend of improvement in the Eurasian Steppe was identified, with the improved area being 4.72 times larger than the degraded area (221.4 x 104 and 46.92 x 104 km2, respectively). The Tibetan Plateau and Loess Plateau led to the improvement. Simultaneously, the area surrounding the northern Caspian Sea has been severely degraded. The three areas correspond to frigid humid and semi-humid grassland, temperate humid and semi-humid grassland, and temperate arid and semi-arid grassland, respectively. Globally, the combined effects of climate change and human activities dominated the observed grassland degradation and improvement, accounting for 77.13% and 89.64%, respectively. Our method provides a robust tool for detecting grassland degradation and improvement across large scales, offering scientific support for achieving the United Nations' Sustainable Development Goals (SDGs), particularly land degradation neutrality (LDN).

The successful launch and in-a-bit operation of the 5 m Optical Satellite 02(ZY1-02E)have provided a wealth of remote sensing data for various main businesses within the forestry and grass industry,providing reliable information support for forestry management and ecological services.This study aims to test the application capability of ZY1-02E multispectral data in farmland shelterbelt monitoring,a primary forestry business.Zhangbei County,Hebei Province,serves as the study area.Spectral,vegetation index,and texture feature sets are constructed based on the ZY1-02E multispectral data,and four classification information extraction schemes are designed:(1)spectral features,(2)spectral features+vegetation index,(3)spectral features+texture features,and(4)spectral features+vegetation index+texture features.Random forest algorithm was employed for feature selection,classification information extraction,and validation to evaluate the application potential and effectiveness of the ZY1-02E multispectral data in farmland shelterbelt information extraction.The results show that(1)ZY1-02E multispectral data allow for the accurate extraction of farmland shelterbelt information in the study area,reflecting the actual distribution of farmland shelterbelts to a high degree.Among them,the overall accuracy and Kappa coefficient of Scheme 1 are 0.8371 and 0.7760,respectively;the overall accuracy and Kappa coefficient of Scheme 2 are 0.8440 and 0.7855,respectively;the overall accuracy and Kappa coefficient of Scheme 3 reach 0.8839 and 0.8403,respectively;and Scheme 4 has the highest accuracy,with its overall accuracy and Kappa coefficient being 0.8908 and 0.8499,respectively.(2)The effective use of multiple feature variables can significantly improve the accuracy of farmland shelterbelt information extraction.Regarding the contribution of different features to farmland shelterbelt information extraction,in terms of their contribution to farmland shelterbelt information extraction,the spectral features are the most significant,followed by texture features and vegetation indices.(3)ZY1-02E multispectral data exhibit high accuracy and reliable results for farmland shelterbelt information extraction,which can better meet the needs of protection forest monitoring operations and has considerable potential for application in forest surveys and monitoring thematic operations.In conclusion,this study demonstrates the potential and effectiveness of ZY1-02E multispectral data for extracting farmland shelterbelt information.Using multiple feature variables and the random forest algorithm enables the accurate extraction and validation of farmland shelterbelt information,providing valuable insights for future forest monitoring and management.As more data become available and the application capabilities of the ZY1-02E are further explored,future work can consider integrating multispectral data from different periods and linear features of farmland shelterbelt to enhance the accuracy of information extraction,ultimately achieving more efficient and precise extraction of farmland shelterbelt information.

高分辨率遥感林业应用技术与服务平台采用基于云构架的资源协同管理和林业专题产品生产流程定制技术,集成了高分辨率遥感在森林资源调查、湿地资源监测、荒漠化监测、林业生态工程监测和森林灾害监测等林业调查和监测应用中算法模块,开发了高分森林资源调查、湿地资源监测、荒漠化监测、林业生态工程监测和森林灾害监测 5 大应用系统,研制了 21 种林业专题产品生产线,实现了高分林业应用专题产品的定制化生产,形成了满足我国林业调查与监测业务需求的高分辨率遥感林业应用技术体系,实现了高分遥感数据并行处理和远程多客户端的同步操作运行,以及专题产品的网络发布与共享.

Due to the effects of global climate change and altered human land-use patterns, typical shrub encroachment in grasslands has become one of the most prominent ecological problems in grassland ecosystems. Shrub coverage can quantitatively indicate the degree of shrub encroachment in grasslands; therefore, real-time and accurate monitoring of shrub coverage in large areas has important scientific significance for the protection and restoration of grassland ecosystems. As shrub-encroached grasslands (SEGs) are a type of grassland with continuous and alternating growth of shrubs and grasses, estimating shrub coverage is different from estimating vegetation coverage. It is not only necessary to consider the differences in the characteristics of vegetation and non-vegetation variables but also the differences in characteristics of shrubs and herbs, which can be a challenging estimation. There is a scientific need to estimate shrub coverage in SEGs to improve our understanding of the process of shrub encroachment in grasslands. This article discusses the spectral differences between herbs and shrubs and further points out the possibility of distinguishing between herbs and shrubs. We use Sentinel-2 and Gao Fen-6 (GF-6) Wide Field of View (WFV) as data sources to build a linear spectral mixture model and a random forest (RF) model via space–air–ground collaboration and investigate the effectiveness of different data sources, features and methods in estimating shrub coverage in SEGs, which provide promising ways to monitor the dynamics of SEGs. The results showed that (1) the linear spectral mixture model can hardly distinguish between shrubs and herbs from medium-resolution images in the SEG. (2) The RF model showed high estimation accuracy for shrub coverage in the SEG; the estimation accuracy (R2) of the Sentinel-2 image was 0.81, and the root-mean-square error (RMSE) was 0.03. The R2 of the GF6-WFV image was 0.72, and the RMSE was 0.03. (3) Texture feature introduced in RF models are helpful to estimate shrub coverage in SEGs. (4) Regardless of the linear spectral mixture model or the RF model being employed, the Sentinel-2 image presented a better estimation than the GF6-WFV image; thus, this data has great potential to monitor shrub encroachment in grasslands. This research aims to provide a scientific basis and reference for remote sensing-based monitoring of SEGs.

The Beijing–Tianjin Sandstorm Source Control Project (BTSSCP) has been implemented for more than 20 years (2001–2022), and a scientific, accurate, and complete evaluation of its implementation effect is of great significance for the study of ecosystem evolution. In this study, we present a vegetation–water–soil–environment system comprising the net ecosystem productivity (NEP), water conservation (WC), soil erosion (SE), and habitat quality (HQ) to evaluate the spatiotemporal changes and future trends. The results showed that: (1) In areas with high vegetation coverage, only WC was significantly and negatively correlated with SE (−0.68, p less than 0.01). In areas with low vegetation coverage, NEP, WC, and HQ were all significantly negatively correlated with SE. This indicates that SE is a basic function that affects the performance of other ecological services. (2) From 2000 to 2020, the annual maximum average vegetation coverage increased by about 10 %; the annual average NEP increased by about 60 g C/km2, and the annual average SE decreased by about 500 t/km2. Compared with Phase I (2000–2010), the vegetation condition (coverage, NEP) in summer (July–August) was improved by about 10 %, and SE in winter (December–January) decreased by 3 t/km2 per month in Phase II (2010–2020). (3) In terms of future trends, NEP may continue to increase in the grassland area, especially in the Mu Us Sandland, but it may not change significantly in the forest area. Similarly, SE may continue to decrease in grassland-covered areas, but not in forest-covered areas. Most areas of WC could continue to increase, but the HQ will not change significantly. In general, the ecological restoration effects of the BTSSCP have improved significantly. This paper is intended to provide some key information for the management of ecological projects and regional ecological security.

Grassland is the second largest terrestrial ecosystem and a fundamental land resource for human survival and development. Although grassland degradation is a recognized and crucial ecological problem, there is no consensus on the area, scope, and degree of its global degradation trends, making the implementation of Sustainable Development Goals (SDG) 15.3 for achieving a land degradation-neutral world uncertain. This study quantitatively explored global grassland degradation trends from 2000 to 2020 by coupling vegetation growth and its response to climate change. Furthermore, the driving factors behind these trends were analyzed, especially in hotspots. Results show that the improvement in global grassland has been remarkable since 2000, with a 1.92 times larger area than degrading grassland, amounting to 372.47 x 10(4) and 193.57 x 10(4) km(2), respectively. Africa and Asia lead in global grassland degradation and improvement, respectively. Globally, the combined effects of climate change and human activities are the main driving factors for grassland degradation and improvement, accounting for 84.72 and 87.76%, respectively. Notably, human activities played a crucial role in reversing the trend of grassland degradation in some hotspots. Finally, this study provides an essential scientific reference and support for realizing SDG 15.3 on global and regional scales.

GF-1—GF-7 satellite series with 19 major payloads has been launched with the continuous implementation of the highresolution Earth Observation System（referred to as GF） in the past decade. This progress is vital in forming the multispectral and multimode observation capability of China’s Earth Observation System. Remote sensing data with high spatial, temporal, and spectral resolution have been obtained and widely used in scientific research and remote sensing applications. However, obtaining high-quality remote sensing information products from the original satellite data is a complicated scientific issue and faces huge challenges. Hence, the conversion chain from GF data to information must be urgently set up to reduce the remote sensing application threshold and improve the effectiveness of application services.The errors of remote sensing quantitative products are determined by accumulating a series of errors, such as sensor imaging error,calibration error, remote sensing data processing error, and quantitative inversion error. Thus, improving the accuracy of quantitative remote sensing products is a complex system engineering. Completing the whole process, including data processing, retrieval algorithm development, product generation, and validation independently, is challenging. Remote sensing algorithm test and product validation are the two crucial ways for the quality improvement of remote sensing products. Hence, this study proposes the technique system of GF common product generation and validation to improve the quality of GF remote sensing products further, thereby guaranteeing the improvement of the application quality and the extensive application area of GF remote sensing products. Lastly, the current progress of the GF common product validation and algorithm determination system platform is introduced and discussed.GF common products are required by more than two thematic remote sensing products. They can be validated using in situ observations.According to the GF common product system, the number of 39 + 6 products in seven categories are sorted out for the common requirements of multiple users, including geometric products, basic radiation products, land cover and land type products, energy balance products, vegetation products, water products, and atmosphere products. This study presents the technique flowchart of GF common product algorithm determination and product generation. The key technologies of algorithm testing, algorithm optimization, product generation, and validation are developed. Eleven national standards for remote sensing product validation are issued and implemented. Other group standards, such as GF common product generation, ground in situ observation, and validation of GF common remote sensing products, are being designed and compiled. Based on these validation technologies and the in situ data from the national network of GF remote sensing product validation field sites, the GF common product validation platform and product algorithm determination system platform can ensure the high quality of GF common products.Building such a technical system for GF common product generation and validation has great relevance for ensuring high accuracy and high quality to improve the efficiency of application services further. It requires the cooperation of multiple researchers from different units to research and develop common product retrieval algorithms. Moreover, the algorithm should be continuously tested to improve the accuracy of common products.

Shrub encroachment has become a global concern. The fraction of shrub coverage (FSC) is an important indicator that reflects the distribution of shrubs and the degree of shrub encroachment in grasslands. The line-point intercept (LPI) method is commonly used for FSC measurement in field surveys, but it's often associated with issues of poor accuracy and low measurement efficiency. Here, we focus on Caragana microphylla shrubencroached grassland as a case study. We used normalized difference vegetation index (NDVI) data obtained from unmanned aerial vehicle (UAV) to derive the actual values of FSC (FSCT) and to simulate measurements using the LPI method. We compared the results for measurements based on different sample plot designs, including variation in plot shape and line distribution, the number of sample lines, and the sample point spacings. The aim was to investigate the influence of these main parameters of sample plot design upon measurement results, in order to provide a reference for FSC measurements in field experiments. We first calculated FSCT and stratified the plots according to coverage levels. Based on this, we further evaluated the measurement accuracy of the LPI method under different parameter settings. The results revealed significant systematic errors in the measurement from circular plots. For square plots, the minimum number of lines required for the "high," "medium," and "low" coverage groups are 48, 16, and 8 (for 80% accuracy), and likewise, 108, 24, and 16 (for 90% accuracy), respectively. Futuremore, point spacings of 0.1 m and 0.5 m can achieve the same accuracy as the original spacing (0.02 m). We conclude that the systematic errors in circular plots are caused by the radial distribution of lines, whereas in square plots, achieving an efficient measurement requires considering different coverage levels, ensuring an adequate number of lines, and setting a relatively smaller point spacing.

Remote sensing experiments and products validation have always played an important role in the development of remote sensing science and technology. As an important way of understanding the natural characteristics, remote sensing experiments have played an important role in remote sensing science and technology research. Remote sensing product validation is crucial to ensuring the high accuracy of remote sensing algorithms and improving the quality of remote sensing products. Remote sensing experiments are the basis of validation because the ground truth, which is derived from field experiments, is the core of the validation. However, obtaining the ground truth is a complex systematic process, including optimized sampling, field observation, scale transformation, and validation. Thus, reducing the error of each process is important in obtaining high-quality ground truth.On April 6, 2022, China held the “First Forum on Remote Sensing Field Experiments and Products Validation” where intense exchanges and discussions on the key topics occurred, including remote sensing experiments and validation theory and technology, ground field observation and uncertainty, validation practice and platform, and the future development of remote sensing experiments and validation. More than 1000 scholars and students from different domestic scientific research institutes and universities attended the event.On the basis of reviewing the main progress in the field of remote sensing experiments and validation in China, thus paper summarizes the opinions and suggestions of the Chinese scientists who attended the meeting and expounds on the problems and challenges faced by China in terms of remote sensing field experiments and products validation. The development process of remote sensing experiments and products validation in China has three main development stages, namely, the remote sensing application experiment stage from the year of 1978, the quantitative remote sensing mechanism experiment stage from the year of 1994, and the comprehensive experiment stage of the quantitative remote sensing mechanism and validation. Significant progress has been made in experimental technology, validation technology, national specifications of product validation, and the validation system platform.Five related topics of remote sensing experiments and product validation have been discussed by 80 scientists, namely, the theoretical framework and basic theory of remote sensing product validation, ground observation and standardized data processing, land surface heterogeneity and scale transformation, product validation and evaluation, and system platform and data sharing.It should be considered systematically from the entire link key process of obtaining ground truth for remote sensing experiment and validation to truly become an important basis in ensuring the quality of satellite remote sensing products. Finally, the prospect of remote sensing experiment and validation in China is discussed. Remote sensing experiment and validation should be implemented from the joint efforts of national and even global scientific researchers to achieve global implementation through international cooperation and improve the ability of remote sensing experiments and product validation.

目前不同中空间分辨率遥感卫星在利用混合像元分解方法提取石漠化信息上存在效果差异,比较不同遥感卫星在提取石漠化信息上的差异,有助于进一步提高石漠化信息提取精度.本研究以贵州省普定县为例,采用GF-6 与Landsat8 卫星数据,利用顶点成分分析(vertex component analysis,VCA)和完全约束最小二乘法(fully constrain-ed least squares,FCLS)相结合的混合像元分解方法进行石漠化信息提取,探究GF-6 与Landsat8 在石漠化信息提取的端元特征和等级差异,以此探索GF-6 在提取石漠化信息的可行性与有效性.研究结果表明:①红边波段范围上,GF-6 植被端元波谱曲线明显区别于基岩与土壤端元,更易识别出植被端元;②石漠化信息端元提取精度上,GF-6 和Landsat8 提取植被端元OA分别为 0.63 和 0.45,Kappa系数分别为 0.50 和 0.29,RMSE分别为 1.19和1.71,GF-6 和Landsat8 提取基岩端元OA分别为 0.79 和 0.61,Kappa系数分别为 0.63 和 0.42,RMSE分别为0.54 和0.88;③石漠化等级评价上,GF-6 和Landsat8 提取石漠化等级OA分别为 0.76 和 0.59,Kappa系数分别为0.56 和0.38,RMSE分别为0.64 和1.27.因此,GF-6 在混合像元分解提取石漠化信息精度要优于Landsat8,且GF-6 的红边波段能更好地识别石漠化区域植被信息,基于GF-6 的混合像元分解方法可作为一种石漠化监测手段应用于实际工作中.