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This paper proposes a workflow to assess the uncertainty of the Normalized Difference Vegetation Index (NDVI), a critical index used in precision agriculture to determine plant health. From a metrological perspective, it is crucial to evaluate the quality of vegetation indices, which are usually obtained by processing multispectral images for measuring vegetation, soil, and environmental parameters. For this reason, it is important to assess how the NVDI measurement is affected by the camera characteristics, light environmental conditions, as well as atmospheric and seasonal/weather conditions. The proposed study investigates the impact of atmospheric conditions on solar irradiation and vegetation reflection captured by a multispectral UAV camera in the red and near-infrared bands and the variation of the nominal wavelengths of the camera in these bands. Specifically, the study examines the influence of atmospheric conditions in three scenarios: dry–clear, humid–hazy, and a combination of both. Furthermore, this investigation takes into account solar irradiance variability and the signal-to-noise ratio (SNR) of the camera. Through Monte Carlo simulations, a sensitivity analysis is carried out against each of the above-mentioned uncertainty sources and their combination. The obtained results demonstrate that the main contributors to the NVDI uncertainty are the atmospheric conditions, the nominal wavelength tolerance of the camera, and the variability of the NDVI values within the considered leaf conditions (dry and fresh). [ABSTRACT FROM AUTHOR]

The normalized difference vegetation index (NDVI) can depict the status of vegetation growth and coverage in grasslands, whereas coarse spatial resolution, cloud cover, and vegetation phenology limit its applicability in fine-scale research, especially in areas covering various vegetation or in fragmented landscapes. In this study, a methodology was developed for obtaining the 30 m annual maximum NDVI to overcome these shortcomings. First, the Landsat NDVI was simulated by fusing Landsat and MODIS NDVI by using the enhanced spatial and temporal adaptive reflectance fusion model (ESTARFM), and then a single-peaked symmetric logistic model was employed to fit the Landsat NDVI data and derive the maximum NDVI in a year. The annual maximum NDVI was then used as a season-independent substitute to monitor grassland variation from 2001 to 2022 in a typical area covering the major vegetation types in the Qinghai Lake Basin. The major conclusions are as follows: (1) Our method for reconstructing the NDVI time series yielded higher accuracy than the existing dataset. The root mean square error (RMSE) for 91.8% of the pixels was less than 0.1. (2) The annual maximum NDVI from 2001 to 2022 exhibited spatial distribution characteristics, with higher values in the northern and southern regions and lower values in the central area. In addition, the earlier vegetation growth maximum dates were related to the vegetation type and accompanied by higher NDVI maxima in the study area. (3) The overall interannual variation showed a slight increasing trend from 2001 to 2022, and the degraded area was characterized as patches and was dominated by Alpine kobresia spp., Forb Meadow, whose change resulted from a combination of permafrost degradation, overgrazing, and rodent infestation and should be given more attention in the Qinghai Lake Basin. [ABSTRACT FROM AUTHOR]

The advancement of remote sensing technology has led to the development of sophisticated image processing methods that yield highly accurate land cover classification information, minimizing misinterpretations. The Normalized Difference Vegetation Index (NDVI) is a widely utilized method in remote sensing for measuring green vegetation. A significant portion of the Jember Regency area is covered by vegetation. This study aimed to identify various land cover types in the Jember Regency area, quantify the area for each classification, and establish the NDVI value ranges for each type of cover. Sentinel-2 was employed as the primary data source, and the NDVI method was utilized for land cover classification in the Jember Regency. The region exhibited diverse land cover types. Data from Sentinel-2A captured in June and October 2019 were chosen due to their accessibility, open-source nature, and adequate spectral, spatial, and temporal resolution. The classification in this study encompassed five classes: water bodies, settlements, dry fields, irrigated paddy fields, and forests. Error analysis was conducted using a confusion matrix with the Overall and Kappa algorithms. The accuracy results for June indicated a Kappa Accuracy of 37.7% and Overall Accuracy of 54.5%. In October, the Kappa Accuracy increased to 39.9%, and the Overall Accuracy reached 56.5%. In conclusion, the NDVI method did not meet the criteria for accurately interpreting land cover classification. [ABSTRACT FROM AUTHOR]

The high-resolution temporal records (1738–2018 CE) of the dendrochronological analysis on Cedrus deodara from Uttarakhand and Himachal Pradesh in the western Himalayan region were generated. An analysis of the association between the Normalized Difference Vegetation Index (NDVI) and tree-ring radial growth indicated a significant positive relationship from April to July. The association between tree rings and other hydroclimatic variables was used to further evaluate this relationship. Based on the observation, mean April–July NDVI variability since 1790 CE was reconstructed based on the regional tree-ring chronology. Comparisons of the reconstructed NDVI with gridded precipitation, drought records, and proxy-based hydroclimatic reconstructions revealed a consistent agreement over a large spatial domain extending beyond the study region. We observed a correlation between the region's NDVI browning (reduction) and past droughts and famines. Comparison has been made to the regional proxy-based hydroclimatic reconstruction and stable oxygen isotope datasets with the present NDVI reconstruction. This revealed the commonalities in the regional hydroclimatic scenario recorded in these data sets over the last few centuries. This first tree-ring-based NDVI reconstruction from the western Himalayas infers regional climate-vegetation dynamics during the past two centuries. This reconstruction provides valuable input for modeling vegetation dynamics for predicting vegetation changes due to climate variations in the western Himalaya. [ABSTRACT FROM AUTHOR]

Precision Agriculture (PA) has revolutionized crop management by leveraging information technology, satellite positioning data, and remote sensing. One crucial component in PA applications is the Normalized Difference Vegetation Index (NDVI), which offers valuable insights into crop vigor and health. However, discontinuity of optical satellite acquisitions related to cloud cover and the huge load of the required processing time pose challenges to real-time applications. NDVI prediction emerges as an innovative solution to address these limitations. It allows for proactive decision-making by providing accurate estimates, enabling farmers and land managers to plan essential agronomic activities such as irrigation, fertilization, and pest control, based on anticipated future conditions. This study introduces an Artificial Neural Network (ANN) model incorporating NDVI, Normalized Difference Water Index (NDWI), temperatures, and precipitation as predictive variables. The model employs a novel time series slicing algorithm, Boosting Adaptive Time Series Slicer (BATS), to enhance the input training dataset's variability, presenting the model with a broader range of examples. A 2-Bidirectional Long Short-Term Memory (LSTM) forecasting model was developed to predict future NDVI values over short and medium-term horizons. The study area used to train, test and validate the ANN corresponds to a diverse landscape of cultivated corn fields located in Piemonte (NW-Italy). Results showed that NDVI future estimates were accurate; considering three time horizons for predictions (5, 10, and 15 days) RMSE values resulted to be 0.028, 0.038 and 0.050, respectively. Additionally, ablation tests proved that the most important variable for enhancing the model's accuracy is the NDWI, and the most useful timesteps are the four most recent ones. To preliminary investigate the capability of the ANN to operate over a wider and different area it was applied over the entire Europe, using the LUCAS dataset as reference map to locate corn fields. Results show RMSE of 0.062, 0.083 and 0.105 for the 5, 10 and 15 days forecasting horizons, respectively. The methodology proposed in this paper can be a possible alternative to more ordinary approaches for NDVI forecasting that nowadays appears to be a fundamental step for a proactive precision agriculture where crop management can be significantly improved. Future developments should explore the use of sequence-to-sequence ANNs to predict the development of multiple spectral indices over multiple crop types simultaneously. [ABSTRACT FROM AUTHOR]

In the context of global climate change and the increase in drought frequency, monitoring and accurately assessing the impact of hydrological process limitations on vegetation growth is of paramount importance. Our study undertakes a comprehensive evaluation of the efficacy of satellite remote sensing vegetation indices—Normalized Difference Vegetation Index (MODIS NDVI product), kernel NDVI (kNDVI), and Solar-Induced chlorophyll Fluorescence (GOSIF product) in this regard. Initially, we applied the LightGBM-Shapley additive explanation framework to assess the influencing factors on the three vegetation indices. We found that Vapor Pressure Deficit (VPD) is the primary factor affecting vegetation in southern China (18°–30°N). Subsequently, using Gross Primary Productivity (GPP) estimates from flux tower sites as a performance benchmark, we evaluated the ability of these vegetation indices to accurately reflect vegetation GPP changes during drought conditions. Our findings indicate that SIF serves as the most effective surrogate for GPP, capturing the variability of GPP during drought periods with minimal time lag. Additionally, our study reveals that the performance of kNDVI significantly varies depending on the estimation of different kernel parameters. The application of a time-heuristic estimation method could potentially enhance kNDVI's capacity to capture GPP dynamics more effectively during drought periods. Overall, this study demonstrates that satellite-based SIF data are more adept at monitoring vegetation responses to water stress and accurately tracking GPP anomalies caused by droughts. These findings not only provide critical insights into the selection and optimization of remote sensing vegetation product but also offer a valuable framework for future research aimed at improving our monitoring and understanding of vegetation growth status under climatic changes. [ABSTRACT FROM AUTHOR]

The non-photosynthetic vegetation – soil separation index (NSSI) and normalized difference vegetation index (NDVI) can be used to simultaneously estimate the fractional cover of non-photosynthetic vegetation (fNPV), photosynthetic vegetation (fPV), and bare soil (fBS) in vegetation ecosystems. However, these estimates suffer from problems due to shading by dense vegetation or topography. Based on field-measured data and on hyperspectral data acquired by unmanned aerial vehicles, we analyse how shading affects the morphology of NDVI-NSSI and enhanced vegetation index – NSSI (EVI-NSSI). We also investigate the simultaneous estimation of fNPV, fPV, and fBS. The results show that the NDVI-NSSI normalized difference feature space mitigates the impact of shade. Shade causes the NDVI-NSSI and EVI-NSSI to shift rightward parallel to the BS axis and leftward parallel to the PV axis, respectively. Although such shifts may hinder the determination of NPV, PV, and BS endmembers, they do not affect the spectral separation of NPV. Overall, with the NDVI-NSSI and EVI-NSSI methods, the estimation error for fNPV under shaded conditions is approximately 5% greater than that under illuminated conditions. Shaded green vegetation more strongly affects the estimation error of fPV and fBS in EVI-NSSI than in NDVI-NSSI, whereas fBS and fPV depend more strongly on shade than fNPV. [ABSTRACT FROM AUTHOR]

Winter wheat is one of the most important crops in the world. It is great significance to obtain the planting area of winter wheat timely and accurately for formulating agricultural policies. Due to the limited resolution of single SAR data and the susceptibility of single optical data to weather conditions, it is difficult to accurately obtain the planting area of winter wheat using only SAR or optical data. To solve the problem of low accuracy of winter wheat extraction only using optical or SAR images, a decision tree classification method combining time series SAR backscattering feature and NDVI (Normalized Difference Vegetation Index) was constructed in this paper. By synergy using of SAR and optical data can compensate for their respective shortcomings. First, winter wheat was distinguished from other vegetation by NDVI at the maturity stage, and then it was extracted by SAR backscattering feature. This approach facilitates the semi-automated extraction of winter wheat. Taking Yucheng City of Shandong Province as study area, 9 Sentinel-1 images and one Sentinel-2 image were taken as the data sources, and the spatial distribution of winter wheat in 2022 was obtained. The results indicate that the overall accuracy (OA) and kappa coefficient (Kappa) of the proposed method are 96.10% and 0.94, respectively. Compared with the supervised classification of multi-temporal composite pseudocolor image and single Sentinel-2 image using Support Vector Machine (SVM) classifier, the OA are improved by 10.69% and 5.66%, respectively. Compared with using only SAR feature for decision tree classification, the producer accuracy (PA) and user accuracy (UA) for extracting the winter wheat are improved by 3.08% and 8.25%, respectively. The method proposed in this paper is rapid and accurate, and provide a new technical method for extracting winter wheat. [ABSTRACT FROM AUTHOR]

Water resources are key factors that restrict ecosystem productivity and sustainable junctions in arid and semiarid regions. However, the interaction between vegetation development and soil moisture balance on the Loess Plateau has not been extensively studied. This study aims to assess the relationship between vegetation development and soil moisture balance across different years and precipitation areas on the Loess Plateau using a quantitative trade-off method. The results showed that precipitation had the greatest influence on root-zone soil moisture, while the surface soil moisture was affected by meteorological and topographic factors. In most precipitation areas, the benefit of soil moisture was higher than that of the normalized difference vegetation index (NDVI) and mean annual evaporation (MAE), but in forest areas, the benefits of the NDVI and MAE were higher than that of soil moisture. The relative benefits of land conversion vary over time. During the initial stage of converting farmland to forest, the soil moisture benefit is lower compared to the NDVI and MAE benefits. However, in the later stages, the soil moisture benefit gradually becomes more prominent. The deviation in relative benefits over time is relatively small, indicating a stable overall ecological benefit on the Loess Plateau. Understanding the relationship between the NDVI and soil moisture in different times and spaces will help improve our ability to sustainably manage vegetation construction and water resources. [ABSTRACT FROM AUTHOR]

Water resources must be preserved because they are vital for agricultural, industrial, and domestic use, especially in tropical forest regions. Given the importance of the Ulu Muda Forest Reserve to the Kedah state in sustaining paddy cultivation for the country, this study aimed to provide reliable forest classification method and produce Normalized Difference Vegetation Index-Maximum Likelihood Classification of forest density map for Ulu Muda Forest Reserve. Here, the study reports measurements of the satellite remote sensing index for the forest based the image date. The forest recorded NDVI index of 0.72 to 0.98 for high density, NDVI index of 0.61 to 0.71 for moderate and NDVI index of 0.26 to 0.60 for low density area. The index showed higher value, which is comparable with reports of other tropical forest elsewhere. The results were influenced using satellite remote sensing pre-processing, processing, and classification. As a result, based on the satellite image indices approach, upper threshold of the higher density, the forest can exhibit higher capability for sustaining its ecological and hydrological functions. Given that most of the tropical forest biome has sustained this threshold, forest management should be sustained at this state, and more measures should be taken for example increase of protection forest classification in Rancangan Pengurusan Hutan (RPH) that is one of yearly planning report in states of Peninsular Malaysia. In the meantime, Geographical Information System (GIS) capacity should be mandate looking forwards for more utilisation in mapping for planning and projection of forest resource management activities. [ABSTRACT FROM AUTHOR]

Bangladesh is extremely vulnerable to climate change, and vegetation indices serve as sensitive indicators. Due to the impacts of climate change, the cropping intensity of Southern region of Bangladesh is very low. So, this study aimed to analyze the changes in vegetation cover over time using the Normalized Difference Vegetation Index (NDVI) and identify the use of Climate Smart Agriculture (CSA) technologies and the benefits of using such technologies. A questionnaire survey was carried out by purposive random sampling method to detect 120 farmers' socioeconomic status, hazards faced by climate change, adopted climate smart agricultural practices and its benefits for assessing Adaptive Strategy Index (ASI) in Amtali upazila of Barguna district and Kalapara upazila of Patuakhali district. NDVI analysis of multi-spectral remote sensing data from 2012 and 2022 indicated the extent of sparse vegetation of Kalapara has increased. Western part of Amtali upazila, fallow areas have become lessened in 2022 (354.55 km2) compared to 2012 (368.78 km2) due to adopting different CSA practices. Saline-tolerant crop varieties, sunflowers, and watermelon cultivation were the highest ranked among the CSA practices, with 301, 300, and 296 ASI, respectively. Calculated weighted average of CSA practices indicated the reduction of production cost, increased family income (49.19%) and cropping intensity (51.67%), which impacts developed social bonding. [ABSTRACT FROM AUTHOR]

Remote sensing plays a vital role in analyzing urban changes. In this regard, various datasets collected from satellites today serve as a foundation for decision-makers and urban planners. This study compares the Normalized Difference Vegetation Index (NDVI) and the Normalized Difference Built-up Index (NDBI) as indicators for the creation of surface heat islands. Using Landsat 8 OLI/TIRS C2 L2 images, spatial correlations between land surface temperature (LST) were examined for August 2013, 2019 and 2023. Urban heat islands (UHI) are a contemporary phenomenon and increasingly common in large urban areas compared to surrounding, less populated areas. With the advancement in remote sensing, it is possible to adequately determine the spatial differentiation and prevalence of urban heat islands (UHI). The study is based on Landsat 8 satellite image sets for the Sarajevo basin in August 2013, 2019 and 2023, which were used to analyze LST, NDVI, and NDBI indices. This work indicates a relationship between LST and NDVI but varies depending on the analyzed year. Normalized Difference Built-up Index (NDBI) serves as a suitable indicator for surface UHI effects and can be used as an indicator to assess its spatial distribution within a larger urban environment. [ABSTRACT FROM AUTHOR]

It is important to quantitatively analyze the driving mechanisms of vegetation cover changes and explore the relationship between vegetation and climate factors for regional ecological environment evaluation and soil and water conservation. Based on the normalized difference vegetation index(NDVI)in Ningxia from 1998 to 2019, the trend analysis, partial correlation analysis and variance decomposition of RDA were used to investigate the spatial-temporal variation characteristics of NDVI of different vegetation types and their driving factors. The results show that ① the annual growth rate of NDVI is 0.007 1 a-1, with the lowest NDVI growth rate in spring(0.002 3 a-1)and the highest NDVI growth rate in autumn(0.007 5 a-1); the NDVI growth rates of coniferous forests, shrub and desert are the highest in summer, and the NDVI growth rates of broad-leaved forests, farmland, grasslands and meadows are the highest in autumn; ② on the annual scale, 93.4% of the regional NDVI show an improving trend; within different seasons, the vegetation degradation is serious in spring, 16.6% of the vegetation area show a decreasing trend, which mainly occur in the yellow irrigation area of northern Ningxia, and the degraded areas in other seasons are concentrated around Yinchuan city; ③ climate is the main factor affecting the vegetation change, NDVI is positively correlated with temperature throughout the year and in spring, while NDVI and temperature are negatively correlated in summer, and NDVI and precipitation are positively correlated in different seasons. [ABSTRACT FROM AUTHOR]

The Normalized Difference Vegetation Index (NDVI) is widely used for monitoring vegetation status, as accurate and reliable NDVI time series are crucial for understanding the relationship between environmental conditions, vegetation health, and productivity. Ground digital cameras have been recognized as important potential data sources for validating remote-sensing NDVI products. However, differences in the spectral characteristics and imaging methods between sensors onboard satellites and ground digital cameras hinder direct consistency analyses, thereby limiting the quantitative application of camera-based observations. To address this limitation and meet the needs of vegetation monitoring research and remote-sensing NDVI validation, this study implements a novel NDVI camera. The proposed camera incorporates narrowband dual-pass filters designed to precisely separate red and near-infrared (NIR) spectral bands, which are aligned with the configuration of sensors onboard satellites. Through software-controlled imaging parameters, the camera captures the real radiance of vegetation reflection, ensuring the acquisition of accurate NDVI values while preserving the evolving trends of the vegetation status. The performance of this NDVI camera was evaluated using a hyperspectral spectrometer in the Hulunbuir Grassland over a period of 93 days. The results demonstrate distinct seasonal characteristics in the camera-derived NDVI time series using the Green Chromatic Coordinate (GCC) index. Moreover, in comparison to the GCC index, the camera's NDVI values exhibit greater consistency with those obtained from the hyperspectral spectrometer, with a mean deviation of 0.04, and a relative root mean square error of 9.68%. This indicates that the narrowband NDVI, compared to traditional color indices like the GCC index, has a stronger ability to accurately capture vegetation changes. Cross-validation using the NDVI results from the camera and the PlanetScope satellite further confirms the potential of the camera-derived NDVI data for consistency analyses with remote sensing-based NDVI products, thus highlighting the potential of camera observations for quantitative applications The research findings emphasize that the novel NDVI camera, based on a narrowband spectral design, not only enables the acquisition of real vegetation index (VI) values but also facilitates the direct validation of vegetation remote-sensing NDVI products. [ABSTRACT FROM AUTHOR]

Extreme weather events and frequent meteorological disasters pose significant threats to vegetation growth in the context of climate change. However, information regarding the impact of extreme climate events on vegetation in the ecologically fragile Karst area has been lacking. This study aims to fill this knowledge gap by quantifying changes in extreme temperature and the normalized difference vegetation index (NDVI) while exploring the relationship between extreme temperature and NDVI in Guangxi's Karst area, China. Our analysis reveals a pronounced trend toward extreme warming in the study region from 2000 to 2019. Warm days TX90d, TX95d, and TX99d (count of days where daily maximum temperature TX > 90th, 95th, and 99th percentile) significantly (P < 0.05) increased 7.1, 5.5, and 2.0 days per decade. Warm nights TN90d, TN95d, and TN98d (count of nights where daily minimum temperature TN > 90th, 95th, and 98th percentile) significantly (P < 0.05) increased 9.7, 6.8, and 4.2 days per decade, with the increase rate greater than warm days. Annual days for daily maximum temperature higher than 35 °C also increased 3.4 days per decade. The annual NDVI increased 0.04 per decade. We have identified a negative linear relationship between the trends of very cold and very warm days (i.e., very cold days TX01d and hot days TX99d) and NDVI. In contrast, the annual NDVI demonstrates a positive correlation with the annual days for daily maximum temperature higher than 35 °C. The results suggested that climate warming might to some extent benefit the vegetation growth. Nevertheless, it is imperative to enhance vegetation protection efforts, particularly in regions experiencing extreme warm and cold. [ABSTRACT FROM AUTHOR]

Tree diversity may buffer the negative impact of drought events according to the diversity insurance hypothesis. During the extreme pan-European drought of 2018, we tested whether tree species richness modulated drought impacts on communities of a young tree diversity experiment in Freiburg, Germany. We utilized drone-based hyperspectral images to capture early symptoms of drought stress indicated by variation in Normalized Difference Vegetation Index (NDVI), and ground-based measures that are indicative of tree canopy function, including Plant Area Index (PAI) and leaf chlorophyll content (Chl). Measures were taken the fifth growing season after planting on six broadleaved and six conifer species originating from Europe and North America in monocultures, two-, four- and six-species mixtures. NDVI decreased successively in response to the summer drought. In contrast to our expectation, tree species richness did not reduce declines in NDVI at the plot level. NDVI values were generally lower in six-species mixtures, compared to monocultures or two-species mixtures, which may be indicative of antagonistic tree species interactions in mixtures of high diversity. Changes in PAI in response to the drought were similar to changes in NDVI; however, modeled differences in PAI between mixtures were not statistically significant. Chl initially decreased, but recovered within a period of two months following the drought. Remotely sensed NDVI facilitated monitoring of the drought response of our large field experiment and could aid in monitoring canopy health in response to extreme drought events. Our observations suggest that NDVI responses are likely more strongly related to leaf shedding within tree canopies than decreases in Chl. Tree stands with a high abundance of deciduous trees may therefore be especially sensitive in triggering NDVI changes in response to drought during the stages leading up to tree mortality. Future studies of drought-related canopy responses should include monitoring of leaf shedding as well as the Chl of shed leaves. [ABSTRACT FROM AUTHOR]

The purpose of the article was to describe the outcomes of an in-field evaluation of the NDVI Converter application, which was created to assess the normalized difference vegetation index (NDVI) using estimates of the percentage of green canopy cover. The study was carried out in 2023 on fields of winter wheat, winter barley, and winter oilseed rape in the phenological stages BBCH 21-32 and BBCH 18-39, respectively. The fraction of green canopy cover was estimated using the application Canopeo. By comparing the mean absolute percentage error and Pearson's correlation coefficient to the actual values of the spatial vegetation index, as determined by the platform OneSoil, the quality of the vegetation index evaluation was assessed. Thus, it has been established that NDVI Converter offers accurate vegetation index assessment for cereal crops, with mean absolute percentage error 16.23% and Pearson's correlation coefficient within 0.99, and reasonable quality for oilseed rape (statistical indicators of 46.61% and 0.99, respectively). After the adjustment, the accuracy score of the NDVI Converter increased up to 27.62% for oilseed rape, and 8.71% for winter cereals. Therefore, NDVI Converter could be recommended for practical use in case the spatial vegetation index is not provided by satellite imagery services. [ABSTRACT FROM AUTHOR]

Vegetation indices serve as an essential tool in monitoring variations in vegetation. The vegetation indices used often, viz., normalized difference vegetation index (NDVI) and enhanced vegetation index (EVI) were computed from MODIS vegetation index products. The present study aimed to monitor vegetation's seasonal dynamics by using time series NDVI and EVI indices in Tamil Nadu from 2011 to 2021. Two products characterize the global range of vegetation states and processes more effectively. The data sources were processed and the values of NDVI and EVI were extracted using ArcGIS software. There was a significant difference in vegetation intensity and status of vegetation over time, with NDVI having a larger value than EVI, indicating that biomass intensity varies over time in Tamil Nadu. Among the land cover classes, the deciduous forest showed the highest mean values for NDVI (0.83) and EVI (0.38), followed by cropland mean values of NDVI (0.71) and EVI (0.31) and the lowest NDVI (0.68) and EVI (0.29) was recorded in the scrubland. The study demonstrated that vegetation indices extracted from MODIS offered valuable information on vegetation status and condition at a short temporal time period. [ABSTRACT FROM AUTHOR]

In recent years, research on the relationship between the Normalized Difference Vegetation Index (NDVI) and climate factors has gained significant interest. This study examines vegetation changes in different forest ecosystems in China using the NDVI dataset from 1998 to 2015 and corresponding climate factor data (temperature and precipitation). Trend line analysis and correlation analysis methods are employed to investigate the spatial and temporal change characteristics of NDVI in five representative Chinese forest ecosystems. The driving forces of the two climate factors are simultaneously analyzed. The results indicate that: (1) Over the 18-year study period, the annual maximum NDVI of China’s forest ecosystems exhibited a significant increasing trend (P<0.01), with an average annual increase of 0.0043; (2) Vegetation cover changes demonstrated an overall upward trend, with the NDVI of 84.87% of the forest vegetation in the study area increasing. However, a minimal decrease was observed in 0.94% of the study area; (3) Vegetation coverage in China’s forest ecosystems has increased over the past 18 years, exhibiting a greening trend. Across the entire study area, the correlations between NDVI and temperature and precipitation were insignificant. [ABSTRACT FROM AUTHOR]

Terrestrial ecosystems depend heavily on their vegetation; it is possible to forecast future growth trends of regional vegetation by keeping an eye on changes in vegetation dynamics. To circumvent the potential reduction in prediction accuracy caused by the non-stationarity of meteorological changes, we analyzed the characteristics of NDVI (Normalized Difference Vegetation Index) spatial and temporal changes and the influencing factors over the past 20 years in the Maowusu Sandland of China via attribution analysis. We also constructed a comprehensive analysis system for vegetation pre-restoration. Moreover, we combined meteorological data from 2000 to 2018 and presented a deep-learning NDVI-Informer prediction model with a self-attentive mechanism. We also used distillation operation and fusion convolutional neural network for NDVI prediction. Incorporating a probsparse self-attention method successfully overcomes Transformer weaknesses by lowering the memory use and complexity of large time series. It significantly accelerates the inference speed of long time series prediction and works well with non-smooth data. The primary findings were: (1) the Maowusu Sandland's 20-year average showed a consistent increasing trend in the NDVI at 0.0034 a−1, which was mostly caused by climate change, with a relative contribution rate of 55.47%; (2) The Informer-based model accurately forecasted the NDVI in the research region based on meteorological elements and conducted a thorough analysis of the MAPE (mean absolute percentage error) (2.24%). This suggests that it can effectively lower the data's volatility and increase prediction accuracy. The anticipated outcomes indicate that the trend will stabilize during the following ten years. To attain more sustainable and efficient agricultural production, the results of this study may be used to accurately estimate future crop yields and NDVI using previous data. [ABSTRACT FROM AUTHOR]

Within the Tibetan Plateau (TP), grassland degradation is of great concern, and there is a need to better understand the spatial variability and landscape patterns in grassland degradation across the TP. This study explores potential patterns in grassland degradation, estimated using Landsat-derived normalized difference vegetation index (NDVI) growing season change products generated by a prior study. Shuttle Radar Topography Mission (SRTM) digital terrain model (DTM)-derived geomorphons, topographic slope and the topographic position index (TPI) were used to explore grassland degradation and assess whether changes in growing season NDVI are associated with specific grasslands or landforms. An increase in NDVI within the TP was observed especially during the 1990 to 2018 and 2000 to 2018 time periods. Higher growing season median NDVI change values were found for the Southeast Tibet Shrublands and Meadows (SETSM) and TP Alpine Shrublands and Meadows (TPASM) grasslands in comparison to the other grassland types analysed, suggesting a more pronounced greening trend in the eastern portion of the plateau in comparison to the western portion. Small differences in NDVI change were observed for different geomorphon-based landforms, while topographic slope and TPI showed only modest correlations with NDVI change for all time periods and grassland types. More localized patterns of grassland degradation were masked by the widespread greening trends. Further analysis of only pixels that experienced a decreasing NDVI magnitude of less than or equal to −0.1 also generally suggested more change in the SETSM and TPASM. While this study generally supports the use of geomorphons as an analysis and aggregating unit for studying change patterns at the hillslope scale, more research is required to fully grasp the dynamics of landscape change at the hillslope scale in the TP. [ABSTRACT FROM AUTHOR]

Iraq's climate is prone to frequent droughts, which have severely influenced the country in the past two decades. A study conducted using remote sensing and geographical information systems analyzed the spatiotemporal patterns of drought in Iraqi Kurdistan - specifically Erbil for 19 years from 2003 to 2021. The research found that the region had experienced severe drought episodes during this time, with an increase in severity and frequency, especially in 2008, 2012, and 2021. These years were noticeable by a decrease in vegetation area cover and lower average precipitation. The study utilized the Normalized Difference Vegetation Index (NDVI), Rainfall, and Rainfall Anomaly to produce multi-temporal classified drought maps, which showed that climate conditions played a significant role in the change of the vegetated cover area. The research also estimated the effect of rainfall variability on vegetation cover in Erbil, concluding that rainfall anomalies led to changes in the NDVI. The correlation matrix between rainfall anomalies and NDVI anomalies confirms that irregular rainfall patterns result in modifications to the NDVI. Productivity was found to increase with wet anomalies and decrease with dry anomalies. Overall, the study provides valuable insights into the impact of drought on agricultural productivity in the Iraqi Kurdistan Region (IKR)-Erbil and emphasizes the importance of implementing effective strategies to mitigate the impact of drought on agriculture in the region. [ABSTRACT FROM AUTHOR]

We studied the correspondence between historical series of tree-ring width (TRW) and the normalized difference vegetation index (NDVI, i.e., greenness index) values acquired monthly over an entire year by unmanned aerial vehicles. Dendrochronological techniques revealed differentiated responses between species and seasonality. Pinus engelmannii Carrière and Juniperus deppeana Steudel were affected by warm temperatures (TMAX) during the winter prior to growth and benefited from precipitation (PP) during the seasons prior to the spring period. The standardized precipitation–evapotranspiration index (SPEI) confirmed the high sensitivity of P. engelmannii to drought (r = 0.7 SPEI). Quercus grisea Liebm. presented a positive association with PP at the beginning and end of its growth season. Monthly NDVI data at the individual tree level in the three species (NDVI ~0.37–0.48) statistically confirmed the temporal differences. Q. grisea showed a drastic decrease during the dry season (NDVI = 0.1) that had no impact on drought sensitivity in the same period, according to the climate-TRW relationship. We conclude that a relationship is plausible between the crown greenness index and radial growth, although more extended temporal windows of the NDVI should be explored. Differences in susceptibility to drought found among the species would presumably have implications for the composition of these forests under drought scenarios. [ABSTRACT FROM AUTHOR]

Efficient landscape irrigation is an important water conservation strategy and reduces strain on limited water resources in inland Southern California. A three-year field irrigation trial was conducted in Riverside, California, to evaluate the response of 'Westcoaster' tall fescue (Festuca arundinacea Schreb.) to a wide range of autonomous irrigation scenarios implemented using a Weathermatic smart controller. The response of the turfgrass was assessed using visual rating (VR) and normalized difference vegetation index (NDVI) data collected with a handheld sensor. These experimental data were then used to develop a regression-based turfgrass water response function (TWRF), which was further employed to estimate the response of tall fescue to different irrigation levels during years with varying atmospheric evaporation demands, including extreme conditions. A strong correlation was observed between NDVI and VR (r = 0.88). A minimum NDVI threshold of 0.7 was identified to maintain an acceptable level of tall fescue quality for residential areas. During the trial periods, irrigation application rates below 100% of reference evapotranspiration (ETo) were insufficient to sustain the desired quality of tall fescue. The TWRF estimations suggested that 60% ETo can be applied for short periods (7 to 43 days, depending on the atmospheric evaporative demands) during the summer before the visual quality drops below the minimum threshold. Comparing different irrigation strategies, it was observed that on-demand irrigation, which adjusts irrigation frequency based on ETo demand, resulted in higher NDVI and VR values compared to a limited watering schedule of only a few days per week. Analysis of soil moisture data revealed that the primary root water uptake occurred mainly in the topsoil layer (<33 cm), and the applied irrigation water primarily replenished the shallow soil layer (<20 cm) due to the coarse sandy loam soil characteristics. The Weathermatic controller, equipped with on-site air temperature measurements, provided reliable ETo estimations and can be used for autonomous landscape irrigation scheduling in semiarid regions. [ABSTRACT FROM AUTHOR]

The aim of this study is to determine the effect of vegetation density on the soil quality index (SQI) in the Godrahav basin, which varies in terms of land use and topographic features. For this purpose, a total of 180 soil samples were taken at depths of 0-20 cm (surface soil) from six ranges of the normalized difference vegetation index (NDVI) in a typical watershed in Artvin (northeast Türkiye). The SQI was used to evaluate and calculate soil quality in the study area. Topographic features (elevation, slope, and aspect), land use, physical properties of the soil (clay content, silt content, sand content, mean weight diameter, aggregation rate, aggregate stability, porosity, bulk density, dispersion rate, field capacity, and wilting point), and chemical properties of the soil (organic matter content, pH, and EC) were used as indicators. The results showed that aggregate stability, aggregation rate, porosity, field capacity, and wilting point water content were highest in the NDVI range of 0.45-0.56 with values of 89.83%, 76.76%, 60.56%, 47.89%, and 33.98%, respectively. Meanwhile, bulk density was lowest in the same NDVI range at 0.81 g cm-3. The differences among NDVI ranges in terms of soil properties were statistically significant. SQI values varied significantly (p < 0.01) depending on NDVI classes with an increasing trend from the lowest NDVI range to the highest. These results provide a better explanation of the effect of vegetation density on soil quality. [ABSTRACT FROM AUTHOR]

The normalized difference vegetation index (NDVI) is one of the most common metrics used to describe vegetation dynamics. Unfortunately, low-quality pixels resulting from contamination (by features including clouds, snow, aerosols, and mixed factors) have impeded NDVI products' widespread application. Researchers have thought of several ways to improve NDVI quality when contamination occurs. However, most of these algorithms are based on the noise-negative deviation principle, which aligns low-value NDVI products to an upper line but ignores cases where absolute surface values are low. Consequently, to fill in these research gaps, in this article, we use the random forest model to produce a set of high-quality NDVI products to represent actual surface characteristics more accurately and naturally. Climate and geographical products are used as model inputs to describe environmental factors. They represent the random forest (RF) model that establishes relationships between MODIS NDVI products and meteorological products in high-quality areas. In addition, auxiliary data and empirical knowledge are employed to meet filling requirements. Notably, the random forest (RF) algorithm exhibits a mean absolute error (MAE) of 0.024 and a root mean squared error (RMSE) of 0.034, in addition to a coefficient of determination (R2) value of 0.974. Furthermore, the MAE and RMSE of the RF-based method decreased by 0.014 and 0.019, respectively, when compared to those of the STSG (spatial–temporal Savitzky–Golay) plan and by 0.013 and 0.015, respectively, when compared to the LSTM (long short-term memory) method. R2 increased by 0.039 and 0.027, respectively, compared to the STSG and LSTM methods. We introduced a novel series of NDVI products that demonstrated consistent spatial and temporal connectivity. The novel product exhibits enhanced adaptability to intricate environmental conditions and promises the potential for utilization in investigating vegetation dynamics within the Chinese region. [ABSTRACT FROM AUTHOR]

Introduction-- The strawberry crop is seriously affected by different diseases that result in a decrease in production and fruit quality. Among the most important diseases that attack the strawberry crop is Mycosphaerella fragariae, the causal agent of leaf spot, which in advanced stages can lead to the total loss of the crop. Monitoring of this disease is a fundamental tool for its prevention and control. The tools of precision agriculture and agriculture 4.0, such as images obtained by drones, facilitate decision making by producers and optimize crop work such as monitoring. Objetive-- This study identified the relationship between the percentage of area affected by leaf spot (Mycosphaerella fragaraiae), the Normalized Difference Vegetation Index (NDVI) and Normalized Difference Red Edge Index (NDRE). Metodology-- The study was developed in the province of Pamplona in Norte de Santander with high inoculum pressure of Mycosphaerella fragariae. Leaflets were collected from strawberry plants with different degrees of affectation caused by Mycosphaerella fragariae, this plant material was transported guaranteeing its conservation until the laboratory, where measurements were made with the Stellar Net brand EPP2000 portable spectroradiometer, from which the values of the red band, infrared and the red border of each one was taken to perform the calculation of NDVI and NDRE, these values were averaged for each degree of affectation. The exact affected area was calculated for each leaf using the Compu eye leaf and symptom area software and the correlation coefficients were calculated looking for a mutual linear relationship from simple linear regressions. Results-- The NDRE index correlates 82% with the area affected by Mycosphaerella fragariae by up to 70%. Conclusión-- The percentage of area affected by leaf freckle (Mycosphaerella fragariae) is directly related to the spectral response in terms of NDVI and NDRE indices, The spectral behavior of the leaf freckle (Mycosphaerella fragariae) in the strawberry crop has a linear behavior with respect to the NDVI and NDRE indices, where in the initial stages of the disease the values of the indices are close to zero, and as it progresses disease indices show a decrease in their values. There is a greater degree of correlation between the percentage of the affected area and the NDRE index up to 70% of the affected area, because this is more sensitive to the change in the chlorophyll content of the leaf. [ABSTRACT FROM AUTHOR]

Vegetation is an important type of land cover. Long-term, large-scale, and high-precision vegetation monitoring is of great significance for ecological environment investigation and regional sustainable development in protected areas. This paper develops a long-term remote sensing monitoring method for vegetation by calculating the normalized difference vegetation index (NDVI) based on the Google Earth Engine (GEE) cloud platform and Landsat satellite remote sensing images. First, based on Landsat long-term satellite images and GEE, the spatiotemporal distribution map of the NDVI is accurately drawn. Subsequently, the NDVI is accurately classified, and the time trend analysis of the NDVI is conducted based on the NDVI mean trend graphs, transition matrices, etc. Then, combined with Moran's I, high/low clusters, and other methods, the spatial pattern characteristics of the NDVI are analyzed. Finally, climate factors, terrain factors, and anthropologic factors are considered comprehensively. An analysis of the factors affecting the evolution of the NDVI is performed. Taking Zhoushan Island, China, as an example, an experiment is conducted, and the results reveal that (1) the average NDVI exhibits a decreasing trend from 1985 to 2022, decreasing from 0.53 in 1985 to 0.46 in 2022. (2) Regarding vegetation index transitions, the high NDVI areas (0.6–1) exhibit the most substantial shift toward moderately high NDVI values (0.4–0.6), covering an area of 83.10 km2. (3) There is an obvious spatial agglomeration phenomenon in the NDVI on Zhoushan Island. The high-high NDVI clusters and the significant hot spots are predominantly concentrated in the island's interior regions, while the low-low NDVI clusters and the significant cold spots are mainly situated along the coastal areas. (4) The DEM, slope, and temperature have a greater influence among the single factors on the spatial pattern distribution of the NDVI in 2015. There are significant differences in the spatial pattern distribution of the NDVI between the temperature and DEM, temperature and slope, DEM and precipitation, slope and precipitation, aspect and population, and aspect and gross domestic product (GDP). The DEM and slope, DEM and temperature, and DEM and population are three sets of factors with a strong influence on spatial pattern interaction. This study provides data support for the scientific management of vegetation resources on Zhoushan Island and is of great significance to the sustainable development of the island region. [ABSTRACT FROM AUTHOR]

The successful launch of Landsat-9 marks a significant achievement in preserving the data legacy and ensuring the continuity of Landsat's calibrated Earth observations. This study comprehensively assesses the continuity of reflectance and the Normalized Difference Vegetation Index (NDVI) between Landsat-8 and Landsat-9 Operational Land Imagers (OLIs) over diverse Chinese landscapes. It reveals that sensor discrepancies minimally impact reflectance and NDVI consistency. Although Landsat-9's top-of-atmosphere (TOA) reflectance is slightly lower than that of Landsat-8, small root-mean-square errors (RMSEs) ranging from 0.0102 to 0.0248 for VNIR and SWIR bands (and larger RMSE for NDVI at 0.0422) fall within acceptable ranges for Earth observation applications. Applying atmospheric corrections markedly enhances reflectance uniformity and brings regression slopes closer to unity. Further, Bidirectional Reflectance Distribution Function (BRDF) adjustments improve comparability, ensuring measurement reliability, and the NDVI maintains robust consistency across various reflectance types, time series, and land cover classes. These findings affirm Landsat-9's success in achieving data continuity within the Landsat program, allowing interchangeable use of Landsat-8 and Landsat-9 OLI data for diverse Earth observation purposes. Future research may explore specific sensor correlations across different vegetation types and seasons while integrating data from complementary platforms, such as Sentinel-2, to enhance the understanding of data continuity factors. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

Terminal heat stress is a major constraint for taking a profitable crop of wheat by small and marginal farmers in the Indo‐Gangetic Plains of south Asia. Hence, breeders remain in constant search for heat‐tolerant genotypes. This study was done with the purpose to find out high‐yielding wheat genotypes that perform stably under terminal heat stress as well as to conclude an easy phenotyping trait for this objective. A cross (HUW 234 × HUW 468) was made using two popular cultivars of the eastern Gangetic Plains of India. HUW 234 carries terminal heat tolerance whereas, HUW 468 is high yielding but suffers from terminal heat stress when planted late. So obtained 167 recombinant inbred lines (RILs) were exposed to both timely sown (TS) and late sown (LS) conditions at three locations for two consecutive years 2016–17 and 2017–18 at Varanasi (Uttar Pradesh), Jabalpur (Madhya Pradesh) and Pusa, Samastipur (Bihar). Combined ANOVA revealed that normalized difference vegetation index (NDVI) at anthesis stage (NDVI_AH) and grain filling duration (GFD) was significant for genotype (G), environment (E) and genotype × environment interaction (GEI). Likewise, differences were significant for grain yield (GY) and 1000‐grain weight (TGW). AMMI analysis revealed similar results. However, NDVI_AH showed low variation under heat stressed late sown conditions. Furthermore, the which‐won‐where model and mean versus stability identified the best performing, above‐average yielding and stable lines across the environments. Correlation among measured traits revealed that NDVI_AH had a significant association with GY in most of the environments. Several lines that performed superior to better parent for GY also showed higher values for NDVI_AH and GFD. The study demonstrates that NDVI_AH and GFD appear to be important traits to be considered while breeding for terminal heat stress tolerance in wheat. [ABSTRACT FROM AUTHOR]

Inter-annual climatically driven growth variability of above-ground biomass compartments (for example, tree stems and foliage) controls the intensity of carbon sequestration into forest ecosystems. However, understanding the differences between the climatic response of stem and foliage at the landscape level is limited. In this study, we examined the climate-growth response of stem and leaf biomass and their relationship for Pinus sylvestris (PISY) and Picea abies (PCAB) in topographically complex landscapes. We used tree-ring width chronologies and time series of the normalized difference vegetation index (NDVI) derived from high-resolution Landsat scenes as proxies for stem and leaf biomass, respectively. We then compared growth variability and climate-growth relationships of both biomass proxies between topographical categories. Our results show that the responses of tree rings to climate differ significantly from those found in NDVI, with the stronger climatic signal observed in tree rings. Topography had distinct but species-specific effects: At moisture-limited PISY stands, stem biomass (tree rings) was strongly topographically driven, and leaf biomass (NDVI) was relatively insensitive to topographic variability. In landscapes close to the climatic optimum of PCAB, the relationship between stem and leaf biomass was weak, and their correlations with climate were often inverse, with no significant effects of topography. Different climatic signals from NDVI and tree rings suggest that the response of canopy and stem growth to climate change might be decoupled. Furthermore, our results hint toward different prioritizations of biomass allocation in trees under stressful conditions which might change allometric relationships between individual tree compartments in the long term. [ABSTRACT FROM AUTHOR]

Extreme events such as hailstorms are a cause for concern in agriculture, leading to both economic and food supply losses. Traditional damage estimation techniques have recently been called into question since damages have rarely been quantified at the large-field scale. Damage-estimation methods used by field inspectors are complex and sometimes subjective and hardly account for damage spatial variability. In this work, a normalized difference vegetation index (NDVI)-based parametric method was applied using both unmanned aerial vehicles (UAV) and Sentinel-2 sensors to estimate the leaf area index (LAI) of maize (Zea mays L.) resulting from simulated hail damage. These methods were then compared to the LAI values generated from the Sentinel-2 Biophysical Processor. A two-year experiment (2020–2021) was conducted during the maize cropping season, with hail events simulated during a range of maize developmental stages (the 8th-leaf, flowering, milky and dough stages) using a 0–40% defoliation gradient of damage intensities performed with the aid of specifically designed prototype machines. The results showed that both sensors were able to accurately estimate LAI in a nonstandard damaged canopy while requiring only the calibration of the extinction coefficient k (ϑ) in the case of parametric estimations. In this case, the calibration was performed using 2020 data, providing k (ϑ) values of 0.59 for Sentinel-2 and 0.37 for the UAV sensor. The validation was performed on 2021 data, and showed that the UAV sensor had the best accuracy (R2 of 0.86, root-mean-square error (RMSE) of 0.71). The k (ϑ) value proved to be sensor-specific, accounting for the NDVI retrieval differences likely caused by the different spatial operational scales of the two sensors. NDVI proved effective in parametrically estimating maize LAI under damaged canopy conditions at different defoliation degrees. The parametric method matched the Sentinel-2 biophysical process-generated LAI well, leading to less underestimations and more accurate LAI retrieval. [ABSTRACT FROM AUTHOR]

To meet the challenge of food security, it is necessary to obtain information about rice fields accurately, quickly and conveniently. In this study, based on the analysis of existing rice fields extraction methods and the characteristics of intra-annual variation of normalized difference vegetation index (NDVI) in the different types of ground features, the NDVI difference method is used to extract rice fields using Sentinel data based on the unique feature of rice fields having large differences in vegetation between the pre-harvest and post-harvest periods. Firstly, partial correlation analysis is used to study the influencing factors of the rice harvesting period, and a simulation model of the rice harvesting period is constructed by multiple regression analysis with data from 32 sample points. Sentinel data of the pre-harvest and post-harvest periods of rice fields are determined based on the selected rice harvesting period. The NDVI values of the rice fields are calculated for both the pre-harvest and post-harvest periods, and 33 samples of the rice fields are selected from the high-resolution image. The threshold value for rice field extraction is determined through statistical analysis of the NDVI difference in the sample area. This threshold was then utilized to extract the initial extent of rice fields. Secondly, to address the phenomenon of the "water edge effect" in the initial data, the water extraction method based on the normalized difference water index (NDWI) is used to remove the pixels of water edges. Finally, the extraction results are verified and analyzed for accuracy. The study results show that: (1) The rice harvesting period is significantly correlated with altitude and latitude, with coefficients of 0.978 and 0.922, respectively, and the simulation model of the harvesting period can effectively determine the best period of remote sensing images needed to extract rice fields; (2) The NDVI difference method based on sentinel data for rice fields extraction is excellent; (3) The mixed pixels have a large impact on the accuracy of rice fields extraction, due to the water edge effect. Combining NDWI can effectively reduce the water edge effect and significantly improve the accuracy of rice field extraction. [ABSTRACT FROM AUTHOR]

Normalized difference vegetation index (NDVI) time series data, derived from optical images, play a crucial role for crop mapping and growth monitoring. Nevertheless, optical images frequently exhibit spatial and temporal discontinuities due to cloudy and rainy weather conditions. Existing algorithms for reconstructing NDVI time series using multi-source remote sensing data still face several challenges. In this study, we proposed a novel method, an enhanced gap-filling and Whittaker smoothing (EGF-WS), to reconstruct NDVI time series (EGF-NDVI) using Google Earth Engine. In EGF-WS, NDVI calculated from MODIS, Landsat-8, and Sentinel-2 satellites were combined to generate high-resolution and continuous NDVI time series data. The MODIS NDVI was employed as reference data to fill missing pixels in the Sentinel–Landsat NDVI (SL-NDVI) using the gap-filling method. Subsequently, the filled NDVI was smoothed using a Whittaker smoothing filter to reduce residual noise in the SL-NDVI time series. With reference to the all-round performance assessment (APA) metrics, the performance of EGF-WS was compared with the conventional gap-filling and Savitzky–Golay filter approach (GF-SG) in Fusui County of Guangxi Zhuang Autonomous Region. The experimental results have demonstrated that the EGF-WS can capture more accurate spatial details compared with GF-SG. Moreover, EGF-NDVI of Fusui County exhibited a low root mean square error (RMSE) and a high coefficient of determination (R2). In conclusion, EGF-WS holds significant promise in providing NDVI time series images with a spatial resolution of 10 m and a temporal resolution of 8 days, thereby benefiting crop mapping, land use change monitoring, and various ecosystems, among other applications. [ABSTRACT FROM AUTHOR]

Crop mapping and its health monitoring are of key importance for a country like Pakistan where a considerable share of the GDP originates from agricultural production. Currently, extensive capital and human resource are utilized to record the crop types and monitor crop health due to variations in crop pattern and intercropping practices being observed in many parts of the country. Satellite Remote Sensing, however, can be utilized very efficiently to identify crop types and estimate acreage by using unique phenological cycles derived from NDVI thresholds. In this research, we proposed a semi-automated, efficient and cost-effective state-of-the-art technique for crop classification using satellite remote sensing datasets and GIS techniques to replace outdated conventional survey-based crop estimations procedures. This research was conducted in Sahiwal district, Pakistan to categorise several crop types using two major inputs: satellite (Sentinel-2, 10m) imagery and local crop calendar. The research adopted a semi-automatic approach to find the main crops of Rabi (winter) and Kharif (summer) using orthorectified and atmospherically corrected Level-1C images to extract phenological information based on Normalized Difference Vegetation Index (NDVI). The complete phenological life cycle of crops including sowing, growing, and harvesting stages were deeply analyzed with the help of crop calendar and NDVI profiles. Then this information passed through iterative selforganizing (ISO Data Model) of unsupervised classification technique in the form of temporal stacked images. The NDVI profiles of each crop were synchronized with the crop calendar of the study area to develop the crop categorization procedure. The results were validated with the statistics of the agriculture department Lahore and field samples gave about 85% and 88% accuracy for Rabi and Kharif cropping seasons respectively. It is concluded from the research that the tested methodology is capable of providing a fast, cost-efficient, and reliable solution for crop mapping instead of conventional survey-based crop area assessment techniques (conventional Girdawri) which requires a vast number of resources and time, and it can be replicated in different areas with minor changes. [ABSTRACT FROM AUTHOR]

Since the last couple of decades urban areas of developing countries have been experiencing unprecedented growth. However, this growth is not well planned in most of the cities. Even in recent days, there is no significant change in city planning and still, there is a lack of concern regarding environmental issues especially the changing green covers in cities. Identifying the green space dynamics of major cities is crucial considering strategic planning of smart cities, policy intervention and combating the problems associated with degreening. The present study attempts to assess the potentiality of hyper-temporal normalized difference vegetation index (NDVI) and its temporal trend as an indicator of greening or de-greening process in Delhi National Capital Territory (NCT) and its peri-urban areas. Ten days composite hyper-temporal SPOT-VGT (Satellite Pour l'Observation de la Terre Vegetation) NDVI images (1998-2013) have been used for identifying the vegetation trend and further, it was compared with long-term CHIRPS (climate hazards centre infrared precipitation with station data) rainfall data to unveil the root causes behind the variation in vegetation trend specifically the contribution of natural and anthropogenic factor in changing scenario of urban green space. The study shows that hyper-temporal NDVI data with sufficient temporal depth can be highly convenient to explore the trend of vegetation and changes in green space. The spatio-temporal pattern of NDVI trend indicates a significant part of the area including Noida, Gurgaon etc. experienced decreasing vegetation trend which can be explained by large scale conversion of vegetated land into built-up areas due to establishment of new multi-storied buildings. The strong negative correlation (r ≤ - 0.8) between rainfall-NDVI indicates that rainfall is not the major controlling factor of vegetation trend in that area rather anthropogenic activities played a significant role in depletion of vegetation. During 1998-2013, the areas of bounding zone have been largely converted into built-up area and as a consequence, surface vegetation cover of these areas has been depleted. However, human induced greening was also observed in small pockets of Delhi NCT that can be explained by the impact of positive measures like tree plantation and proper maintenance of urban trees along roads, parks, and gardens. [ABSTRACT FROM AUTHOR]

Monitoring wheat yield and production is essential for ensuring global food security. Remote sensing can be used to achieve it due to its ability to provide global, comprehensive, synoptic, and repetitive information in near real-time. This study used the 2006–2016 Normalized Difference Vegetation Index (NVDI) and Enhanced Vegetation Index 2 (EVI2) time series at a 250 m spatial resolution and 2006–2011 MERIS Terrestrial Chlorophyll Index (MTCI) time series at a 300 m spatial resolution. The post-maximum period for pixels containing wheat was selected based on the EU's Common Agrarian Policy (CAP) and Corine Land Cover (CLC) data. It was correlated with yield and production values from governmental statistics (GS) of the largest Nomenclature of Territorial Units for Statistics level 2 (NUTS-2) wheat producers in Spain and for Spain overall. The selection of wheat masks was crucial for the accuracy of the models, with CAP masks offering greater forecasting capability. Models using CLC produced R2 values between 0.45 and 0.7, while those using CAP outperformed the former with R2 values of 0.9 throughout Spain. Production models outperformed yield models, and MTCI was the vegetation index (VI) that provided the greatest R2 value of 0.94. However, model accuracy was heavily conditioned by the precision of input data, where anomalies were detected in some NUTS-2. [ABSTRACT FROM AUTHOR]

The phenological parameters estimated from different data may vary, especially in response to climatic factors. Therefore, we estimated the start of the growing season (SOS) and the end of the growing season (EOS) based on sunlight-induced chlorophyll fluorescence (SIF), the normalized difference vegetation index (NDVI) and the near-infrared reflectance of vegetation (NIRv). The SIF, NDVI and NIRv breakpoints were detected, and the trends and change-points of phenological parameters based on these data were analyzed. The correlations between the phenological parameters and snow-related factors, precipitation, temperature, soil moisture and population density were also analyzed. The results showed that SIF and NIRv could identify breakpoints early. SIF could estimate the latest SOS and the earliest EOS. NDVI could estimate the earliest SOS and the latest EOS. The change-points of SOSSIF were mostly concentrated from 2001 to 2003, and those of SOSNDVI and SOSNIRv occurred later. The change-points of EOSSIF and EOSNIRv were mostly concentrated from 2001 to 2007, and those of EOSSIF occurred later. Differently from the weak correlation with SOSSIF, SOSNDVI and SOSNIRv were significantly correlated with snow-related factors. The correlation between the meteorological factors in the summer and autumn and EOSSIF was the most significant. The population density showed the highest degree of interpretation for SOSNIRv and EOSNDVI. The results reveal the differences and potentials of different remote-sensing parameters in estimating phenological indicators, which is helpful for better understanding the dynamic changes in phenology and the response to changes in various influencing factors. [ABSTRACT FROM AUTHOR]

Accurate oil palm yield prediction is necessary to sustain oil palm production for food security and economic return. However, there are limited studies on comprehensive mapping and accurate oil palm yield prediction using advanced machine learning algorithms. Using multi-temporal remote sensing data, this paper proposed a new approach to predict oil palm yield based on the normalized difference vegetation index (NDVI) and ensemble machine learning algorithm. ReliefF algorithm with linear projection was employed to select the best combination of spectral indices in oil palm discrimination. Oil palm land cover was classified using random forest (RF) and modified AdaBoost algorithms. A time-series approach known as walk-forward validation was firstly introduced to train the model using the 2016-2019 data and the one-step prediction was performed for 2020 using RF and AdaBoost. Result of the study revealed that the RF model (RMSE=0.384; MSE=0.148; MAE=0.147) outperformed the AdaBoost model (RMSE=0.410; MSE=0.168; MAE=0.176). Our research has demonstrated the value of detailed mapping and subsequent yield prediction by developing a novel approach utilising time-series satellite imagery, ensemble machine learning, and NDVI, which will assist decision-makers in managing their practices related to oil palm. [ABSTRACT FROM AUTHOR]

Extreme drought is increasing in frequency and intensity in many regions globally. Understanding the changes in vegetation resistance and resilience under aggravated drought is essential for maintaining regional ecosystem stability. In this study, a drought event–vegetation response framework was developed to explore vegetation resistance and resilience changes. The normalized difference vegetation index (NDVI) was correlated with the standardized precipitation evapotranspiration index (SPEI) at multiple timescales to screen out the vegetation response time to drought. Then, the SPEI for the response time was detected using run theory to identify drought events during the period 2000–2017. Finally, drought-induced NDVI anomaly changes were identified using a sliding window to explore the changes in resistance and resilience to drought. This study focuses on Jilin province, China, which contains a famous environmentally vulnerable area. The results illustrate that the response time of vegetation to drought is 3 months. The northwest of Jilin province is considered to be drought-vulnerable because it has suffered the most drought events, i.e., 19–21 times, with severities in the range of 2.6–3.2 and durations in the range of 3.6–4.1 months. Grassland shows the weakest resistance and the strongest resilience, and tree cover shows the strongest resistance and the weakest resilience under severe drought disturbance among all vegetation. As the severity and duration of drought increase, the resistance decreases, and the resilience increases. During the growing season, the drought from May to July significantly impacts the vegetation resistance. Drought occurring from June to July has much less impact on resilience. Drought in August to September has less impact on resistance and a more significant impact on resilience. The results of this study may increase our knowledge regarding the response of vegetation to drought and guide ecosystem stability restoration. [ABSTRACT FROM AUTHOR]

In the context of global climate change, many studies have focused on the interannual vegetation variation trends and their response to precipitation and temperature, but ignored the effects of seasonal variability. This study explored the relationship between normalized difference vegetation index (NDVI) and seasonal climate elements in the Wuliangsu Lake Basin area from 1990 to 2020, and quantified the impacts of human activities on vegetation dynamics. We used Landsat series data to analyze the spatial and temporal variation of the NDVI using the trend analysis method, the Theil–Sen median, the Mann–Kendall test, and the Hurst index. Then, we used meteorological data and land use data to quantify the effects of human activities using residual analysis, and correlation methods to determine the driving forces of NDVI variations. The results showed that the NDVI changes presented obvious regional characteristics, with a decreasing trend from southeast to northwest in Wuliangsu Lake Basin. Due to global warming, the start of the growing season (SOS) is 4.3 days (2001 to 2010) and 6.8 days (2011 to 2020) earlier compared with 1990 to 2000. The end of the season (EOS) is advanced by 3.6 days (2001 to 2010), and delayed by 8.9 days (2011 to 2020). Seasonal (spring, summer, autumn, and winter) NDVIs with precipitation and temperature show spatial heterogeneity. Further, changes in grasslands and woodlands were vulnerable to climate change and human activities. Since the beginning of the 21st century, human activity was the driving force for vegetation improvement in the Dengkou, west-central, north and southwest regions, where ecological instability is weak. This finding can provide a theoretical basis for the implementation of the same type of ecological restoration projects and the construction of ecological civilization, and contribute to the regional green and sustainable development. [ABSTRACT FROM AUTHOR]

This paper utilizes high-resolution ERA5 hourly data from 1980 to 2020 and long-term normalized difference vegetation index (NDVI) time series obtained from remote sensing and applies trend analysis, correlation analysis, lag analysis, and other methods to study the spatiotemporal characteristics of extreme rainfall at daily and hourly scales in the Huang-Huai-Hai Plain. The paper explores the NDVI's variability and its relationship with extreme hourly precipitation and analyzes the main factors affecting it. The study made the following observations: (1) The extreme daily precipitation in the Huang-Huai-Hai Plain shows a decreasing trend, with a 13.6 mm/yr reduction rate. In contrast, the proportion of extreme rainfall to total precipitation generally exceeds 20%, and the intensity of extreme rain has gradually increased. The spatial distribution pattern of extreme rainfall follows the distribution pattern of China's rain belts, with the terrain being an important influencing factor. The high-incidence areas for extreme rainfall are the Huaihe River region and the Shandong Peninsula. (2) The observed significant increase in hourly extreme precipitation events in the Shandong and Henan provinces of the Huang-Huai-Hai Plain has led to an increased risk of flooding, while the corresponding events in the northwest region of the Plain have exhibited a gradual weakening trend over time. (3) The extreme hourly precipitation in the Huang-Huai-Hai plain shows a frequent and scattered pattern, with decreasing intensity over time. Extreme precipitation mainly occurs in the first half of the night, especially between 19:00 and 21:00, with extreme hourly rainfall intensity fluctuating between 0.2 and 0.25 and the proportion of rainfall to total precipitation reaching as high as 10%. The spatial distribution of extreme hourly rainstorms during the peak period (19:00–21:00) exhibits a high rainfall volume, intensity, and frequency pattern in the eastern region, while the western part exhibits low rainfall volume, intensity, and frequency. (4) The incidence of extremely heavy rainfall in an hour has exhibited a more significant increase compared to extreme daily events in the Huang-Huai-Hai Plain, primarily in the form of backward-type precipitation. Hourly extreme precipitation events in the Huang-Huai-Hai Plain are affected by terrain and land use/cover change (LUCC), with the micro-topography of hilly areas leading to a concentrated distribution of precipitation and LUCC suppressing extreme precipitation events in arid climates. (5) At the ten-day scale, the spatial distribution of the NDVI shows a gradually increasing trend from northwest to southeast, with the highest NDVI value reaching up to 0.6 in the southern part of the study area. For extreme hourly precipitation, there is no significant change observed at the multi-year ten-day scale; while the NDVI in the northern and central parts of the Huang-Huai-Hai Plain shows a significant decreasing trend, in contrast, it presents a significant increasing trend in the southern region. (6) Finally, the correlation between NDVI at the ten-day scale and extreme hourly precipitation exhibits a decreasing pattern from north to south, with a correlation coefficient decreasing from 0.48 to 0.08. The lagged correlation analysis of extreme hourly rainfall and NDVI for one, two, and three ten-day periods shows that the lagged effect of extreme hourly precipitation on NDVI is negligible. Analyzing the correlation between extreme hourly rainfall and NDVI for different months, the impact of extreme hourly precipitation on NDVI is predominantly negative, except for June, which shows a positive correlation (0.35), passing the significance test. This study offers a scientific foundation for enhancing disaster warning accuracy and timeliness and strengthening the research on disaster reduction techniques. [ABSTRACT FROM AUTHOR]

Land degradation destroys human habitats, and vegetation is a marker reflecting land degradation. In this article, the Balochistan Province of Pakistan, which has a fragile ecological environment, was selected as a typical case to analyze its land degradation over 21 years. Relevant studies that used the NDVI and remote sensing data to monitor land degradation already existed. Based on the data product of MODIS, this study obtained the spatio-temporal trends of the normalized difference vegetation index (NDVI) changes from 2000 to 2020 using the sen+ Mann–Kendall (MK) test and Hurst index and analyzed the driving factors of land degradation and restoration by employing the multiple stepwise regression method. The residual analysis method was an effective tool for distinguishing between anthropogenic and climatic impacts, given that not all regions have a significant correlation between the NDVI and rainfall. The main climatic drivers of the NDVI were derived based on the Geodetector analysis and stripped of the main climatic factors by residual analysis to explore the influence of anthropogenic factors on the NDVI. The results show the following: (1) Balochistan is dominated by land restoration. Land restoration is mainly dominated by climate as well as both climate and human factors, and land degradation is mainly dominated by climate and human factors. (2) The Geodetector-based study found high correlations between the NDVI and TMP, MAP, AET and PET, complementing most previous residual analyses that considered only precipitation and temperature. In Balochistan, TMP, AET, PET and MAP were the dominant climatic factors affecting the spatial distribution of the NDVI; TMP with MAP and TMP with AET were the main interactive factors in the spatial distribution of the NDVI. (3) The article quantifies the impact of the anthropogenic drivers on land degradation. Human activities positively influenced the NDVI in 91.02% of the area and negatively influenced it in 8.98% of the area. (4) The overall trend of the NDVI was mainly stable, with stronger improvement than degradation, and showed strong persistence. The above findings enrich our understanding of the climatic impacts of land degradation and human impacts in arid or semi-arid regions and provide a scientific basis for ecological engineering to achieve ecological conservation and quality development in Balochistan, Pakistan. [ABSTRACT FROM AUTHOR]

The vegetation on the Tibetan Plateau (TP), as a major component of the land–atmosphere interaction, affects the TP thermal conditions. And, as a direct climatic factor of vegetation, precipitation over the TP is significantly regulated by the Indian summer monsoon (ISM). Using remote-sensing-based vegetation images, meteorological observations, and reanalysis datasets, this study deeply explored the influence of the ISM on vegetation on the TP in its main growing season, where the vegetation on the TP is indicated by the normalized difference vegetation index (NDVI). The findings reveal that the ISM is a critical external factor impacting the TP vegetation and has a significantly positive correlation with the TP precipitation and NDVI. Corresponding to a strong ISM, the South Asia high moves northwestward toward the TP and Iranian Plateau with an increase in intensity, and the cyclonic circulation develops over the south of the TP in the middle-lower troposphere. This tropospheric circulation structure aids in the transportation of more water vapor to the TP and enhances convection there, which facilitates more precipitation and thus the TP vegetation growth, featuring a uniform NDVI pattern. Since the positive correlation between precipitation over the TP and NDVI is weaker than that between the ISM and NDVI, we suggest that the ISM can influence the TP vegetation growth not only through changing precipitation but also through other local climatic factors. The increased convection and precipitation over the TP induced by the ISM can also affect the surface thermal conditions, featuring an interaction between the TP vegetation and heat sources. The evapotranspiration of vegetation and its coverage affect local latent and sensible heat fluxes, while the TP thermal condition changes affect in return the vegetation growth. In addition, the changes in thermal conditions over the TP caused by the substantial increase in vegetation may have a de-correlation effect on the relationship between the ISM and uniform NDVI pattern after the TP vegetation reaches its maximum coverage. [ABSTRACT FROM AUTHOR]

This study compares the relationship between different NDVI (Normalized Difference Vegetation Index), the NDVI of AVHRR (Advanced Very High Resolution Radiometer) (NDVIa), the NDVI of MODIS (Moderate Resolution Imaging Spectrometer) (NDVIm), and the NDVI of VIRR (Visible and Infrared Radiometer) (NDVIv), and found that there is a significant correlation between the NDVIa and the NDVIm, and between the NDVIv and the NDVIa, the relationship between the three is NDVIv < NDVIa < NDVIm. Machine learning is an important method in artificial intelligence. It can solve some complex problems through algorithms. This research uses linear regression algorithm in machine learning to construct the Fengyun Satellite NDVI correction method. By constructing a linear regression model, the NDVI value of Fengyun Satellite VIRR is corrected to a level that is basically the same as NDVIm. The corrected correlation coefficients (R2) were significantly improved, and the corrected correlation coefficients were significantly improved, and the confidence levels were all significant correlations less than 0.01. It is proved that the corrected normalized vegetation index of Fengyun Satellite has significantly improved accuracy and product quality compared with the normalized vegetation index of MODIS. [ABSTRACT FROM AUTHOR]

Exploring the relationship between cereal yield and the remotely sensed normalized difference vegetation index (NDVI) is of great importance to decision-makers and agricultural stakeholders. In this study, an approach based on the Pearson correlation coefficient and linear regression is carried out to reveal the relationship between cereal yield and Moderate Resolution Imaging Spectroradiometer (MODIS) NDVI data in the Fez-Meknes region of Morocco. The results obtained show strong correlations reaching 0.70 to 0.89 between the NDVI and grain yield. The linear regression model explains 58 to 79% of the variability in yield in regional provinces marked by the importance of cereal cultivation, and 51 to 53% in the mountainous provinces with less agricultural land devoted to major cereals. The regression slopes indicate that a 0.1 increase in the NDVI results in an expected increase in grain yield of 4.9 to 8.7 quintals (q) per ha, with an average of 6.8 q/ha throughout the Fez-Meknes region. The RMSE ranges from 2.12 to 4.96 q/ha. These results are promising in terms of early yield forecasting based on MODIS-NDVI data, and consequently, in terms of grain import planning, especially since the national grain production does not cover the demand. Such remote sensing data are therefore essential for administrations that are in charge of food security decisions. [ABSTRACT FROM AUTHOR]

As one of the earliest remote sensing indices, the Normalized Difference Vegetation Index (NDVI) has been employed extensively for vegetation research. However, despite an abundance of NDVI review articles, these studies are predominantly limited to either one subject area or one area, with systematic NDVI reviews being relatively rare. Bibliometrics is a useful method of analyzing scientific literature that has been widely used in many disciplines; however, it has not yet been applied to comprehensively analyze NDVI research. Therefore, we used bibliometrics and scientific mapping methods to analyze citation data retrieved from the Web of Science during 1985–2021 with NDVI as the topic. According to the analysis results, the amount of NDVI research increased exponentially during the study period, and the related research fields became increasingly varied. Moreover, a greater number of satellite and aerial remote sensing platforms resulted in more diverse NDVI data sources. In future, machine learning methods and cloud computing platforms led by Google Earth Engine will substantially improve the accuracy and production efficiency of NDVI data products for more effective global research. [ABSTRACT FROM AUTHOR]

Vegetation is an important factor linking the atmosphere, water, soil, and biological functions, and it plays a specific role in the climate change response and sustainable development of regional economies. However, little information is available on vegetation vulnerability and its driving mechanism. Therefore, studying temporal and spatial change characteristics of vegetation and their corresponding mechanisms is important for assessing ecosystem stability and formulating ecological policies in the Kherlen River Basin. We used Moderate-resolution Imaging Spectroradiometer (MODIS) normalized difference vegetation index (NDVI) remote sensing images from 2000 to 2020 to analyse temporal changes in NDVI with the autoregressive moving average model (ARMA) and the breaks for additive season trend (BFAST) in the basin and to assess natural, anthropogenic and topographic factors with the Geodetector model. The results show that: 1) the long NDVI time series remained stable in the Kherlen River Basin from 2000 to 2020, with a certain significant mutation period from 2013 to 2017; 2) the coefficient of variation (CV) in the analysis of the spatial NDVI was generally constant, mainly at the level of 0.01–0.07, and the spatial NDVI change was minimally impacted by external interference; and 3) temperature and precipitation are the key factors affecting the NDVI in the basin, and changes in local hydrothermal conditions directly affect the local NDVI. The results of this study could provide a scientific basis for the effective protection of the ecological environment and will aid in understanding the influence of vegetation change mechanisms and the corresponding factors. [ABSTRACT FROM AUTHOR]