Normalized Difference Vegetation Index Dataset Research Articles

Automated generation of consistent annual maximum NDVI on coal bases with a new algorithm

Coal is one of the most important fossil energy sources and is ensuring global energy security. Annual maximum NDVI (Normalized Difference Vegetation Index) data is an important indicator for the research in balancing coal mining and vegetation conservation. However, the existing annual maximum NDVI data displayed lower values with temporally inconsistent and a noticeable mosaic line. Here we propose an algorithm for automatically generating the annual maximum NDVI of China’s coal bases in Google Earth Engine called: Auto-NDVIcb. The accuracy of the Auto-NDVIcb algorithm has been verified with an average RMSE of 0.087 for the 14 coal bases from 2013 to 2022. Based on the proposed Auto-NDVIcb algorithm, an annual maximum NDVI dataset for all 14 coal bases in China from 2013 to 2022 was publicly released. This dataset can be fast and automatically updated online. Hence, the public dataset will continuously serve to monitor the vegetation change induced by coal mining, exploring the mechanism of vegetation degradation, and providing scientific data for developing vegetation protection policies in coal mines.

Spatio-temporal dynamics of vegetation over cloudy areas in Southwest China retrieved from four NDVI products

The Normalized Difference Vegetation Index (NDVI) is the most commonly used index for assessing vegetation. However, significant differences among various satellite datasets, complex terrain, and the impact of clouds on optical sensors limit vegetation change assessment based on NDVI. To address these issues, this study utilizes multi-source satellite data (GIMMS3g NDVI, CDR AVHRR NDVI, SPOT NDVI, and MODIS NDVI) to monitor vegetation dynamics at different time scales from 1990 to 2020 in Sichuan Province, China. The results indicate that over time, NDVI values from the four NDVI products in Sichuan Province have shown an upward trend. There are certain differences in the spatial distribution and spatial heterogeneity of the change rate of NDVI values among the four NDVI products at different time scales, and the differences are mainly concentrated in the Sichuan Basin (SB) and the Western Sichuan alpine plateau region (WS). Compared with the other three NDVI products, GIMMS NDVI has the highest value but the smallest increase during the study period. The SPOT NDVI value is the smallest, but the increase is relatively large. However, within the overlapping period of the four NDVI datasets, only the annual average of CDR AVHRR NDVI showed a downward trend (slope2000–2013 = −0.0001·a−1). The annual fluctuation of CDR AVHRR NDVI is the smallest, and compared to other NDVI datasets, its correlation with climate factors shows significantly weaker spatial variability. Moreover, the ability of CDR AVHRR NDVI to distinguish different vegetation land cover types is significantly poor (STD = 0.045). The findings of this study will provide a reference for further research on vegetation changes in Sichuan Province and NDVI reconstruction in cloudy areas.

Climatic Constraints of Spring Phenology and Its Variability on the Mongolian Plateau From 1982 to 2021

AbstractThe start of vegetation growing season (SOS) plays an important role in the energy cycle between the land and atmosphere. Due to the limited temporal span of a single satellite sensor through time, the continuous variation of the SOS over 40 years has not been adequately quantified. Using the overlapping periods (2001–2015) between the Global Inventory Modeling and Mapping Studies (GIMMS) (1982–2015) and Moderate Resolution Imaging Spectroradiometer (MODIS) normalized difference vegetation index (NDVI) (2001–2021) data sets, we construct an NDVI data set covering the period 1982–2021 on the Mongolian Plateau and further construct a map of relative climatic constraint on the SOS (divided into “temperature‐constrained,” “precipitation‐constrained,” and “other” regions) for quantifying SOS variability. We show that the constructed NDVI data set has high consistency and continuity with earlier GIMMS NDVI data. Regions with the SOS constrained by temperature account for 55.3% of the plateau and are located in northwestern and northeastern cold areas, while regions with the SOS constrained by precipitation constitute over 34.7% and are located in central and southwestern drier regions. Importantly, the temperature‐constrained SOS has continuously and significantly advanced, with a total advance of 4.8 days over 40 years. In contrast, the precipitation‐constrained SOS reversed from advancing to delaying in 2005. This suggests that differentiating the climatic constraint on the SOS might be a practical treatment for reducing the uncertainties in the SOS trends in previous studies. Interestingly, the precipitation‐constrained SOS does not significantly correlate to both the chilling and forcing temperatures, indicating less dependency of the SOS on chilling, which may not have been well considered previously.

Mapping and classification of Liao River Delta coastal wetland based on time series and multi-source GaoFen images using stacking ensemble model

The precise mapping of coastal wetlands holds great significance for monitoring carbon sequestration and storage within coastal ecosystems, particularly in light of climate change and human-induced activities. Time series and multi-source remote sensing data offer distinct advantages in spatial and temporal land use mapping, particularly in wetland systems encompassing various vegetation types. The primary aim of this study was to delineate the spatial distribution of land use within the Liao River Delta (LRD) wetland. This was achieved by employing a stacking ensemble model that integrates time-series GaoFen-1 (GF-1) optical imagery, GaoFen-3 (GF-3) synthetic aperture radar (SAR) imagery, and Moderate Resolution Imaging Spectroradiometer (MODIS) Normalized Difference Vegetation Index (NDVI) imagery. The first step involved the application of an enhanced spatial and temporal adaptive reflectance fusion model (ESTARFM) to fuse the GF-1 NDVI and MODIS NDVI datasets, producing time-series NDVI data. Subsequently, the parameters pertaining to vegetation phenology were obtained by employing the threshold method on the time-series NDVI data. We compiled feature datasets encompassing GF-1 spectral bands, indices, phenological parameters, and GF-3 SAR features. A Recursive Feature Elimination and Cross-Validation (RFECV) model was employed to identify and select significant features to mitigate data redundancy. Finally, a stacking ensemble model was constructed by combining five base models [K-Nearest Neighbors (KNN), Random Forest (RF), Adaptive Boosting (AdaBoost), Extreme Gradient Boosting (XGBoost), and a Light Gradient Boosting Machine (LightGBM)] to perform wetland classification. The findings were as follows: (1) ESTARFM was able to successfully fuse GF-1 NDVI and MODIS NDVI data, resulting in spatiotemporal fusion with a coefficient of determination (R2) of 0.85 and a root mean square error (RMSE) of 0.07. (2) the RFECV algorithm was employed to select relevant features in the wetland classification process. Specifically, 75 spectral band features, 89 spectral index features, 13 SAR features, and seven phenological parameters were identified as significant for this task. (3) A stacking ensemble model was constructed using the aforementioned multi-source features. This model exhibited a robust and consistent performance in wetland classification, achieving the highest overall accuracy of 94.33%. Notably, this accuracy improvement ranged from approximately 0.09% to 10.02% compared to the individual base models. Thus, the present study has the potential to be utilized for fine-scale wetland monitoring, thereby offering valuable assistance in the field of wetland environmental research.

Reconstructed NDVI and EVI datasets in China (ReVIChina) generated by a spatial-interannual reconstruction method

ABSTRACTRemote sensing-based vegetation index (VI) data are significantly impacted by cloud contamination. Spatiotemporal reconstruction methods demonstrate higher accuracy than temporal reconstruction methods. However, the computing time and random access memory (RAM) consumption of these spatiotemporal reconstruction methods for large-scale reconstruction remains unclear. In this study, a method called spatial-interannual reconstruction (SIR) was proposed to reconstruct cloud-contaminated pixels in MODIS normalized difference VI (NDVI) and enhanced VI (EVI) data. SIR has four major advantages: (1) High accuracy. The average mean absolute error of SIR was 0.0338, which was 20.2% and 23.4% lower than that of two state-of-the-art spatiotemporal reconstruction methods (i.e. interpolation of the mean anomalies (IMA) and Gapfill). (2) High computing speed. The average computing time of SIR was 99.7% and 98.8% lower than IMA and Gapfill, respectively. (3) Low RAM consumption. (4) Simultaneous reconstruction of all invalid values. Reconstructed 250 m spatial resolution and 16-day composite NDVI and EVI datasets in China from 2000 to 2022 (written as ReVIChina) were developed based on the SIR method and MODIS MOD13Q1 data. Spatiotemporal analyses revealed that the reconstructed datasets were more reliable than the original product and a similar dataset.

Influence of Climate, Topography, and Hydrology on Vegetation Distribution Patterns—Oasis in the Taklamakan Desert Hinterland

Vegetation in natural desert hinterland oases is an important component of terrestrial ecosystems. Determining how desert vegetation responds to natural variability is critical for a better understanding of desertification processes and their future development. The aim of this study is to characterize the spatial distribution of vegetation in the natural desert hinterland and to reveal how different environmental factors affect vegetation changes. Taking a Taklamakan Desert hinterland oasis as our research object, we analyzed the effects of different environmental factors on desert vegetation using a time-series normalized difference vegetation index (NDVI) combined with meteorological, topographic, and hydrological data, including surface water and groundwater data. Vegetation was distributed in areas with high surface water frequency, shallow groundwater levels, relatively flat terrain, and dune basins. NDVI datasets show greening trends in oasis areas over the past 20 years. The frequency of surface water distribution influences water accessibility and effectiveness and shapes topography, thus affecting the spatial distribution pattern of vegetation. In this study, areas of high surface water frequency corresponded with vegetation distribution. The spatial distribution of groundwater depth supports the growth and development of vegetation, impacting the pattern of vegetation growth conditions. Vegetation is most widely distributed in areas where the groundwater burial depth is 3.5–4.5 m. This study provides data for restoring riparian vegetation, ecological water transfer, and sustainable development.

Applying deep-learning enhanced fusion methods for improved NDVI reconstruction and long-term vegetation cover study: A case of the Danjiang River Basin

The Normalized Difference Vegetation Index (NDVI) is an essential metric in vegetation monitoring for remote sensing applications. While there are numerous long-term low-resolution NDVI datasets available there remains an unmet need for high-resolution NDVI reconstructions over extended time frames. Existing research has not comprehensively assessed the efficacy of various temporal fusion techniques for NDVI reconstruction at large regional scales. Traditional Spatiotemporal Image Fusion (TSTIF) methods often suffer from limited fusion accuracy due to input data quality constraints. To address these limitations this study introduces an innovative Deep Learning-Enhanced Spatiotemporal Fusion Method. Deep learning algorithms are employed to refine the spatial resolution of input data thereby facilitating the separation of complex image elements located at feature boundaries. This improved approach significantly enhances fusion accuracy as validated against five established TSTIF techniques through empirical analysis. As a case study we generate long-term Fractional Vegetation Cover (FVC) datasets to investigate the ecological dynamics of the Danjiang River Basin. Our findings reveal substantial gains in NDVI reconstruction accuracy through the incorporation of deep learning into traditional fusion techniques. Among the methods tested the STRUM algorithm showed the greatest improvement with its R2 value increasing from 0.872 to 0.894 and a consistent reduction in Root Mean Square Error (RMSE). The FSDAF technique emerged as the most effective boasting an R2 value of 0.953 and an RMSE of 0.012. These enhanced NDVI datasets provide more accurate representations of the basin's vegetation cover thus enriching long-term observational archives and facilitating further quantitative remote sensing analyses. Moreover we report a 21-year mean FVC of 0.702 for the Danjiang River Basin characterized by a “north-high south-low” distribution that varies seasonally. Improvements in vegetation cover were observed in Xichuan and Neixiang counties while environmental degradation is evident in the broader Danjiang River watershed. Key factors influencing these spatial variations include elevation and soil composition in addition to climate variables such as rainfall and temperature. Infrastructure projects like the South-to-North Water Diversion Project and targeted ecological conservation policies have also positively impacted vegetation within the basin.

Appraisal of Climate Response to Vegetation Indices over Tropical Climate Region in India

Extreme climate events are becoming increasingly frequent and intense due to the global climate change. The present investigation aims to ascertain the nature of the climatic variables association with the vegetation variables such as Leaf Area Index (LAI) and Normalized Difference Vegetation Index (NDVI). In this study, the impact of climate change with respect to vegetation dynamics has been investigated over the Indian state of Haryana based on the monthly and yearly time-scale during the time period of 2010 to 2020. A time-series analysis of the climatic variables was carried out using the MODIS-derived NDVI and LAI datasets. The spatial mean for all the climatic variables except rainfall (taken sum for rainfall data to compute the accumulated rainfall) and vegetation parameters has been analyzed over the study area on monthly and yearly basis. The liaison of NDVI and LAI with the climatic variables were assessed at multi-temporal scale on the basis of Pearson correlation coefficients. The results obtained from the present investigation reveals that NDVI and LAI has strong significant relationship with climatic variables during the cropping months over study area. In contrast, during the non-cropping months, the relationship weakens but remains significant at the 0.05 significance level. Furthermore, the rainfall and relative humidity depict strong positive relationship with NDVI and LAI. On the other, negative trends were observed in case of other climatic variables due to the limitations of NDVI viz. saturation of values and lower sensitivity at higher LAI. The influence of aerosol optical depth was observed to be much higher on LAI as compared to NDVI. The present findings confirmed that the satellite-derived vegetation indices are significantly useful towards the advancement of knowledge about the association between climate variables and vegetation dynamics.

A Study of the Method for Retrieving the Vegetation Index from FY-3D MERSI-II Data

NDVI data have been widely used to detect and monitor vegetation status at regional, continental, and global scales. FY-3D MERSI-II NDVI (FNDVI) is a critical operational product used in many studies monitoring ecosystems and agriculture and assessing climate change and its risks, including drought and fire. MERSI-II and MODIS have very similar spectral response functions in the red and near-infrared channels, making MERSI/NDVI an effective replacement for MODIS/NDVI (MNDVI). Therefore, it is critical to conduct a thorough evaluation of the product’s quality. In this study, the consistency characteristics of two normalized difference vegetation index (NDVI) products, FY-3D MERSI-II NDVI and MODIS NDVI, were compared and validated at national and regional scales in China from 2020 to 2021. To assess the consistency of these two NDVI datasets, the correlation coefficient, root-mean-square error, and mean bias error were used. The findings revealed that the spatial distribution patterns of FNDVI and MNDVI were highly consistent across the country at the monthly time scale. The correlation coefficients were greater than 0.9475 for the two years 2020–2021, while the average deviation was between 0.02 and 0.05, and the root-mean-square error was 0.11. Based on the difference in the time consistency between FNDVI and MNDVI, the changes in the monthly NDVI values of the two types of satellites are generally consistent across the country. Among the three typical experimental areas, the relative deviation of the regional time series for products was the highest in Xinjiang. The relative average deviation of FNDVI in other regions was low, and its change trend was consistent with that of MODIS.

Deep spatial-temporal graph modeling for efficient NDVI forecasting

Spatio-temporal graph modelling is a new, prominent predictive tool to use on datasets with complex spatial and temporal relationships. Normalized Difference Vegetation Index (NDVI) is a remote measure offering these complex relationships, used by agricultural producers and researchers due to its strong correlation with crop growth. Accurate periodic field-level NDVI forecasting helps project crop yield, crucial for planning agricultural production. This NDVI forecasting problem was previously studied, with best results obtained by Convolutional Long Short-Term Memory (ConvLSTM) architecture. We modify the ConvLSTM architecture, improving over the original paper. Additionally, we propose a new architecture based on Graph WaveNet (GWNN). GWNN captures spatial relationships in the non-tabular data with an adaptive dependency matrix and long-range temporal relationships with stacked spatial-temporal layers. We test each model (original ConvLSTM, new ConvLSTM, and GWNN) over the same geographical points. Under Root Mean Square Error metric, GWNN outperforms original ConvLSTM by 31% and our new one by 15%. Moreover, the GWNN is more than 170 times faster at training. We compare these models on other NDVI datasets, up to 50 times larger than the original set. The consistent results show the GWNN is most efficient in both quality and runtime for the NDVI forecasting problem.

Reconstructing NDVI and land surface temperature for cloud cover pixels of Landsat-8 images for assessing vegetation health index in the Northeast region of Thailand.

Critical applications of satellite data products include monitoring vegetation dynamics and assessing vegetation health conditions. Some indicators like normalized difference vegetation index (NDVI) and land surface temperature (LST) are used to assess the status of vegetation growth and health. But one of the major problems with passive remote sensing satellite data products is cloud and shadow cover that leads to data gaps in the images. The present study proposes temporal aggregation of images over a short time span and developing short span harmonic analysis of time series (SS-HANTS) and pixel-wise multiple linear regression (PMLR) algorithms for retrieving cloud contaminated NDVI and LST information from Landsat-8 (L8) data products, respectively. The developed algorithms were applied in the northeastern part of Thailand to recover the missing NDVI and LST values from time series L8 images acquired in 2018. The predicted NDVI and LST values at artificially clouded locations were compared with the corresponding clear pixel values. Additionally, the model predicted LST and NDVI values were also compared with MODIS LST and NDVI datasets. The calculated root mean square (RMSE) values were ranging from 0.03 to 0.11 and 1.50 to 2.98°C for NDVI and LST variables, respectively. The validation statistics show that these models can be satisfactorily applied to retrieve NDVI and LST values from cloud-contaminated pixels of L8 images. Furthermore, a vegetation health index (VHI) computed from cloud retrieved continuous NDVI and LST images at province level shows that most of the western provinces have healthy vegetation condition than other provinces in the northeast of Thailand.

Extraction and spatiotemporal changes of open-pit mines during 1985-2020 using Google Earth Engine: A case study of Qingzhou City, Shandong Province, China.

The global use of mineral resources has increased exponentially for decades and will continue to grow for the foreseeable future, resulting in increasingly negative impacts on the surrounding environment. However, to date, there are a lack of historical and current spatial extent datasets with high accuracy for mining areas in many parts of the world, which has hindered a more comprehensive understanding of the environmental impacts of mining. Using the Google Earth Engine cloud platform and the Landsat normalized difference vegetation index (NDVI) datasets, the spatial extent data of open-pit mining areas for eight years (1985, 1990, 1995, 2000, 2005, 2010, 2015, and 2020) was extracted by the Otsu algorithm. The limestone mining areas in Qingzhou, Shandong Province, China, was selected as a case study. The annual maximum NDVI was first derived from the Landsat NDVI datasets, and then the Otsu algorithm was used to segment the annual maximum NDVI images to obtain the extent of the mining areas. Finally, the spatiotemporal characteristics of the mining areas in the study region were analyzed in reference to previous survey data. The results showed that the mining areas were primarily located in Shaozhuang Town, Wangfu Street and the northern part of Miaozi Town, and the proportion of mining areas within these three administrative areas has increased annually from 88% in 1985 to more than 98% in 2010. Moreover, the open-pit mining areas in ​​Qingzhou gradually expanded from a scattered, point-like distribution to a large, contiguous distribution. From 1985 to 2020, the open-pit mining area expanded to more than 10 times its original size at a rate of 0.5 km2/year. In 2015, this area reached its maximum size of 19.7 km2 and slightly decreased in 2020. Furthermore, the expansion of the mining areas in Qingzhou went through three stages: a slow growth period before 1995, a rapid expansion period from 1995 to 2005, and a shutdown and remediation period after 2005. A quantitative accuracy assessment was performed by calculating the Intersection over Union (IoU) of the extraction results and the visual interpretation results from Gaofen-2 images with 1-m spatial resolution. The IoU reached 72%. The results showed that it was feasible to threshold the Landsat annual maximum NDVI data by the Otsu algorithm to extract the annual spatial extent of the open-pit mining areas. Our method will be easily transferable to other regions worldwide, enabling the monitoring of mine environments.

Quantitative Assessment of the Spatial Scale Effects of the Vegetation Phenology in the Qinling Mountains

Vegetation phenology reflects the temporal dynamics of vegetation growth and is an important indicator of climate change. However, differences consistently exist in land surface phenology derived at different spatial scales, which hinders the understanding of phenological events and integration of land surface phenology products from different scales. The Qinling Mountains are a climatic and geographical transitional region in China. To better understand the spatial scale effect issues of land surface phenology in mountainous ecosystems, this study up-scaled vegetation start of season (SOS) and end of season (EOS) in the Qinling Mountains derived from three different Moderate Resolution Imaging Spectroradiometer (MODIS) normalized difference vegetation index (NDVI) products to four scales (i.e., 2 km × 2 km, 4 km × 4 km, 6 km × 6 km, and 8 km × 8 km) using the spatial averaging method. Then, similarities and differences between the up-scaled SOSs/EOSs were examined using the simple linear regression, cumulative distribution function, and absolute difference. Finally, the random forest model was used to reveal the major factors influencing the spatial scale effect of land surface phenology in Qinling Mountains. Results showed that the derived basic SOS/EOS datasets using the same filtering method from the 250 m and 500 m NDVI datasets were consistent in spatial distribution, while the results from the 1000 m NDVI dataset differed. For both the basic and the up-scaled datasets, the land surface phenology derived from the Savitzky-Golay-filtered NDVI showed an advance in SOS, but a delay in EOS, compared to those derived from the asymmetric Gaussian- and double logistic-filtered NDVI. The up-scaled SOS was greatly impacted by both NDVI resolution and the filtering methods. On the other hand, EOS was mostly impacted by the filtering methods. Moreover, up-scaled SOSs usually had larger differences compared to up-scaled EOSs. While different filtering methods sometimes amplified the absolute differences between different SOS/EOS across scales, the upscaling reduced the differences. Influence factor analysis showed that spatial variations observed in SOS in Qinling Mountains were mainly caused by forest cover, uneven distribution of spring precipitation, and annual precipitation, while spatial variations in aspect, winter temperature, and autumn precipitation all strongly influenced the observed EOS across scales in the study area. These findings enhance our understanding of the effects of observational scale on vegetation phenology in mountain ecosystems and provide a reference for phenology modeling in mountainous areas.

The Effect of Psychological Burden on Dyslipidemia Moderated by Greenness: A Nationwide Study from China.

Globally, dyslipidemia is now become a leading risk factor for many adverse health outcomes, especially in the middle-aged and elderly. Recent evidence suggests that exposure to greenness and the relief of a psychological burden may decrease the prevalence of dyslipidemia. The objective of our study was to examine whether a green space can moderate the association between mental health status and dyslipidemia. Our study selected the datasets of depression symptoms, dyslipidemia from the China Health and Retirement Longitudinal Study (CHARLS), and the satellite-based normalized difference vegetation index (NDVI) from the 30 m annual maximum NDVI dataset in China in 2018. Ultimately, a total of 10,022 middle-aged and elderly Chinese were involved in our study. Multilevel logistic regressions were performed to examine the association between symptoms of depression and dyslipidemia, as well as the moderate effect of greenness exposure on the association. Our research suggested that adults diagnosed with depression symptoms were more likely to suffer from dyslipidemia. In addition, the NDVI was shown to moderate the effect of depression on dyslipidemia significantly, though the effect was attenuated as depression increased. Regarding the moderate effect of the NDVI on the above association across age, gender, and residence, the findings presented that females, the elderly, and respondents living in urban areas were at a greater risk of having dyslipidemia, although the protective effect of the NDVI was considered. Likewise, the moderate effect of the NDVI gradually decreased as the level of depression increased in different groups. The current study conducted in China provides insights into the association between mental health, green space, and dyslipidemia. Hence, improving mental health and green spaces can be potential targets for medical interventions to decrease the prevalence of dyslipidemia.

Drought index revisited to assess its response to vegetation in different agro-climatic zones

A combination of in-situ climatic variables and remote sensing data could provide dynamic information that facilitates to get effective dryness or wetness situations for planetary ecosystems. Thus, the traditional standardized precipitation evapotranspiration index (SPEI) was revisited by drought index (DI) using satellite soil moisture data and land surface evapotranspiration from Penman-Monteith-Leuning Version 2 (PML_V2) datasets to assess the dryness over 2417 station’s grid points in China. DI was compared with the self-calibrating Palmer drought severity index (scPDSI), SPEI and composite drought index (CDI) for three major land cover classes (croplands, grasslands, and forests) area. The independent evaluation was carried out using sun-induced chlorophyll fluorescence (vegetation photosynthesis), and normalized difference vegetation index datasets including crop yield. Results exhibited that DI performance was comparable with CDI than scPDSI and SPEI to capture the impact of drought on vegetation photosynthesis. Vegetation productivity assessed by NDVI was significantly affected by dryness in the northern and southern part of China using DI, CDI and scPDSI and compared to SPEI. DI displayed a positive correlation with wheat, maize, and rice yield. Revisited drought index showed significant results for vegetation productivity but still necessitate future work to improve DI for agricultural drought research.

Using remote sensing to assess plant health and drought response in game reserves and adjacent farmland overtime in the Eastern Cape, South Africa

The aim of the study described in this article was to investigate the vegetation health and drought response of naturally occurring Albany thicket and neighbouring farmland vegetation, that appears in an area of the Eastern Cape, South Africa. Google Earth Engine was used to manipulate Landsat 5, 7 and 8 datasets to produce a 30-year temporal dataset, after which the normalized difference vegetation index (NDVI) and the normalized difference water index (NDWI) were then applied to create a time series analysis. The Mann-Kendall and Spearman correlation statistical tests were used on the time series to observe trends and correlations between the NDVI and the NDWI datasets. The Spearman correlation test results showed that there were high correlations between the NDVI and the NDWI datasets (all above 0.805). Furthermore, the Man-Kendall test showed that all the datasets had positively increasing trends, while the NDVI datasets all had monotonic positive trends. Large differences in the NDVI and the NDWI were seen for the different vegetation types during times of drought, and farmland was the most severely affected with an average of 19% decrease in the NDVI and an average of 71% decrease in the NDWI.

Linear and Non-Linear Vegetation Trend Analysis throughout Iran Using Two Decades of MODIS NDVI Imagery

Vegetation is the main component of the terrestrial Earth, and it plays an imperative role in carbon cycle regulation and surface water/energy exchange/balance. The coupled effects of climate change and anthropogenic forcing have undoubtfully impacted the vegetation cover in linear/non-linear manners. Considering the essential benefits of vegetation to the environment, it is vital to investigate the vegetation dynamics through spatially and temporally consistent workflows. In this regard, remote sensing, especially Normalized Difference Vegetation Index (NDVI), has offered a reliable data source for vegetation monitoring and trend analysis. In this paper, two decades (2000 to 2020) of Moderate Resolution Imaging Spectroradiometer (MODIS) NDVI datasets (MOD13Q1) were used for vegetation trend analysis throughout Iran. First, the per-pixel annual NDVI dataset was prepared using the Google Earth Engine (GEE) by averaging all available NDVI values within the growing season and was then fed into the PolyTrend algorithm for linear/non-linear trend identification. In total, nearly 14 million pixels (44% of Iran) were subjected to trend analysis, and the results indicated a higher rate of greening than browning across the country. Regarding the trend types, linear was the dominant trend type with 14%, followed by concealed (11%), cubic (8%), and quadratic (2%), while 9% of the vegetation area remained stable (no trend). Both positive and negative directions were observed in all trend types, with the slope magnitudes ranging between −0.048 and 0.047 (NDVI units) per year. Later, precipitation and land cover datasets were employed to further investigate the vegetation dynamics. The correlation coefficient between precipitation and vegetation (NDVI) was 0.54 based on all corresponding observations (n = 1785). The comparison between vegetation and precipitation trends revealed matched trend directions in 60% of cases, suggesting the potential impact of precipitation dynamics on vegetation covers. Further incorporation of land cover data showed that grassland areas experienced significant dynamics with the highest proportion compared to other vegetation land cover types. Moreover, forest and cropland had the highest positive and negative trend direction proportions. Finally, independent (from trend analysis) sources were used to examine the vegetation dynamics (greening/browning) from other perspectives, confirming Iran’s greening process and agreeing with the trend analysis results. It is believed that the results could support achieving Sustainable Development Goals (SDGs) by serving as an initial stage study for establishing conservation and restoration practices.

Reconstructing High-Spatiotemporal-Resolution (30 m and 8-Days) NDVI Time-Series Data for the Qinghai–Tibetan Plateau from 2000–2020

As the largest and highest alpine ecoregion in the world, the Qinghai–Tibetan Plateau (QTP) is extremely sensitive to climate change and has experienced extraordinary warming during the past several decades; this has greatly affected various ecosystem processes in this region such as vegetation production and phenological change. Therefore, numerous studies have investigated changes in vegetation dynamics on the QTP using the satellite-derived normalized-difference vegetation index (NDVI) time-series data provided by the Moderate-Resolution Imaging Spectroradiometer (MODIS). However, the highest spatial resolution of only 250 m for the MODIS NDVI product cannot meet the requirement of vegetation monitoring in heterogeneous topographic areas. In this study, therefore, we generated an 8-day and 30 m resolution NDVI dataset from 2000 to 2020 for the QTP through the fusion of 30 m Landsat and 250 m MODIS NDVI time-series data. This dataset, referred to as QTP-NDVI30, was reconstructed by employing all available Landsat 5/7/8 images (>100,000 scenes) and using our recently developed gap-filling and Savitzky–Golay filtering (GF-SG) method. We improved the original GF-SG approach by incorporating a module to process snow contamination when applied to the QTP. QTP-NDVI30 was carefully evaluated in both quantitative assessments and visual inspections. Compared with reference Landsat images during the growing season in 100 randomly selected subregions across the QTP, the reconstructed 30 m NDVI images have an average mean absolute error (MAE) of 0.022 and a spatial structure similarity (SSIM) above 0.094. We compared QTP-NDVI30 with upscaled cloud-free PlanetScope images in some topographic areas and observed consistent spatial variations in NDVI between them (averaged SSIM = 0.874). We further examined an application of QTP-NDVI30 to detect vegetation green-up dates (GUDs) and found that QTP-NDVI30-derived GUD data show general agreement in spatial patterns with the 250 m MODIS GUD data, but provide richer spatial details (e.g., GUD variations at the subpixel scale). QTP-NDVI30 provides an opportunity to monitor vegetation and investigate land-surface processes in the QTP region at fine spatiotemporal scales.

Differential effects of topography on the timing of the growing season in mountainous grassland ecosystems

• What is the overall scientific problem and why did you study it? • Topography is a key driver of the timing of ecosystem processes in mountainous areas. • How did you address the problem, and which spheres are included? • Different aspects of topography affect different stages of annual growth in grasslands. • What was the major method used? • Remote sensing revealed timing of annual grassland growth in relation to topography. • Major finding(s) • Elevation affects the timing of the start, aspect the end of the growing season. • Take home message • Elevation and aspect do not lead to equivalent responses in grassland phenology. Grasslands in mountainous areas often show distinct responses in the timing of their growing season in relation to topographical variation. However, it is unclear which factors of topography (elevation, aspect, and slope) affect which phases of the timing in seasonal growth (start, peak, end, length of growing season). Here we investigated these relationships between topography and growing season timing in the three key grassland types in mountainous areas in South Island, New Zealand. From a near-daily NDVI (Normalized Difference Vegetation Index) dataset over a 16 year period (2001–2016), we extracted five annual land surface phenology indices: start, end, length, peak of the growing season and peak NDVI. Averages in these phenology indices were correlated with three topographical factors. The start of growing season occurred later by 7.1, 5.2 and 3.5 days per 100 m elevation in the three grassland types (Alpine, Tall Tussock and Low Producing grasslands). The end of the season occurred earlier by 1.8, 1.6 days and later by 0.4 days per 10-degree more south-facing (colder) aspects in the three grasslands. A longer growing season was observed at lower elevation and on north-facing (sunny) slopes in alpine grasslands. A later season peak occurred at higher elevation and on north-facing slopes in alpine grasslands and at higher elevations and on steeper slopes in non-alpine grasslands. Higher peak NDVI was detected at the lower elevation. Our results show that different facets of a landscape's topography affect different stages of a grassland's growing season, and these responses also differ between grassland types. This highlights the importance of considering all topographical features when relationships between the physical environment and biological responses are investigated.

Estimation of crop water requirements within Wainganga sub-basin for Kharif and Rabi season using spatial analysis

In India, largest user of the water is agriculture sector is the, so it is important to do the proper management of available water. Aim of this study to estimate crop water requirements (CWR) for Kharif and Rabi seasons within Wainganga sub-basin by using remote sensing and GIS technique. For this, reference evapotranspiration (ET0) is estimated by using the Food and Agricultural Organization (FAO) Penman-Monteith method. Weather data from 8 weather stations has been collected to estimate ET0. The crop coefficient (KC) is estimated by using linear relationship with Normalized Difference Vegetation Index (NDVI). The MODIS NDVI dataset is used for calculation of crop coefficient. The effective precipitation (Pe) has been calculated to estimate CWR by using FAO recommended empirical method. The spatial variation maps for ET0, KC, actual evapotranspiration (ETa) and CWR are generated using Inverse Distance Weightage (IDW) interpolation technique in ArcGIS software. The results show that ET0 and ETa are higher in Kharif season than the Rabi season. The KC is also found higher in kharif than in rabi. Since the Pe is negligible in Rabi season, CWR in Rabi season is found to be higher than kharif season. The CWR in Rabi varies between 320 mm to 378 mm and in kharif season it varies between 94 mm to 263 mm. It is concluded that the seasonal estimation of CWR helps in understanding the peak water demand in that season in better way. It is required to provide sufficient irrigation to the crops in Rabi season especially as the agricultural production completely depends on the irrigation facilities in the study area because there is no rainfall in this season.