Search Results (961)

Paddy rice mapping is critical for food security and environmental management, yet existing methods face challenges such as cloud obstruction in optical data and speckle noise in synthetic aperture radar (SAR). To address these limitations, this study introduces PRICOS, a novel paddy rice index that systematically combines time series Sentinel-2 optical features (NDVI for bare soil/peak growth, MNDWI for the submerged stages) and Sentinel-1 SAR backscatter (VH polarization for structural dynamics). PRICOS automates key phenological stage detection through harmonic fitting and dynamic thresholding, requiring only 10–20 samples per region to define rice growth cycles. Validated across six agroclimatic regions, PRICOS achieved overall accuracy (OA) and F1 scores of 0.90–0.98, outperforming existing indices like SPRI (OA: 0.79–0.95) and TWDTW (OA: 0.85–0.92). By integrating multi-sensor data with minimal sample dependency, PRICOS provides a robust, adaptable solution for large-scale paddy rice mapping, advancing precision agriculture and climate change mitigation efforts. Full article

The timely and precise estimation of crop aboveground biomass (AGB) is crucial for evaluating crop development and forecasting yields. The objective is to examine the differences, advantages, and limitations between time series parameters and single-time-phase indicators derived from various vegetation indices in AGB estimation. Moreover, we aim to quantitatively investigate and elucidate the impact of the topographic and geographic conditions of the study region on the estimation of highland barley AGB. Results indicate that AGB simulations utilizing time series parameters from vegetation index time series (VI-TS) curves yield satisfactory results for all three VIs, with the exception of the Normalized Difference Vegetation Index (NDVI), which encounters saturation issues. The performance metrics are as follows: the Enhanced Vegetation Index (EVI) (R² = 0.73, RMSE = 20.24 g/m²), the Soil-Adjusted Vegetation Index (SAVI) (R² = 0.67, RMSE = 20.97 g/m²), and the Normalized Difference Mountain Vegetation Index (NDMVI) (R² = 0.54, RMSE = 24.92 g/m²). The inclusion of our quantitative terrain factor improves the simulation accuracies of NDVI, SAVI, and NDMVI. Overall, the terrain factor has a beneficial impact on the highland barley AGB simulation outcomes. This study establishes a foundational framework for the timely and precise estimation of highland barley biomass, crucial for monitoring agricultural production in plateau mountainous regions. Full article

Vegetation anomalies are frequently occurring and may greatly affect ecological functions. Many near-real-time (NRT) detection methods have been developed to detect these anomalies in a timely manner whenever a new satellite observation is available. However, the undisturbed vegetation conditions captured by these methods are only applicable to a particular pixel or vegetation type, resulting in a lack of universality. Also, most methods that use single characteristic parameter may ignore the multi-spectral expression of vegetation anomalies. In this study, we developed a universal framework to simultaneously detect various vegetation anomalies in NRT from Landsat observations. Firstly, Landsat surface reflectance data from the Benchmark Land Multisite Analysis and Intercomparison of Products (BELMANIP) sites were selected as a reference vegetation dataset to calculate the normalized difference vegetation index (NDVI) and the normalized burn ratio (NBR), which describe vegetation conditions from the perspectives of greenness and moisture, respectively. After the elimination of cloud-contaminated pixels, the high-quality NDVI and NBR data over the BELMANIP sites were further normalized in order to remove the differences in the growth of the varying vegetation. Based on the normalized NDVI and NBR, kernel density estimation (KDE) was used to create a universal measure of undisturbed vegetation, which described the uniform spectral frequency distribution of different undisturbed vegetation with a series of accumulated probabilities on a monthly basis. Whenever a new Landsat observation is collected, the vegetation anomalies are determined according to the universal measure in NRT. To demonstrate the potential of this framework, three study areas with different anomaly types (deforestation, fire event, and insect outbreak) in distinct ecozones (rainforest, coniferous forest, and deciduous broad-leaf forest) were used. The quantitative analyses showed generally high overall accuracies (>90% with the kappa >0.82). The user accuracy for the fire event and the producer accuracy for the earlier insect infestation were relatively lower. The accuracies may be affected by the complexity of the land surface, the quality of the Landsat image, and the accumulated probability threshold. Full article

In recent years, machine learning methods have garnered significant attention in the field of crop recognition, playing a crucial role in obtaining spatial distribution information and understanding dynamic changes in planting areas. However, research in smaller plots within mountainous regions remains relatively limited. This study focuses on Shangzhou District in Shangluo City, Shaanxi Province, utilizing a dataset of high-resolution remote sensing images (GF-1, ZY1-02D, ZY-3) collected over seven months in 2021 to calculate the normalized difference vegetation index (NDVI) and construct a time series. By integrating field survey results with time series images and Google Earth for visual interpretation, the NDVI time series curve for maize was analyzed. The Random Forest (RF) classification algorithm was employed for maize recognition, and comparative analyses of classification accuracy were conducted using Support Vector Machine (SVM), Gaussian Naive Bayes (GNB), and Artificial Neural Network (ANN). The results demonstrate that the random forest algorithm achieved the highest accuracy, with an overall accuracy of 94.88% and a Kappa coefficient of 0.94, both surpassing those of the other classification methods and yielding satisfactory overall results. This study confirms the feasibility of using time series high-resolution remote sensing images for precise crop extraction in the southern mountainous regions of China, providing valuable scientific support for optimizing land resource use and enhancing agricultural productivity. Full article

This study investigates decadal trends in vegetation cover in Djibouti from 1990 to 2020, addressing challenges related to its arid climate and limited resources. Using Digital Earth Africa’s Open Data Cube and thirty years of Landsat imagery, change detection algorithms, and statistical analysis, this research explores vegetation dynamics at various spatial and temporal scales. Studies on change detection have advanced the field through exploring Landsat time series and diverse algorithms, but face limitations in handling data inconsistencies, integrating methods, and addressing practical and socio-environmental challenges. The results, obtained through change detection using NDVI differencing and Welch’s t-test, reveal significant trends in vegetation across Djibouti’s administrative and countrywide levels. Results show significant countrywide vegetative loss from 1990 to 2010, but recovery from 2010 to 2020, as evidenced by Welch’s t-test results. This disproved the Null Hypothesis of no trend and confirmed significant trends across all regions and resolutions analyzed. The findings provide important information for policymakers, land managers, and conservationists, providing awareness into patterns of Djibouti’s vegetation trends in the face of future climate change. The use of Open Data Cube and cloud computing enhances research capacity, allowing for the rapid and repeated analysis of larger time periods and geographical regions. Full article

This study investigates the complex interaction of biophysical and meteorological factors that drive evapotranspiration (ET) in saline environments. Leveraging a total of 182 cloud-free Landsat 5/8 time-series data from 1988 to 2019, we employed the Surface Energy Balance System (SEBS) model to quantify ET and investigate its relationships with soil salinity, vegetation cover, groundwater depth, and landscape metrics. We validated the predicted ET at two experimental sites using ET observation calculated by a water balance model. The result shows an R² of 0.78 and RMSE of 0.91 mm for the SEBS predicted ET, indicating high accuracy of the ET estimation. We detected abandoned saline farmland patches across Hetao and extracted the normalized difference vegetation index (NDVI), salinization index (SI), and the predicted ET for analysis. The results indicate that ET is negatively correlated with SI with a Pearson correlation coefficient (r) up to −0.7, while ET is positively correlated with NDVI (r = 0.4). In addition, we designed a control-variable experiment in the Yichang subdistrict to investigate the effects of groundwater depth, land aggregation index, soil salinity index, and the area of abandoned saline farmland patches on ET. The results indicate that increased NDVI could significantly enhance ET, while smaller saline farmland patches exhibited greater sensitivity to groundwater recharge, with higher averaged ET than larger patches. Moreover, we analyzed factor importance using Lasso regression and Random Forest (RF) regression. The result shows that the ranking of the importance of the features is consistent for both methods and for all the features, with NDVI being the most important (with an RF importance score of 0.4), followed by groundwater table depth (GWTD), and the influence of the surface area of abandoned saline farmland being the weakest. We found that smaller patches of abandoned saline farmland were more sensitive to changes in groundwater levels induced by nearby irrigation, affecting their averaged ET more dynamically than larger patches. Decreasing patch size over time indicates ongoing changes in land management and ecological conditions. This study, through a multifactor analysis of ET in abandoned saline farmland and its intrinsic factors, provides a reference for evaluating the dry drainage efficiency of abandoned saline farmland in a dry drainage system. Full article

Grassland ecosystems provide a range of services in semi-arid and arid regions. However, they have significantly declined due to overgrazing and desertification. In the current study, we employed Landsat time series data (TM, OLI, OLI-2) spanning from 1990 to 2024, combined with vegetation indices such as NDVI and SAVI, along with NDWI and digital elevation models (DEMs), to analyze land cover dynamics in the Ugii Lake watershed area, Mongolia. By integrating multisource remote sensing data into the advanced XGBoost (extreme gradient boosting) machine learning algorithm, we achieved high classification accuracy, with overall accuracies exceeding 94% and Kappa coefficients greater than 0.92. The results revealed a decline in montane grasslands (−6.2%) and an increase in other grassland types, suggesting ecosystem redistribution influenced by climatic and anthropogenic factors. Cropland exhibited resilience, recovering from a significant decline in the 1990s to moderate growth by 2024. Our findings highlight the stability of barren land and underscore pressures from ecological degradation and human activities. This study provides up-to-date statistical data to support decision-making in the conservation and sustainable management of grassland ecosystems in Mongolia under changing climatic conditions. Full article

Trees are indispensable to ecosystems, yet mortality rates have been increasing due to the abnormal changes in forest growth environments caused by frequent extreme weather events associated with global climate warming. Consequently, the need to monitor, assess, and predict tree mortality has become increasingly urgent to better address climate change and protect forest ecosystems. Over the past few decades, remote sensing has been widely applied to vegetation mortality observation due to its significant advantages. Here, we reviewed and analyzed the major research advancements in the application of remote sensing for tree mortality monitoring, using the Web of Science Core Collection database, covering the period from 1998 to the first half of 2024. We comprehensively summarized the use of different platforms (satellite and UAV) for data acquisition, the application of various sensors (multispectral, hyperspectral, and radar) as image data sources, the primary indicators, the classification models used in monitoring tree mortality, and the influence of tree mortality. Our findings indicated that satellite-based optical remote sensing data were the primary data source for tree mortality monitoring, accounting for 80% of existing studies. Time-series optical remote sensing data have emerged as a crucial direction for enhancing the accuracy of vegetation mortality monitoring. In recent years, studies utilizing airborne LiDAR have shown an increasing trend, accounting for 48% of UAV-based research. NDVI was the most commonly used remote sensing indicator, and most studies incorporated meteorological and climatic factors as environmental variables. Machine learning was increasingly favored for remote sensing data analysis, with Random Forest being the most widely used classification model. People are more focused on the impacts of tree mortality on water and carbon. Finally, we discussed the challenges in monitoring and evaluating tree mortality through remote sensing and offered perspectives for future developments. Full article

The clumping index (CI) is a key structural parameter that quantifies the nonrandomness of the spatial distribution of vegetation canopy leaves. Investigating seasonal variations in the CI is crucial, especially for estimating the leaf area index (LAI) and studying global carbon and water cycles. However, accurate estimations of the seasonal CI have substantial challenges, e.g., from the need for accurate hot spot measurements, i.e., the typical feature of the bidirectional reflectance distribution function (BRDF) shape in the current CI algorithm framework. Therefore, deriving a phenologically simplified stable CI product from a high-frequency CI product (e.g., 8 days) to reduce the uncertainty of CI seasonality and simplify CI applications remains important. In this study, we applied the discrete Fourier transform and an improved dynamic threshold method to estimate the start of season (SOS) and end of season (EOS) from the CI time series and indicated that the CI exhibits significant seasonal variation characteristics that are generally consistent with the MODIS land surface phenology (LSP) product (MCD12Q2), although seasonal differences between them probably exist. Second, we divided the vegetation cycle into two phenological stages based on the MODIS LSP product, ignoring the differences mentioned above, i.e., the leaf-on season (LOS, from greenup to dormancy) and the leaf-off season (LFS, after dormancy and before greenup of the next vegetation cycle), and developed the phenologically simplified two-stage CI product for the years 2001–2020 using the MODIS 8-day CI product suite. Finally, we assessed the accuracy of this CI product (RMSE = 0.06, bias = 0.01) via 95 datasets from 14 field-measured sites globally. This study revealed that the CI exhibited an approximately inverse trend in terms of phenological variation compared with the NDVI. Globally, based on the phenologically simplified two-stage CI product, the CI_LOS is smaller than the CI_LFS across all land cover types. Compared with the LFS stage, the quality for this CI product is better in the LOS stage, where the QA is basically identified as 0 and 1, accounting for more than ~90% of the total quality flag, which is significantly higher than that in the LFS stage (~60%). This study provides relatively reliable CI datasets that capture the general trend of seasonal CI variations and simplify potential applications in modeling ecological, meteorological, and other surface processes at both global and regional scales. Therefore, this study provides both new perspectives and datasets for future research in relation to CI and other biophysical parameters, e.g., the LAI. Full article

Solar-induced chlorophyll fluorescence (SIF), as a direct indicator of vegetation photosynthesis, offers a more accurate measure of plant photosynthetic dynamics than traditional vegetation indices. However, the current SIF satellite products have low spatial resolution, limiting their application in fine-scale agricultural research. To address this, we leveraged MODIS data at a 1 km resolution, including bands b1, b2, b3, and b4, alongside indices such as the NDVI, EVI, NIRv, OSAVI, SAVI, LAI, FPAR, and LST, covering October 2018 to May 2020 for Shandong Province, China. Using the Random Forest (RF) model, we downscaled SIF data from 0.05° to 1 km based on invariant spatial scaling theory, focusing on the winter wheat growth cycle. Various machine learning models, including CNN, Stacking, Extreme Random Trees, AdaBoost, and GBDT, were compared, with Random Forest yielding the best performance, achieving R² = 0.931, RMSE = 0.052 mW/m²/nm/sr, and MAE = 0.031 mW/m²/nm/sr for 2018–2019 and R² = 0.926, RMSE = 0.058 mW/m²/nm/sr, and MAE = 0.034 mW/m²/nm/sr for 2019–2020. The downscaled SIF products showed a strong correlation with TanSIF and GOSIF products (R² > 0.8), and consistent trends with GPP further confirmed the reliability of the 1 km SIF product. Additionally, a time series analysis of Shandong Province’s wheat-growing areas revealed a strong correlation (R² > 0.8) between SIF and multiple vegetation indices, underscoring its utility for regional crop monitoring. Full article

Potato, a vital food and cash crop, necessitates precise identification and area estimation for effective planting planning, market regulation, and yield forecasting. However, extracting large-scale crop areas using satellite remote sensing is fraught with challenges, such as low spatial resolution, cloud interference, and revisit cycle limitations, impeding the creation of high-quality time–series datasets. In this study, we developed a high-resolution vegetation index time–series by calculating coordination coefficients and integrating reflectance data from Landsat-8, Landsat-9, and Sentinel-2 satellites. The vegetation index time–series were enhanced through using linear interpolation and Savitzky–Golay (S-G) filtering to reconstruct high-quality data. We employed the harmonic analysis of NDVI time–series (HANTS) method to extract features from the time–series and evaluated the classification accuracy across five feature sets: vegetation index time–series features, band means, vegetation index means, texture features, and color space features. The Random Forest (RF) model, utilizing the full feature set, emerged as the most accurate, achieving a precision rate of 0.97 and a kappa value of 0.94. We further refined the feature subset using the SHAP-SFS feature selection method, leading to the SHAP-SFS-RF classification approach for differentiating potato from non-potato crops. This approach enhanced accuracy by approximately 0.1 and kappa value by around 0.2 compared to the RF model, with the extracted areas closely aligning with statistical yearbook data. Our study successfully achieved the accurate extraction of potato planting areas at the county level, offering novel insights and methodologies for related research fields. Full article

The widespread oil extraction in the Niger Delta and the impacts on different types of vegetation are poorly understood due to lack of ground access. This study aims to determine the impact of oil spills on vegetation in the Niger Delta using a Landsat satellite-derived normalised difference vegetation index (NDVI). The impact of oil spill volume and time after an oil spill on the health of different types of vegetation were evaluated, and the time series of the changes in NDVI were analysed to determine the medium- and long-term responses of vegetation to oil spill exposure, using a combination of linear regression and paired t-tests. With regards to the relationship between spill volume and NDVI, a moderate correlation (R² = 0.5018) was observed for spill volumes in the range of 401–1000 barrels for sparse vegetation, for volumes greater than 1000 barrels for dense vegetation (R² = 0.4356), whilst no correlation was found for mangrove vegetation at any range of spill volume. Similarly, the results of the paired t-test confirmed a significant difference (p-value < 0.05) between the change in NDVI values for spill sites and non-spill sites for all vegetation types, with the sparse vegetation being the most affected of the three types. However, the impact of the oil spill on vegetation over a period is not statistically significant. The outcomes of this study provide insights into how different types of vegetation in the Niger Delta respond to oil spills, which could ultimately help in designing an oil spill clean-up program to reduce the impact on the environment. Full article

A variety of drought indices were developed to monitor different types of drought, a significant natural hazard with multidimensional impacts. However, no single drought index can capture all dimensions of drought, necessitating a composite drought index (CDI) that integrates a range of indicators. This study proposes a CDI using principal component analysis (PCA) and a temporal dependence assessment (TDA) applied to time series of drought indices in a spatially distributed approach at the basin level. The indices considered include the Simplified Standardized Precipitation Index (SSPI), Simplified Standardized Precipitation-Evapotranspiration Index (SSPEI), soil moisture (SM), Normalized Difference Vegetation Index (NDVI), and streamflow (SF) from two climatically distinct small-sized basins in Portugal. Lag correlation analyses revealed a high contemporaneous correlation between SSPI and SSPEI (r > 0.8) and weaker but significant lagged correlations with SF (r > 0.5) and SM (r > 0.4). NDVI showed lagged and negligible correlations with the other indices. PCA was iteratively applied to the lag correlation-removed matrix of drought indices for all grid points, repeating the procedure for several SSPI/SSPEI time scales. The first principal component (PC1), capturing the majority of the matrix’s variability, was extracted and represented as the CDI for each grid point. Alternatively, the CDI was computed by combining the first and second PCs, using their variances as contribution weights. As PC1 shows its highest loadings on SSPI and SSPEI, with median loading values above 0.52 in all grid points, the proposed CDI demonstrated the highest agreement with SSPI and SSPEI across all grid cells, followed by SM, SF, and NDVI. Comparing the CDI’s performance with an independent indicator such as PDSI, which is not involved in the CDI’s construction, validated the CDI’s ability to comprehensively monitor drought in the studied basins with different hydroclimatological characteristics. Further validation is suggested by including other drought indicators/variables such as crop yield, soil moisture from different layers, and/or groundwater levels. Full article

Spatial and temporal information about cropping patterns of single and multiple crops is important for monitoring crop production and land-use intensity. We used time-series MODIS NDVI 8-day composite data to develop annual cropping pattern products at a 250 m spatial resolution for China, covering the period from 2001 to 2023. To address the potential impacts of varying parameters in both data pre-processing and the peak detection algorithm on the accuracy of cropping pattern mapping, we employed a grid-search method to fine-tune these parameters. This process focused on optimizing the Savitzky–Golay smoothing window size and the peak width parameters using a calibration dataset. The results highlighted that an optimal combination of a five to seven MODIS composite window size in Savitzky–Golay smoothing and a peak width of four MODIS composites achieved good overall mapping accuracy. Pixel-wise accuracy assessments were conducted for the selected mapping years of 2001, 2011, and 2021. Overall accuracies were between 89.7% and 92.0%, with F1 scores ranging from 0.921 to 0.943. Nationally, this study observed a fluctuating trend in multiple cropping percentages, with a notable increase after 2013, suggesting shifts toward more intensive agricultural practices in recent years. At a finer spatial scale, the combination of Mann–Kendall and Sen’s slope analyses revealed that approximately 12.9% of 3 km analytical windows exhibited significant changes in cropping intensity. We observed spatial clusters of increasing and decreasing crop intensity trends across provinces such as Hebei, Shandong, Shaanxi, and Gansu. This study underscores the importance of data smoothing and peak detection methods in analyzing high temporal resolution remote sensing data. The generation of annual single/multiple cropping pattern maps at a 250 m spatial resolution enhances our comprehension of agricultural dynamics through time and across different regions. Full article

In recent decades, global ecosystems have increasingly faced impacts from heightened precipitation variability. Specifically, water availability is an essential factor in wetland dynamics and has ecological importance in the high-Andean wetlands in both mountains and downstream ecosystems, particularly in semi-arid regions. This study focused on a chain of twelve high-Andean wetlands within the “Estero Derecho” nature sanctuary at the headwaters of the Elqui River in north-central Chile. The analysis of the spatiotemporal dynamics of precipitation and vegetation cover used the Landsat 5 and 8 Satellite imagery-derived normalized difference vegetation index (NDVI) and normalized difference moisture index (NDMI) time series during the austral summer (December–March). We employed time series, boxplots, and least-squares regression analyses to explore vegetation cover behavior in relation to precipitation, water quality, and vegetation indices. Precipitation had a marked influence on vegetation behavior, particularly during the Chilean “megadrought” phenomenon. For both the NDVI and NDMI indices and precipitation, negative trends in the time series were observed, along with a highly significant correlation with a one-year lag between both indices and precipitation. The analysis of the individual wetlands showed different vegetation cover behaviors, which were attributable to the altitude, terrain slope, and additional water inputs from streams that have also given rise to alluvial fans that exert a shaping influence on the wetlands. In addition, significant correlations between both indices and water quality parameters (CE, Cl, Mg, Na, and Fe) were identified. The findings of this study can be incorporated into the Sanctuary’s management plan and concretely assist communities involved with wetland conservation. Full article