Normalized Difference Vegetation Index Data Research Articles

DEEP LEARNING BASED MULTI TASK ROAD EXTRACTOR MODEL FOR PARCEL EXTRACTION AND CROP CLASSIFICATION USING KNOWLEDGE BASED NDVI TIME SERIES DATA

Abstract. The role of agriculture in food security is critical by the rapid increase of the global population. Food production should be strongly increased to secure and provide life necessities. Remote sensing provides a fruitful tool in agriculture management, and it provides advantages in terms of saving time and effort. This study aims to examine the feasibility of deep learning edge detection algorithm in order to automatically extract the agricultural parcel boundaries from the open-source Google Earth data. The potential of using Normalized Difference Vegetation Index (NDVI) in crop classification crop was also presented after analyzing the pattern of Rabi major crops (Wheat and Sugarcane) in Haridwar district, Uttarakhand, India. The advantage of Google Earth Engine cloud-based platform was exploited in generating NDVI data from Sentinel-2 satellite between October 2022 and February 2023 in order to save time and effort. To check the accuracy of the deep learning model, the value of the Mean Intersection of Union (mIoU) was tested and reached 0.79.To examine the results, ground truth data were collected in the study area using Unmanned Aerial Vehicles. The overall accuracy of the rule set-based classification reached 91.17%, and the kappa coefficient value was 0.82.

Multi-attention Generative Adversarial Network for multi-step vegetation indices forecasting using multivariate time series

Generative Adversarial Networks (GANs) are one of the most significant research directions in the field of Deep Learning (DL). GANs has received wide attention due to their outstanding ability to produce realistic-looking images and can implicitly learn rich distributions over Spatio-Temporal (ST) correlations in Multivariate Time Series (MTS). Forecasting using MTS remains a challenge that needs to be solved to make successful tasks in different ST related applications such as vegetation forecasting. Vegetation is an extremely valuable component of our global ecosystem, as well as an important indicator of land cover change dynamics and productivity. As a result, the Normalized Difference Vegetation Index (NDVI) is one of the most widely used indices in vegetation related studies. The challenges of accurately forecasting vegetation indices come from both the non-stationary ST correlations of the NDVI data and the complex impact of the external context factors, including weather and remote sensing indices. It is further more challenging for most existing one-step forecasting methods, making an accurate forecast over many future time slots. To achieve this goal, we propose a new multi-attention GAN model composed of three main parts. An encoder network first encodes the driving sequence into latent space vectors and store all the information carried by the driving sequence. A generator is then in charge of generating forecasting data by extracting long-term temporal patterns. Eventually, in order to increase forecasting accuracy, we use as a third part an improved discriminator for classification and feedback purposes. The suggested model has been extensively tested with real-world datasets, demonstrating its effectiveness and robustness compared to existing methods. The test results indicate high performance with a Coefficient of Determination (R2) of 0.95, Root Mean Square Error (RMSE) of 0.04, Mean Absolute Error (MAE) of 0.01, and Mean Absolute Percentage Error (MAPE) of 15.35.

Temporal and Spatial Variations in the Normalized Difference Vegetation Index in Shanxi Section of the Yellow River Basin and Coal Mines and Their Response to Climatic Factors

Investigating the spatiotemporal variations in the Normalized Difference Vegetation Index (NDVI) in the Shanxi section of the Yellow River Basin and its coal mining areas holds significant importance for dynamic vegetation monitoring and mining area management. This study employs MODIS NDVI data and combines various analytical methods, including trend analysis and coefficient of variation analysis, to reveal the characteristics of NDVI spatiotemporal variations and their response to climatic factors in the study area. The results indicate the following: (1) The overall NDVI in the Shanxi section of the Yellow River Basin exhibits a growth trend with an annual growth rate of 1.82% and a 36% increase. Among the mining areas, the NDVI increase is most prominent in the Hebaopian mining area with a 100% growth, while the QinYuan mining area shows the lowest increase at 21%; (2) The NDVI in the Shanxi section of the Yellow River Basin displays high fluctuations, with areas of moderate and high fluctuations accounting for 54.39% of the total. The Hebaopian mining area has a substantial portion of high-fluctuation areas at 38.85%; (3) According to the Hurst index analysis, future vegetation changes in the Shanxi section of the Yellow River Basin are uncertain, with approximately 9.77% of areas expected to continue improving; (4) The variations in the NDVI and climatic factors across the Shanxi section of the Yellow River Basin display spatial heterogeneity. The NDVI exhibits a positive correlation with both temperature and precipitation, with the correlation with precipitation being more pronounced than that with temperature. Precipitation exerts a more significant influence on the NDVI than temperature. These findings not only provide scientific guidance for vegetation restoration and area management in the Shanxi section of the Yellow River Basin and its mining areas but also serve as a scientific basis for decision making regarding vegetation management under the influence of climate change and human activities.

Increased sensitivity of vegetation to soil moisture and its key mechanisms in the Loess Plateau, China

AbstractSoil moisture (SM) plays a crucial role in connecting various ecological processes in terrestrial ecosystems, as vegetation growth heavily relies on SM availability. Despite many studies evaluating the impacts of SM on vegetation activity, changes in vegetation sensitivity to SM are still poorly understood. In this study, we evaluated the spatiotemporal changes of vegetation sensitivity to SM across the Loess Plateau (LP) region from 1982 to 2010 using satellite‐based normalised difference vegetation index (NDVI) and reanalysis SM data. Our results showed a significant increase in NDVI (slope = 0.0015/year, p < 0.001) and a slight decrease in SM (slope = −0.0001 m3/m3/year, p = 0.7) over the past 29 years, indicating a greening trend in the LP. By establishing a multiple linear model, we found that the sensitivity of vegetation dynamics to SM has significantly increased during the same period (slope = 0.029, p < 0.05). Spatially, vegetation in over half (51.2%) of the total area showed enhanced sensitivity to SM. Further analysis using the Lindeman–Merenda–Gold method showed that atmospheric CO2 was the major contributor (27.20%) to the sensitivity changes, followed by nitrogen deposition (23.70%) and air temperature (20.11%). Ecosystem structure changes (represented by non‐tree land cover fractions) and precipitation shifts contributed 16.13% and 12.83%, respectively, to sensitivity changes. These findings provide a better understanding of the spatiotemporal pattern and underlying mechanisms of ecosystem sensitivity to SM in the greening and warming LP.

Global patterns and drivers of spatial autocorrelation in plant communities in protected areas

AbstractAimStochastic and deterministic (biotic factors such as intraspecies competition or abiotic factors) processes affect the spatial patterns of ecological communities. These processes can be quantitatively assessed based on spatial autocorrelation (SAC) parameters. However, the global patterns of SAC and their differences across vegetation types remain unknown. We aimed to (1) quantitatively assess the SAC global pattern in plant communities and their variation from 1990 to 2020 and (2) identify the key drivers of SAC in plant communities in protected areas (PAs).LocationGlobal.Time Period1990–2020.Major Taxa StudiedTerrestrial plants.MethodsUsing normalized difference vegetation index data extracted from remote sensing datasets, we calculated the SAC parameters, including the Nugget, Partial Sill (PSill), and Sill, in 147 samples from 49 PAs in 1990, 2000, 2010, and 2020. The random forest model was employed to identify the drivers among 10 annual and 120 monthly climatic variables.ResultsThe SAC patterns of plant communities in global PAs differed across vegetation types. It reached the highest in grasslands and evergreen needleleaved forests, mainly explained by annual energy metrics, and the lowest in tropical evergreen rain forests around equator and boreal coniferous forests near the Arctic Circle. From 1990 to 2020, the Nugget, PSill, and Sill decreased by 92.36%, 58.36%, and 60.61%, respectively, indicating biological homogeneity. Monthly climatic variables explain SAC variations better than annual ones.Main ConclusionsFrom 1990 to 2020, the SAC parameters decreased sharply across vegetation types, indicating an obvious biologically homogenous trend that was mainly driven by monthly climatic variation, vary across sites and vegetation types. Our results highlight the need to assess the SAC in different vegetation types separately to understand the shifts of global plant communities under climate change.

Crop NDVI time series construction by fusing Sentinel-1, Sentinel-2, and environmental data with an ensemble-based framework

Satellite-based Normalized Difference Vegetation Index (NDVI) time series data are conducive for near-real-time (NRT) monitoring of crop progress, but they commonly suffer from data gaps due to cloudy weather conditions. Fortunately, Synthetic Aperture Radar (SAR) data, which are cloud-insensitive, has high potentials to fill these data gaps. However, the relationship between NDVI and SAR data is not generic and affected by various factors (e.g., crop canopy structure and phenology, soil exposure and humidity). Therefore, it is worth exploring to evaluate the relationships and develop a more robust SAR-NDVI fusion method for NDVI time series construction. In this study, we proposed and evaluated an ensemble-based data fusion framework that accounts for these factors, based on which dense NDVI time series for crop monitoring were constructed. The framework consisted of three steps: (1) NDVI was predicted from Sentinel-1 SAR data and auxiliary environmental factors using a random forest (RF) model; (2) an improved feature importance measurement method was proposed to reveal the different contributions of multisource data to the modeling, and suggestions for selecting optimal input parameters were presented; (3) the uncertainty of the predicted data was quantified by an ensemble-based method and incorporated into a Weighted Least Squares (WLS) method to construct dense NDVI time series. Results showed that the RF models were generally improved by the auxiliary data and achieved a satisfactory accuracy of NDVI estimation (R2 > 0.93, RMSE < 0.075) for both corn and soybean crops in Southwestern Ontario, Canada. The proposed method performed well in filling data gaps in both the vegetative and reproductive stages for corn and soybean, providing a practical and promising solution for continuous crop monitoring.

Early Crop Mapping Using Dynamic Ecoregion Clustering: A USA-Wide Study

Mapping target crops earlier than the harvest period is an essential task for improving agricultural productivity and decision-making. This paper presents a new method for early crop mapping for the entire conterminous USA (CONUS) land area using the Normalized Difference Vegetation Index (NDVI) and Enhanced Vegetation Index (EVI) data with a dynamic ecoregion clustering approach. Ecoregions, geographically distinct areas with unique ecological patterns and processes, provide a valuable framework for large-scale crop mapping. We conducted our dynamic ecoregion clustering by analyzing soil, climate, elevation, and slope data. This analysis facilitated the division of the cropland area within the CONUS into distinct ecoregions. Unlike static ecoregion clustering, which generates a single ecoregion map that remains unchanged over time, our dynamic ecoregion approach produces a unique ecoregion map for each year. This dynamic approach enables us to consider the year-to-year climate variations that significantly impact crop growth, enhancing the accuracy of our crop mapping process. Subsequently, a Random Forest classifier was employed to train individual models for each ecoregion. These models were trained using the time-series MODIS (Moderate Resolution Imaging Spectroradiometer) 250-m NDVI and EVI data retrieved from Google Earth Engine, covering the crop growth periods spanning from 2013 to 2017, and evaluated from 2018 to 2022. Ground truth data were sourced from the US Department of Agriculture’s (USDA) Cropland Data Layer (CDL) products. The evaluation results showed that the dynamic clustering method achieved higher accuracy than the static clustering method in early crop mapping in the entire CONUS. This study’s findings can be helpful for improving crop management and decision-making for agricultural activities by providing early and accurate crop mapping.

Spatiotemporally consistent global dataset of the GIMMS Normalized Difference Vegetation Index (PKU GIMMS NDVI) from 1982 to 2022

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 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).

Spatiotemporal variations and its driving factors of soil conservation services in the Three Gorges Reservoir area in China

Soil conservation services play a vital role in regulating ecosystem services to prevent soil erosion and ensure regional ecological security. Therefore, effective evaluation and quantification of soil conservation services in the Three Gorges Reservoir Area (TGRA) are conducive to sustainable management under future global change. In this study, based on a basic database, including land use/cover data, soil data, topographic data, meteorological data, and NDVI (Normalized Difference Vegetation Index) data as the basic databases, to evaluate the temporal and spatial changes of soil conservation services in the TGRA from 1990 to 2015 at a regional-scale level using the general soil loss equation. The results showed that forest ecosystems (including coniferous and broad-leaved mixed forests, coniferous forests, shrub forests, and broad-leaved forests) made a greater contribution (69%) to regulating soil conservation in TGRA, followed by farmland ecosystems (29%). In total TGRA, large spatial variation in soil conservation, such as the highest appeared in the northern hinterland, whereas the lowest was mostly shown in the northwest with relatively frequent human activities and developed industry and agriculture. In general, soil conservation in the TGRA ecosystem gradually increased from 1990 to 2015, with a total increase of 6%. In this period, with the effective implementation of ecological projects, such as the conversion of farmland to forest and natural forest protection, the distributed proportion of forest land area in total TGRA showed a significant increase. In the meantime, the increase of vegetation coverage also helps the restoration of ecosystem structure and function and the improvement of soil conservation services. Our findings will aid our knowledge regarding the ecosystem services of the TGRA and provide implications for future sustainable land management and ecological protection.

Assimilation of NDVI data in a land surface – Vegetation model for leaf area index predictions in a tree-grass ecosystem

ABSTRACT Periodic observations of vegetation index, such as the normalized difference vegetation index (NDVI), can be used for data assimilation in heterogenous ecosystems. Indeed, the new Sentinel 2 Multispectral instrument and Landsat 8 Operational Land Imager sensor data are available at such high temporal and spatial resolutions that can be used to detect the patches of the main vegetation components (grass and trees) of heterogenous ecosystems, and capture their dynamics. We demonstrate the possibility to merge grass and tree NDVI observations and models, to optimally provide robust predictions of grass and tree leaf area index. The proposed assimilation approach assimilates NDVI data through the Ensemble Kalman filter (EnKF) and dynamically calibrates a key vegetation dynamic model parameter, the maintenance respiration coefficient (ma ). In the presence of large bias of the grass and tree ma base values, only the use of the proposed assimilation approach removes the large bias in the biomass balance, dynamically calibrating maintenance respiration coefficients, and corrects the model. The performance of a land surface – vegetation model was improved by assimilating observations of NDVI. The effective impact of the proposed assimilation approach on the evapotranspiration and CO2 uptake predictions in the heterogenous ecosystem is also demonstrated.

Determination of Crop Coefficients and Evapotranspiration of Potato in a Semi-Arid Climate Using Canopy State Variables and Satellite-Based NDVI

Estimating crop coefficients and evapotranspiration (ET) accurately is crucial for optimizing irrigation. Remote sensing techniques using green canopy cover, leaf area index (LAI), and normalized difference vegetation index (NDVI) have been applied to estimate basal crop coefficients (Kcb) and ET for different crops. However, analysis of the potential of these techniques to improve water management in irrigated potato (Solanum tuberosum L.) is still lacking. This study aimed to assess the modified nonlinear relationship between LAI, Kcb and NDVI in estimating crop coefficients (Kc) and ET of potato. Moreover, Kc and ET were derived from the measured fraction of green canopy cover (FGCC) and the FAO-56 approach. ET estimated from the FAO-56, FGCC and NDVI approaches were compared with the ET simulated using the LINTUL-Potato model. The results showed that the Kc values based on FGCC and NDVI were on average 0.16 lower than values based on FAO-56 Kc during the mid-season growing stage. ET estimated from FAO-56, FGCC and NDVI compared well with ET calculated by the LINTUL-Potato model, with RMSE values of 0.83, 0.79, and 0.78 mm day−1, respectively. These results indicate that dynamic crop coefficients and potato ET can be estimated from canopy cover and NDVI. The outcomes of this study will assist potato growers in determining crop water requirements using real-time ETo, canopy state variables and NDVI data from satellite images.

Changes in vegetation greenness and its response to precipitation seasonality in Central Asia from 1982 to 2022

Central Asia is the world’s largest azonal arid region, with strong seasonal precipitation patterns. Vegetation in this region is relatively sparse and extremely sensitive to climatic changes. However, long-term trends in vegetation in Central Asia are still unclear or even controversially recognized, hindering the assessment of climate change’s impact on regional sustainability. Here, we present the longest time series of vegetation index in Central Asia and investigated its response to precipitation seasonality from 1982 to 2022 by integrating normalized difference vegetation index data from the Global Inventory Monitoring and Modeling Studies and the Moderate Resolution Imaging Spectroradiometer. The results indicate a greening trend during 1982–2000 and a browning trend during 2000–2008. In contrast to previous studies, we detected a rapid greening trend during 2008–2022, largely resulted from a continuous warm-wet trend in Central Asia. In addition, strong spatial variation in vegetation is uncovered within the region, suggesting spatial differences in vegetation responding to contrasting precipitation seasonality. Under CMIP6 climate scenarios, spring wetting and summer drying are projected to prompt Central Asian vegetation change to a simultaneous greening south and browning north.

A new approach to improve precipitable water vapour estimations of Sentinel-3A satellite data using LST, elevation and NDVI over Iran

ABSTRACT The total precipitable water vapour (TPW) extraction algorithm using the Sentinel-3A Ocean and Land Colour Instrument (OLCI) has the potential to be improved on a regional scale. The aim of this study was to improve the TPW extraction algorithm of OLCI for the first time using environmental variables, including the Normalized Difference Vegetation Index (NDVI), land surface temperature (LST), and elevation from mean sea level, on a regional scale over Iran. A previously developed TPW recovery algorithm was applied to the OLCI data during cloud-free days throughout a one-year period (1 January–29 December 2020). The artificial neural network (ANN) methodology was utilized in eight models. The evaluation results revealed the effectiveness of the models varied based on the topography and climate of each station. The assessment findings demonstrated that model 2, which integrated LST, elevation, and NDVI data in the ANN framework, outperformed other models across the study area.

Spatiotemporal Analysis of Vegetation Cover in Relation to Its Driving Forces in Qinghai–Tibet Plateau

The Tibet Plateau of Qinghai supports complex vegetation types that are ecologically fragile and sensitive to climatic factors. Therefore, it is important to understand the changes in vegetation and the factors responsible for these changes and to maintain the ecosystem balance and promote sustainable development in the region. Therefore, this paper is based on annual SPOT/VEG NDVI (Normalized Difference Vegetation Index) data, land use data, topographic data, temperature data, and precipitation data from 1999 to 2019. The spatiotemporal variation in the NDVI over the Tibetan Plateau in the last 21 years and its response to different driving factors were investigated by using the Theil–Sen slope method, Mann–Kendall test, partial correlation analysis, and geographical detector method. The results showed that (1) the vegetation coverage on the Qinghai–Tibet Plateau showed an increasing trend from 1999 to 2019, with increases in approximately 67.00% of the plateau area. (2) The spatial differences in vegetation coverage were large; notably, low-density vegetation areas decreased obviously, moderate-density vegetation areas accounted for approximately 50% of the total area, high-density vegetation areas were the least common, and the overall growth rate was significant. (3) The NDVI was positively correlated with temperature and precipitation, and a positive correlation was observed in more than 66% of the region. (4) The order of the influence of single driving factors on the NDVI was as follows: precipitation &gt; soil type &gt; altitude &gt; temperature &gt; gradient &gt; slope &gt; population density &gt; GDP. (5) The combined effect of the factors was significantly higher than that of single driving factors, with a notable nonlinear influence. The interactions between meteorological factors, such as precipitation, and topographic factors, such as altitude, were important, with a q-value over 0.79. The results of this study provide some methodological support for the ecological conservation of the Tibetan Plateau, and at the same time establish a scientific and reasonable strategy for vegetation restoration.

Influence of Key Climate Factors on Desertification in Inner Mongolia

Desertification is a major environmental problem facing the world today, and climate change is an important factor influencing desertification. This study investigates the impact of changes in key climate factors on desertification based on normalized difference vegetation index data, precipitation data and evaporation data from Inner Mongolia between 1982 and 2020 using correlation analysis, regression modelling, and residual analysis. The results show that precipitation and evaporation are significantly correlated with mild desertification and severe desertification, respectively, with correlation coefficients reaching 0.98 and −0.96, respectively. In severely desertified areas in central-eastern Inner Mongolia, there is a high correlation between desertification and temperature, the characteristics of the correlation of average maximum and minimum temperatures with desertification are similar to those of the correlation of average temperature with desertification, and the average maximum and minimum temperatures are well correlated with mild desertification, with correlation coefficients as high as 0.98 and 0.978, respectively. Climate contribution accounts for 97% of desertification in severely desertified areas, indicating that climate change has increased desertification in these areas. In regions with improved desertification, approximately 75% are primarily influenced by climate change (with a relative contribution greater than 50%), with climate factors exhibiting a relative contribution greater than 75% to desertification in 30% of these regions.

Is deeper always better? Evaluating deep learning models for yield forecasting with small data

Predicting crop yields, and especially anomalously low yields, is of special importance for food insecure countries. In this study, we investigate a flexible deep learning approach to forecast crop yield at the provincial administrative level based on deep 1D and 2D convolutional neural networks using limited data. This approach meets the operational requirements—public and global records of satellite data in an application ready format with near real time updates—and can be transferred to any country with reliable yield statistics. Three-dimensional histograms of normalized difference vegetation index (NDVI) and climate data are used as input to the 2D model, while simple administrative-level time series averages of NDVI and climate data to the 1D model. The best model architecture is automatically identified during efficient and extensive hyperparameter optimization. To demonstrate the relevance of this approach, we hindcast (2002–2018) the yields of Algeria’s three main crops (barley, durum and soft wheat) and contrast the model’s performance with machine learning algorithms and conventional benchmark models used in a previous study. Simple benchmarks such as peak NDVI remained challenging to outperform while machine learning models were superior to deep learning models for all forecasting months and all tested crops. We attribute the poor performance of deep learning to the small size of the dataset available.

Evaluation of Physiological Changes in Flowering Dogwood under Drought Conditions in a Container Production System

Flowering dogwoods (Cornus florida L.) are drought-sensitive ornamental trees. Two trials (in 2021 and 2022) were conducted to evaluate the physiological changes induced as a result of drought conditions. In an outdoor setting, trees were organized in a randomized complete block design. Three different irrigation treatments were applied at 125%, 25%, and 10% (control, moderate, and severe drought, respectively) of their daily water usage (evapotranspiration). The two physiological parameters normalized difference vegetation index (NDVI) and leaf moisture potential were collected every week for 1 month. Plant growth data (height and width) were collected at the beginning and the end of the study. Normalized difference vegetation index data collected with a handheld NDVI meter and a Sentera NDVI sensor mounted on an unmanned aerial vehicle (UAV) were correlated for ground truthing. In 2021, control plants had a greater plant width increase and shoot biomass, whereas no significant differences in growth were observed among the treatments in 2022. In both trials, the NDVI was the greatest for control plants compared with the other treatments on days 7, 14, 21, and 27. In both studies, no differences were observed for leaf moisture potential on day 7, but was greatest for controls on days 14, 21, and 27. The correlation between the handheld NDVI and the UAV NDVI was found to be strong and positive, ranging from 0.84 to 0.93 (trial 1: P ≤ 0.0001, P ≤ 0.0001, P = 0.0002, and P ≤ 0.0001; trial 2: P = 0.0002, P ≤ 0.0001, P ≤ 0.0001, and P ≤ 0.0001 for weeks 1–4, respectively). This information will be applicable to understanding the physiology of the crop and the inclusion of emerging technology in crop production and monitoring.

Exploring the Spatiotemporal Dynamics of CO2 Emissions through a Combination of Nighttime Light and MODIS NDVI Data

Climate change caused by CO2 emissions is posing a huge challenge to human survival, and it is crucial to precisely understand the spatial and temporal patterns and driving forces of CO2 emissions in real time. However, the available CO2 emission data are usually converted from fossil fuel combustion, which cannot capture spatial differences. Nighttime light (NTL) data can reveal human activities in detail and constitute the shortage of statistical data. Although NTL can be used as an indirect representation of CO2 emissions, NTL data have limited utility. Therefore, it is necessary to develop a model that can capture spatiotemporal variations in CO2 emissions at a fine scale. In this paper, we used the nighttime light and the Moderate Resolution Imaging Spectroradiometer (MODIS) normalized difference vegetation index (NDVI), and proposed a normalized urban index based on combination variables (NUI-CV) to improve estimated CO2 emissions. Based on this index, we used the Theil–Sen and Mann–Kendall trend analysis, standard deviational ellipse, and a spatial economics model to explore the spatial and temporal dynamics and influencing factors of CO2 emissions over the period of 2000–2020. The experimental results indicate the following: (1) NUI-CV is more suitable than NTL for estimating the CO2 emissions with a 6% increase in average R2. (2) The center of China’s CO2 emissions lies in the eastern regions and is gradually moving west. (3) Changes in industrial structure can strongly influence changes in CO2 emissions, the tertiary sector playing an important role in carbon reduction.

An improved change detection method for tacking remote sensing time series trends

ABSTRACT To improve the accuracy of detecting changes in remote sensing time series, an improved algorithm based on the combination of the antileakage least-squares spectral analysis (ALLSSA) algorithm and detecting breakpoints and estimating segments in trends (DBEST) algorithm is proposed and applied. The method uses the ALLSSA algorithm to decompose the time series and identify the trend components in the time series. Then, the trend segmentation mechanism of the DBEST algorithm is used to detect the changes in the trend component. In this paper, the improved algorithm is evaluated using a simulated time series data set, a time series data set with multiple change points, and data set based on the moderate resolution imaging spectroradiometer (MODIS) normalized difference vegetation index (NDVI) remote sensing time series. The results demonstrate that the average detection accuracies of the improved algorithm and DBEST algorithm are 98.4% and 85.2%, respectively, for the simulated time series data set. For the time series data set with multiple change points, the average root mean square errors (RMSEs) of the trend data for the improved and DBEST algorithms are 0.0386 and 0.0331, respectively. The mean normalized residual norms (MNRNs) of the improved and DBEST algorithms are 0.0252 and 0.0351, respectively. Finally, the improved algorithm, DBEST algorithm, and breaks for additive season and trend (BFAST) algorithm are applied to MODIS NDVI data, and their performance with remote sensing data is compared. The improved algorithm has higher detection accuracy and a smaller MNRN, indicating that more information is included in the trend and seasonal components. Therefore, the proposed method is useful for analysing trends in remote sensing time series data.

A spatio-temporal fusion method based on Landsat and MODIS normalized vegetation index data

Abstract. Normalized difference vegetation index (NDVI) can simply and effectively reflect the growth status of plants, and has a linear relationship with vegetation coverage, thus it is an important indicator to identify vegetation growth status and coverage. However, the commonly used MODIS NDVI or Landsat NDVI cannot simultaneously achieve the high temporal and spatial resolution. To this end, we proposed a generic and automatic method to fuse MODIS and Landsat satellite vegetation index (NDVI) into daily high-resolution products which takes time series NDVI data from MODIS/Landsat as input, filters noise pixels, and generates high-resolution and high-frequency products through a fusion process of temporal-linearly interpolation to increase the temporal resolution and spatial filtering to alleviate the blocky artifacts from MODIS pixel border. The fused NDVI maps are visually compared to evaluate the performance of this method, and more quantitative assessment will be continued in the next step. This study can provide important support for large-scale long-term surface vegetation monitoring.