Leaf area index (LAI) is a fundamental parameter for assessing the structure and dynamics of terrestrial vegetation ecosystems. Current long-term LAI datasets are typically derived from medium-resolution satellite imagery, which limits their utility in fine-scale applications. We present a high-resolution LAI mapping algorithm using 30-meter Landsat surface reflectance data across China. The algorithm is based on similar to 390,000 LAI samples uniformly distributed across the country and integrates MODIS-derived LAI and Landsat reflectance as the target variable and primary predictor, respectively. For each of the eight major vegetation community types in China, a Random Forest model was trained using rigorously filtered and optimized samples. Cross-validation results indicated that the model achieved good accuracy (coefficient of determination (R-2) = 0.899, bias = -0.007 m(2)/m(2), mean absolute error (MAE) = 0.180 m(2)/m(2), root mean square error (RMSE) = 0.382 m(2)/m(2), mean absolute percentage error (MAPE) = 23.8%, and normalized root mean square error (NRMSE) = 0.057), although the performance varied by vegetation type. An independent validation using 156 ground-based LAI measurements from the DIRECT V2.1 dataset for two vegetation community types (grass and cropland) yielded R-2, bias, MAE, RMSE, MAPE, and NRMSE values of 0.595, -0.675 m(2)/m(2), 0.968 m(2)/m(2), 1.203 m(2)/m(2), 36.39 %, and 0.237, respectively.

Frequent droughts increasingly threaten ecosystem stability and agricultural production. Soil moisture is a key indicator of drought, but its spatial coverage remains limited. Remote sensing drought indices provide higher spatial resolution, yet their ability to reflect soil moisture variability has not been systematically assessed. This study evaluates the Vegetation Condition Index, Vegetation Water Index, and Temperature Condition Index by combining Pearson correlation analysis with a Copula-based conditional probability framework to assess their long-term and threshold-based relationships with soil moisture across multiple temporal scales in China. The Vegetation Condition Index shows the strongest correlation with soil moisture at the annual scale and remains dominant during spring, summer, and autumn at shorter time scales. Ecosystem-dependent patterns emerge in summer, with the Vegetation Water Index performing better in forests and the Temperature Condition Index in grasslands, reflecting differences in vegetation density and surface energy processes. The Copula-based analysis reveals a contrasting pattern: across regions, remote sensing indices are less likely to reach extreme or severe drought thresholds than to indicate general drought, suggesting weaker vegetation and temperature responses under extreme soil moisture deficits. Under soil moisture drought conditions, the Vegetation Condition Index shows the highest conditional drought probability in forested regions, whereas the Vegetation Water Index is more responsive in northern arid regions and grasslands, and the Temperature Condition Index shows clearer responses during the growing season. These results indicate that vegetation regulation and legacy effects can weaken synchronous responses during extreme droughts, and that dominant drought signals vary among ecosystems.

Land cover products provide critical information for monitoring and analyzing land surface changes. However, notable disagreement and incompatible classification systems among existing land cover products bring challenges in using them. Here, we developed a hierarchical International Geosphere-Biosphere Programme (IGBP) classification system and integrated four widely used land cover products (i.e., MODIS-IGBP, ESA-CCI, GlobeLand30, and GLC_FCS30) based on their accuracy against a collection of global reference samples. We generated a hybrid global annual land cover product (HYBMAP) with ~1 km (1/120°, 30″) spatial resolution from 2000 to 2020. The HYBMAP integrates information from the four products of high- and medium-resolution and reduces the disagreement between them by up to 20.1%. The overall accuracy of the HYBMAP is 75.5%, which is higher than the best of the four products (MODIS-IGBP, 70.9%). HYBMAP also integrates the temporal change information from the four products and identifies a faster growth of built-up lands. The HYBMAP provides more consistent and reliable global land cover time series data for global change research. It is free to access at https://doi.org/10.5281/zenodo.10488191.

The fraction of absorbed photosynthetically active radiation (FPAR) is an essential biophysical parameter that characterizes the structure and function of terrestrial ecosystems. Despite the extensive utilization of several satellite-derived FPAR products, notable temporal inconsistencies within each product have been underscored. Here, the new generation of the GIMMS FPAR product, GIMMS FPAR4g, was developed using a combination of a machine learning algorithm and a pixel-wise multi-sensor records integration approach. PKU GIMMS NDVI, which eliminates the orbital drift and sensor degradation issues, was used as the data source. Comparisons with ground-based measurements indicate root mean square errors ranging from 0.10 to 0.14 with R-squared ranging from 0.73 to 0.87. More importantly, our product demonstrates remarkable spatiotemporal coherence and continuity, revealing a persistent terrestrial darkening over the past four decades (0.0004 yr−1, p < 0.001). The GIMMS FPAR4g, available for half-month intervals at a spatial resolution of 1/12° from 1982 to 2022, promises to be a valuable asset for in-depth analyses of vegetation structures and functions spanning the last 40 years.

Gross primary productivity (GPP) is jointly controlled by the structural and physiological properties of the vegetation canopy and the changing environment. Recent studies showed notable changes in global GPP during recent decades and attributed it to dramatic environmental changes. Environmental changes can affect GPP by altering not only the biogeochemical characteristics of the photosynthesis system (direct effects) but also the structure of the vegetation canopy (indirect effects). However, comprehensively quantifying the multi-pathway effects of environmental change on GPP is currently challenging. We proposed a framework to analyse the changes in global GPP by combining a nested machine-learning model and a theoretical photosynthesis model. We quantified the direct and indirect effects of changes in key environmental factors (atmospheric CO _2 concentration, temperature, solar radiation, vapour pressure deficit (VPD), and soil moisture (SM)) on global GPP from 1982 to 2020. The results showed that direct and indirect absolute contributions of environmental changes on global GPP were 0.2819 Pg C yr ^−2 and 0.1078 Pg C yr ^−2. Direct and indirect effects for single environmental factors accounted for 1.36%–51.96% and 0.56%–18.37% of the total environmental effect. Among the direct effects, the positive contribution of elevated CO _2 concentration on GPP was the highest; and warming-induced GPP increase counteracted the negative effects. There was also a notable indirect effect, mainly through the influence of the leaf area index. In particular, the rising VPD and declining SM negatively impacted GPP more through the indirect pathway rather than the direct pathway, but not sufficient to offset the boost of warming over the past four decades. We provide new insights for understanding the effects of environmental changes on vegetation photosynthesis, which could help modelling and projection of the global carbon cycle in the context of dramatic global environmental change.

Global products of remote sensing Normalized Difference Vegetation Index (NDVI) are critical to assessing the vegetation dynamic and its impacts and feedbacks on climate change from local to global scales. The previous versions of the Global Inventory Modeling and Mapping Studies (GIMMS) NDVI product derived from the Advanced Very High Resolution Radiometer (AVHRR) provide global biweekly NDVI data starting from the 1980s, being a reliable long-term NDVI time series that has been widely applied in Earth and environmental sciences. However, the GIMMS NDVI products have several limitations (e.g., orbital drift and sensor degradation) and cannot provide continuous data for the future. In this study, we presented a machine learning model that employed massive high-quality global Landsat NDVI samples and a data consolidation method to generate a new version of the GIMMS NDVI product, i.e., PKU GIMMS NDVI (1982–2022), based on AVHRR and Moderate-Resolution Imaging Spectroradiometer (MODIS) data. A total of 3.6 million Landsat NDVI samples that were well spread across the globe were extracted for vegetation biomes in all seasons. The PKU GIMMS NDVI exhibits higher accuracy than its predecessor (GIMMS NDVI3g) in terms of R2 (0.97 over 0.94), root mean squared error (RMSE: 0.05 over 0.09), mean absolute error (MAE: 0.03 over 0.07), and mean absolute percentage error (MAPE: 9 % over 20 %). Notably, PKU GIMMS NDVI effectively eliminates the evident orbital drift and sensor degradation effects in tropical areas. The consolidated PKU GIMMS NDVI has a high consistency with MODIS NDVI in terms of pixel value (R2 = 0.956, RMSE = 0.048, MAE = 0.034, and MAPE = 6.0 %) and global vegetation trend (0.9×10-3 yr−1). The PKU GIMMS NDVI product can potentially provide a more solid data basis for global change studies. The theoretical framework that employs Landsat data samples can facilitate the generation of remote sensing products for other land surface parameters. The PKU GIMMS NDVI product is open access and available under a Creative Commons Attribution 4.0 License at https://doi.org/10.5281/zenodo.8253971 (Li et al., 2023).

The long-term global Leaf Area Index (LAI) products are critical supports for characterizing the changes in land surface and its interactions with other components of the Earth system under the dramatic global change. However, intercomparisons between current available long-term global LAI products present significant spatiotemporal inconsistencies which have been a persistent source of uncertainties in global change ecology. Yet, a direct and systematic evaluation of current long-term LAI products is still lacking due to the absence of appropriate LAI references, especially before 2000. Here, we proposed a novel evaluation framework to directly evaluate the mainstream long-term global LAI products (GIMMS LAI3g, GLASS LAI, and GLOBMAP LAI) using massive high-quality LAI validation samples. The LAI validation samples, derived from the Landsat archive using machine learning and MODIS LAI, have a global distribution, a long temporal coverage (1982−2020), and a large amount of 4.9 million. They substantially address the issue of insufficient LAI reference data and can enable quantitative LAI assessments. The long-term global LAI products showed reasonable quality in terms of absolute value, with GIMMS LAI3g having better performance (R:0.96; MAE: 0.29 m^2 m^(-2); RMSE: 0.49 m^2 m^(-2)), followed by GLASS LAI (R:0.96; MAE: 0.31 m^2 m^(-2); RMSE: 0.51 m^2 m^(-2)) and GLOBMAP LAI (R:0.90; MAE: 0.52 m^2 m^(-2); RMSE: 0.91 m^2 m^(-2)). For all LAI products, the data quality after 2000 was better than before 2000. Their annual maximum LAI trends presented mediocre consistencies with the LAI validation samples (R: 0.20−0.29) which showed a significantly larger area of greening. The evaluation of ten state-of-the-art ecosystem models demonstrated varied capabilities in simulating global LAI trends, with the standard deviations ranging from ~0.01 to 0.04 m^2 m^(-2) a^(-1). Although the Multi-Model Ensemble Mean LAI agreed with satellite-based LAI products, they differed with vegetation biomes especially for the tropics. The Landsat LAI validation dataset produced in this study can facilitate the development of long-term global LAI products and provide a quantitative reference for vegetation dynamic studies.

Leaf area index (LAI) with an explicit biophysical meaning is a critical variable to characterize terrestrial ecosystems. Long-term global datasets of LAI have served as fundamental data support for monitoring vegetation dynamics and exploring its interactions with other Earth components. However, current LAI products face several limitations associated with spatiotemporal consistency. In this study, we employed the back propagation neural network (BPNN) and a data consolidation method to generate a new version of the half-month 1/12∘ Global Inventory Modeling and Mapping Studies (GIMMS) LAI product, i.e., GIMMS LAI4g, for the period 1982–2020. The significance of the GIMMS LAI4g was the use of the latest PKU GIMMS normalized difference vegetation index (NDVI) product and 3.6 million high-quality global Landsat LAI samples to remove the effects of satellite orbital drift and sensor degradation and to develop spatiotemporally consistent BPNN models. The results showed that the GIMMS LAI4g exhibited overall higher accuracy and lower underestimation than its predecessor (GIMMS LAI3g) and two mainstream LAI products (Global LAnd Surface Satellite (GLASS) LAI and Long-term Global Mapping (GLOBMAP) LAI) using field LAI measurements and Landsat LAI samples. Its validation against Landsat LAI samples revealed an R2 of 0.96, root mean square error of 0.32 m2 m−2, mean absolute error of 0.16 m2 m−2, and mean absolute percentage error of 13.6 % which meets the accuracy target proposed by the Global Climate Observation System. It outperformed other LAI products for most vegetation biomes in a majority area of the land. It efficiently eliminated the effects of satellite orbital drift and sensor degradation and presented a better temporal consistency before and after the year 2000. The consolidation with the reprocessed MODIS LAI allows the GIMMS LAI4g to extend the temporal coverage from 2015 to a recent period (2020), producing the LAI trend that maintains high consistency before and after 2000 and aligns with the reprocessed MODIS LAI trend during the MODIS era. The GIMMS LAI4g product could potentially facilitate mitigating the disagreements between studies of the long-term global vegetation changes and could also benefit the model development in earth and environmental sciences. The GIMMS LAI4g product is open access and available under Attribution 4.0 International at https://doi.org/10.5281/zenodo.7649107 (Cao et al., 2023).

Global vegetation has experienced notable changes in greenness and productivity since the early 1980s. However, the changes in the relationship between productivity and greenness, i.e., the coupling, and its underlying mechanisms, are poorly understood. The Loess Plateau (LP) is one of China's most significant areas for vegetation greening. Yet, it remains poorly documented what changes in the coupling between productivity and greenness are and how environmental and anthropogenic factors affect this coupling in the LP over the past four decades. We investigated the interannual trend of coupling between Gross Primary Productivity (GPP) and Leaf Area Index (LAI), i.e., the GPP-LAI coupling, and its response to climate factors and afforestation in the LP using long-term remote-sensed LAI, GPP and Solar-induced Chlorophyll Fluorescence (SIF). We found a monotonically increasing trend in the GPP-LAI coupling in the LP from 1982 to 2018 (0.0043 yr-1, p < 0.05), in which the significant trend in the northwest LP was driven by increasing soil water and landcover change, e.g., increased grassland and afforestation. An ensemble of 11 state-of-the-art ecosystem models from the TRENDY project failed to capture the observed monotonically increasing trend of the GPP-LAI coupling in the LP. The consistent projection of a decreasing GPP-LAI coupling in LP during 2019-2100 by 22 Earth System Models (ESMs) under various future scenarios should be treated with caution due to the identified inherent uncertainties in the ecosystem component in ESMs and the notable biases in the simulation of future climate conditions. Our study highlights the need to enhance the key mechanisms that regulate the coupling relationships between photosynthesis and canopy structure in indigenized ecosystem models to accurately estimate the ecosystem change in drylands under global climate change.

Brief Introduction: The PKU GIMMS Normalized Difference Vegetation Index product (PKU GIMMS NDVI, version 1.2) provides spatiotemporally consistent global NDVI data in half-month and 1/12° from 1982 to 2022. It is created to address the major uncertainties presented in current global long-term NDVI products, i.e., the effects of NOAA satellite orbital drift and AVHRR sensor degradation. The PKU GIMMS NDVI was generated based on biome-specific BPNN models that employed GIMMS NDVI3g product and 3.6 million high-quality global Landsat NDVI samples. It was then consolidated with the MODIS NDVI (MOD13C1) to extend the temporal coverage to 2022 via a pixel-wise Random Forests fusion method. The PKU GIMMS NDVI exhibits overall high accuracy evaluated by Landsat NDVI samples. Besides, it efficiently eliminated the effects of satellite orbital drift and sensor degradation and presents a good temporal consistency with MODIS NDVI in terms of pixel value and global vegetation trend. It could potentially provide a more solid data basis for global change studies. Here we provide two versions of PKU GIMMS NDVI for download, one solely based on AVHRR data (1982−2015) and the other consolidated with the MODIS NDVI (1982−2022). We strongly recommend an adequate use of the quality control (QC) layer in the product. Please refer to the Readme file for more details. We also recommend removing sparse vegetation by a threshold (e.g., 0.1) in trend analysis (Zhou et al., 2001; Liu et al., 2016) Major updates: Version 1.0 (December 15, 2022): · The original version of the product. Version 1.1 (June 17, 2023): · A pixel-wise Random Forests consolidation method is used to replace the linear one. · The data files have been re-organized on a decade basis. Version 1.2 (August 17, 2023): · The BPNN model without explanatory variables of NOAA satellite number and years since launch is used to generate NDVI values of EBF during the periods of 1982−1984 and all October to April, when the Landsat NDVI samples were relatively scarce. Dataset Characteristics: Spatial Coverage: 180ºW~180ºE, 63ºS~90ºN Projection: Geographic Spatial Resolution: 1/12 degree Temporal Resolution: Half month Temporal Coverage: January 1982 to December 2022 Image Dimension: Rows-2160; Columns-4320 Units: unitless Fill Value: 65535 Data Type: uint16 Valid Range: 0-1000 Scale Factor: 0.001 File Format: TIFF(.tif) File Size: ~8Mb each file References: Li, M., Cao, S., Zhu, Z., Wang, Z., Myneni, R. B., and Piao, S.: Spatiotemporally consistent global dataset of the GIMMS Normalized Difference Vegetation Index (PKU GIMMS NDVI) from 1982 to 2022, Earth Syst. Sci. Data, 15, 4181–4203, https://doi.org/10.5194/essd-15-4181-2023, 2023. Liu, Q., Fu, Y. H., Zhu, Z., Liu, Y., Liu, Z., Huang, M., Janssens, I. A., and Piao, S.: Delayed autumn phenology in the Northern Hemisphere is related to change in both climate and spring phenology, Global Change Biology, 22, 3702–3711, https://doi.org/10.1111/gcb.13311, 2016. Zhou, L., Tucker, C. J., Kaufmann, R. K., Slayback, D., Shabanov, N. V., and Myneni, R. B.: Variations in northern vegetation activity inferred from satellite data of vegetation index during 1981 to 1999, J. Geophys. Res., 106, 20069–20083, https://doi.org/10.1029/2000JD000115, 2001.

Abstract. Global products of remote sensing Normalized Difference Vegetation Index (NDVI) are critical to assessing the vegetation dynamic and its impacts and feedbacks on climate change from local to global scales. The previous versions of the Global Inventory Modelling and Mapping Studies (GIMMS) NDVI product derived from the Advanced Very High Resolution Radiometer (AVHRR) provide global biweekly NDVI data starting from the 1980s, being a reliable long-term NDVI time series that has been widely applied in Earth and environmental sciences. However, the GIMMS NDVI products have several limitations (e.g., orbital drift and sensor degradation) and cannot provide continuous data for the future. In this study, we presented a machine learning model that employed massive high-quality and global-wide Landsat NDVI samples and a data consolidation method to generate a new version of the GIMMS NDVI product, i.e., PKU GIMMS NDVI (1982−2020), based on AVHRR and Moderate-Resolution Imaging Spectroradiometer (MODIS) data. A total of 3.6 million Landsat NDVI samples that well spread across the globe were extracted for vegetation biomes in all seasons. The PKU GIMMS NDVI exhibits higher accuracy than its predecessor (GIMMS NDVI3g) in terms of R2 (0.975 over 0.942), mean absolute error (MAE: 0.033 over 0.074), and mean absolute percentage error (MAPE: 9 % over 20 %). Notably, PKU GIMMS NDVI effectively eliminates the evident orbital drift and sensor degradation effects in tropical areas. The consolidated PKU GIMMS NDVI has a high temporal consistency with MODIS NDVI in describing vegetation trends (R2 = 0.962, MAE = 0.032, and MAPE = 6.5 %). The PKU GIMMS NDVI product can potentially provide a more solid data basis for global change studies. The theoretical framework that employs Landsat data samples can facilitate the generation of remote sensing products for other land surface parameters.

An unsupervised change-detection problem is formulated as a binary classification problem corresponding to the change and no change areas. This paper proposes a novel unsupervised object-oriented change detection method based on neighborhood correlation images (NCIs) and k-means clustering for high-resolution remote sensing images. We tested our proposed method in two study areas of Beijing with RapidEye images and compared it with three other popular change detection methods based on different images: change vector analysis (CVA), principal component analysis (PCA), and multivariate alteration detection (MAD). The results indicate that our method has the highest overall accuracy (90.80% in Shunyi District, Beijing and 90.40% in Daxing District, Beijing) and Kappa coefficient (0.7922 in Shunyi District, Beijing and 0.7796 in Daxing District, Beijing). In addition, the McNemar test indicates that our method is robust and stable across different study areas. We concluded that the object-oriented NCIs method outperforms traditional difference images (CVA, PCA, and MAD) in unsupervised change detection. The experimental results demonstrate the effectiveness of the proposed approach in solving the problem of unsupervised change detection for high-resolution images.

Ecological connectivity is the foundation of maintaining urban biodiversity and ecosystem health. Identifying and managing ecological (connectivity) networks can help maintain the stability of urban ecosystems. However, few studies have explored the cluster effect in the ecological network caused by the imbalance in connectivity strength between habitat patches, which is not conducive to the in-depth restoration of ecological networks. In the present study, a typical urban area, Shenzhen, was used as an example to analyze the important habitats in the city based on the focal species and to identify an ecological network. Habitat patch clusters in the ecological network were explored based on random walk network community detection. These are clusters of closely connected habitat lands. Finally, we analyzed existing urban policies for the protection of clusters and the points to be repaired in the network. The results showed that 50 ecological corridors connected 39 habitats in the study area, which further formed seven habitat patch clusters. Most of the clusters were well-protected by existing policies. Nineteen barrier points were identified between the clusters, and their restoration helped strengthen the connectivity between clusters. This study provides a reference for future urban ecological restoration.

Cloud contamination has largely limited the application of the Moderate Resolution Imaging Spectroradiometer(MODIS) normalized difference snow index (NDSI). Here, a novel gap-filling method based on spatial-temporal similar pixel interpolation was proposed to remove cloud occlusions in MODIS NDSI products. First, the widely used Terra and Aqua combination and three-day temporal filter methods were applied. The remaining missing NDSI information was estimated by using similar eligible pixels in the remaining cloud-free portion of a target image through a spatial-temporal similar pixel selecting algorithm (SPSA). The MODIS daily NDSI product data from 2003 to 2018 in the Qinghai–Tibetan Plateau (China) was used as a case study. The results demonstrate that the three-step methodology can generate almost completely cloud-free, daily MODIS NDSI images, reducing the cloud-gap fraction from >45% to less than 1.5% on average. The validation results of the SPSA method exhibited a high accuracy, with a high R2 exceeding 0.78, a low mean absolute error of 2.77%, a root mean square error of 3.78%, and a 96.92% overall accuracy. The proposed method can fill cloud gaps without a significant loss of accuracy, especially during snow cover transition periods (autumn and spring), which may provide more accurate cloud-free NDSI data for climate change and energy balance studies.

An algorithm uses the Normalized Difference Vegetation Index (NDVI) time series curve to compute NDVI change rate (CR) for every 8-day over a period (2008-2018) from Moderate-resolution Imaging Spectroradiometer (MODIS) data. The indices of CR variables within the winter wheat growing season were correlated with the end of the season winter wheat yield. A strong correlation (Pearson correlation coefficient = -0.48) was found in day of year 153-161 (filling) and the CR_153-161 was used to build a univariate regression model. Stepwise multiple linear regression was then used and then 16 CR variables were selected to construct the multiple regression model. The two models are good at predicting yield. The prediction results of two models were compared with official agricultural statistics showing that the RMSE = 578.21 kg ha -1 /457.38 kg ha -1 and MRE=18.54%/14.56% Remote sensing of NDVI-CR, therefore, is a valuable tool for estimating winter wheat yield well in advance of harvest.

Accurate evaluation of start of season (SOS) changes is essential to assess the ecosystem’s response to climate change. Smoothing method is an understudied factor that can lead to great uncertainties in SOS extraction, and the applicable situation for different smoothing methods and the impact of smoothing parameters on SOS extraction accuracy are of critical importance to be clarified. In this paper, we use MOD13Q1 normalized difference vegetation index (NDVI) data and SOS observations from eight agrometeorological stations on the Qinghai–Tibetan Plateau (QTP) during 2001–2011 to compare the SOS extraction accuracies of six popular smoothing methods (Changing Weight (CW), Savitzky-Golay (SG), Asymmetric Gaussian (AG), Double-logistic (DL), Whittaker Smoother (WS) and Harmonic Analysis of NDVI Time-Series (HANTS)) for two types of different SOS extraction methods (dynamic threshold (DT) with 9 different thresholds and double logistic (Zhang)). Furthermore, a parameter sensitivity analysis for each smoothing method is performed to quantify the impacts of smoothing parameters on SOS extraction. Finally, the suggested smoothing methods and reference ranges for the parameters of different smoothing methods were given for grassland phenology extraction on the QTP. The main conclusions are as follows: (1) the smoothing methods and SOS extraction methods jointly determine the SOS extraction accuracy, and a bad denoising performance of smoothing method does not necessarily lead to a low SOS extraction accuracy; (2) the default parameters for most smoothing methods can result in acceptable SOS extraction accuracies, but for some smoothing methods (e.g., WS) a parameter optimization is necessary, and the optimal parameters of the smoothing method can increase the R2 and reduce the RMSE of SOS extraction by up to 25% and 331%; (3) The main influencing factor of the SOS extraction using the DT method is the stability of the minimum value in the NDVI curve, and for the Zhang method the curve shape before the peak of the NDVI curve impacts the most; (4) HANTS is the most stable method no matter with (fitness = 35.05) or without parameter optimization (fitness = 33.52), which is recommended for QTP grassland SOS extraction. The findings of this study imply that remote sensing-based vegetation phenology extraction can be highly uncertain, and a careful selection and parameterization of the time-series smoothing method should be taken to achieve an accurate result.

Here we present a new index, observed snow probability (OSP), to explore the spatiotemporal characteristics of snow cover. We determine the distribution of OSP values across the Tibetan Plateau (TP) by analyzing Moderate Resolution Imaging Spectroradiometer (MODIS) data from the 2002-2016 time period to investigate the relationship between OSP and two terrain factors, elevation and aspect. Our results suggest that the distribution of OSP across the TP exhibits a large spatiotemporal heterogeneity with high OSPs mainly concentrated along the southern and western edges of the TP, which is strongly linked to the supply of moist air. The distribution of snow cover is also heavily dependent on elevation, with higher OSPs at higher elevations. Aspect is another key factor that significantly influences OSP, with higher OSPs corresponding to the shady aspects and windward slopes.

为了探索遥感卫星影像和无人机调查在实际遥感面积估算中的调查效果和适用性,以广东省阳春市晚稻为例,采用卫星影像与无人机调查结果相结合,使用两种面积估算方法进行2013年晚稻种植面积估算.实验结果表明,比估计和回归估计方法的估算结果分别为22 501.1 hm2和22 781.1 hm2,二者的CV分别为8.84％和1.03％.研究结果表明:①无人机调查可获得高质量的面积测量信息;②优化后的样本能够支持面积估算过程,但其理论意义需要更进一步地讨论与证明;③卫星数据与无人机调查数据相结合的方法可以提供满足统计精度要求的面积测量结果,具有良好的应用前景.

Flood is the most frequent disaster in the world, which can do harm to agriculture and threat to food security. Using kernel based supervised classifier to execute change detection for multi-temporal remote sensing data is a common method for flood disaster monitoring and assessment, and kernel Fisher's discrimination analysis (KFDA) is one of them. Choosing training sample by visual interpretation is an important step, but difficult and wasting time, for the reason that a great amount of the flooded pixels are heterogeneous. In this study, we proposed an automatic sample extraction method, finding pixels in relative homogeneous areas by multiresolution segmentation and zonal standard deviation calculating, and then assigning sample class via clustering or linear discrimination of some specific index. The results showed that overall accuracy could reach 91.57% and the Kappa coefficient was 0.8316. The method we proposed was proved to be efficient.