MODIS Normalized Difference Vegetation Index Research Articles

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.

Monitoring Grassland Variation in a Typical Area of the Qinghai Lake Basin Using 30 m Annual Maximum NDVI Data

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.

Spatiotemporal evolution characteristics and driving forces of vegetation cover variations in the Chengdu-Chongqing region of China under the background of rapid urbanization.

The research on the spatiotemporal changes and driving factors of ecosystems in rapidly urbanizing regions has always been a topic of widespread concern. As the fourth pole of China's economic development, the research on the Chengdu-Chongqing region has reference significance for the urbanization process of developing countries such as India, Brazil, and South Africa.The normalized difference vegetation index (NDVI) has been widely applied in studies of plant and ecosystem changes. Based on MODIS NDVI data from 2001 to 2020 and meteorological data of the same period, this study reveals the evolution of NDVI in the Chengdu-Chongqing region from three aspects: the spatiotemporal variation characteristics of NDVI, the prediction of future trends in vegetation coverage, and the response of vegetation to climate change and human activities. During the period of plant growth, the mean NDVI achieved a value of 0.78, and the vegetation coverage rate is increasing year by year. According to the Hurst index, the future NDVI in Chengdu-Chongqing region will tend to decrease, and its trend is opposite to that of the past period of time. The Chengdu-Chongqing region vegetation positively affected by human activities is greater than those negatively affected, and in terms of vegetation degradation, the impact of human activities is greater than climate change.

Climate change and topographic differences influence grassland vegetation greening across environmental gradients

Despite the research on the responses of grassland vegetation to climate change and topography has advance worldwide, the large-scale importance of these parameters to grassland vegetation greening in arid regions across environmental gradients is unclear. To address this, in this study, we applied MODIS Normalized Difference Vegetation Index (NDVI) data and trend analysis methods to measure the spatial–temporal variation in grassland vegetation greening in central Eurasia. Multiple regression models and hierarchical partitioning were used to quantify the importance of climate [annual precipitation (AP), annual mean temperature (AMT), relative humidity (RH)] and topography [elevation (ELE), aspect (ASP), topographic position index (TPI)] to the NDVI. The results showed that there was a significant increasing trend in the NDVI of meadows, but not other grassland types, from 2000 to 2021 (3.3 × 10−3/year, p < 0.05). Additionally, the responses of the NDVI to climate and topography in deserts were positively correlated with RH, AP, and ELE. Meanwhile, the dependence of NDVI on climate and topography decreased with increasing RH. Under conditions of escalating AMT and AP, RH and ELE independently contributed to explaining the NDVI. However, RH may be the key determinant of long-term NDVI stabilization in arid grassland. These findings underscore the significance of vegetation–climate–topography feedback and can inform the development of more comprehensive and effective climate mitigation and adaptation strategies.

Spatiotemporal change in vegetation cover in the Yellow River Basin between 2000 and 2022 and driving forces analysis

In this paper, we investigate the features of spatiotemporal change in fractional vegetation cover (FVC) throughout the Yellow River Basin between 2000 and 2022 and identify the driving factors behind the change using the MODIS normalized difference vegetation index (NDVI) as a data source. On that basis, our research involves trend analysis and the center migration model to examine the correlation between vegetation cover changes and various factors, such as climates, topographies, soils, and human activities. In this way, we aim to uncover how such correlation contributed to the reduction in suspended sediment concentration (SSC) in surface runoff. Results suggest that (1) the FVC of the Yellow River Basin has been trending upward over the past 23 years, and vegetation growth has been remarkably improved; (2) the center of medium-high and denser vegetation cover has progressively migrated from the upper reaches of the Yellow River down to its middle-lower segments; (3) soil erosion control measures are critical to improve vegetation cover, given the great impact of the shifting natural elements on vegetation cover changes on a local basis; (4) an improvement in vegetation cover leads to considerable positive change in the runoff and SSC of the Yellow River mainstem. This study has yielded noteworthy contributions in correctly understanding the current vegetation changes and related factors in the Yellow River Basin. Furthermore, it has laid the groundwork for further research in improving the accuracy of basic data and deepening the correlation between factors.

PHENOLOGICAL ANALYSIS OF THE WESTERN HIMALAYAN FOREST USING TEMPORAL REMOTE SENSING DATA

Abstract. The Himalayan range is considered to include the highest mountains on earth, with a widely recognized assortment of flora and fauna. The Indian Himalayan range spread over 10 states, of which being Uttarakhand, has the largest forest cover of 48.5% of the total area forest. In Doon Valley, the phenological behavior and variations of several forest types have been studied using the Normalized Difference Vegetation Index (NDVI) and Temporal Normalized Phenology Index (TNPI) along with elevation, surface temperature, slope, and aspect data into consideration. A two-scale method has been employed to study forest phenology, using Sentinel-2 data at 10 m spatial resolution for local-scale studies and MODIS NDVI data at 250 m spatial resolution to find large-scale phenological patterns. The framework used in this study is based on Google Earth Engine (GEE) which has potential applications at various spatial and temporal scales. Multiple phenological phases and phenological metrics have been identified and examined within the duration from December 2018 to May 2023. The investigation concluded that phenological behaviors were considerably affected by environmental and topographic variables such as elevation, surface temperature, slope, and aspect. Significant changes in phenology were recorded at low altitudes; however, less fluctuation was reported at medium to higher elevations due to remoteness at greater elevations. Using NDVI from open-source MODIS and Sentinel-2 datasets, TNPI has been successfully tested for this forest region. The findings showed that this study opens new opportunities for trend analysis of forest health and productivity.

Water availability and atmospheric dryness controls on spaceborne sun-induced chlorophyll fluorescence yield

Climate change is amplifying the duration, frequency, and intensity of droughts, harming global ecosystems. During droughts, plants can close their stomata to save water, at the expense of a reduced carboxylation rate. When in a carboxylation-limited regime, plants benefit from an increase in water availability, as it increases their photosynthetic rate. The sun-induced chlorophyll fluorescence (SIF) signal, measurable from satellites, is mechanistically linked to this rate. Like canopy photosynthesis, SIF carries an imprint from the available irradiation (PAR) as well as the canopy structure and the efficiency of the photosynthesis at the photosystem level. Normalizing the global TROPOMI SIF observations with TROPOMI reflectance and MODIS Normalised Difference Vegetation Index (NDVI) data, we extracted the fluorescence quantum yield (ϕF), which lab-scale experiments have found to be linked to the photosynthetic electron transport. Plant physiologists have long proved the photosynthetic electron transport to be sensitive to plant water status. Here, the plant water status is controlled by the soil moisture (SM) and the vapour pressure deficit (VPD). Combining data from the TROPOMI, AIRS and SMAP satellite sensors, this study describes how SM and VPD control the ϕF at the global scale. We identify a VPD range (VPD<1.5 kPa) in which the ϕF is mainly controlled by VPD, and another (VPD>1.5 kPa) in which the ϕF is co-regulated by SM and VPD. The precise values of this range, as well as the magnitude of ϕF values, are modulated by the plant isohydricity. To gain a deeper understanding of the link between ϕF and photosynthetic efficiency at large scale, we used the link between ϕF and data on the canopy conductance (Gs), which were calculated using remote sensing data-driven models. A comparison found that the ϕF-Gs relationship at large scale is in line with the ϕF-Gs relationship described in plant-level studies.

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

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

A spatio-temporal fusion strategy for improving the estimation accuracy of the aboveground biomass in grassland based on GF-1 and MODIS

The accurate estimation of the aboveground biomass (AGB) is crucial for the sustainable utilization and management of grassland resources. Spatio-temporal inconsistencies between field samples and satellite images are major source of error in the estimation of grassland AGB. To solve this problem, this study selected the Three Rivers Headwater Region as the study area and proposed a selection strategy for base-period images of spatio-temporal fusion that was suitable for use at a large spatial scale in which cloud-free images are difficult to obtain. The spatial and temporal adaptive reflectance fusion model (STARFM) based on the selection strategy was used to generate a synthetic Normalized Difference Vegetation Index (NDVI) dataset with high spatial-temporal resolution by using the maximum value composite of GF-1 NDVI and MODIS NDVI to enhance the spatial-temporal quality of the images for field-scale application. Three estimation models for grassland AGB were then constructed by the random forest algorithm using synthetic NDVI, MODIS NDVI, GF-1 NDVI respectively, together with ancillary data. Following this, the estimation model with the highest accuracy was used to generate a 16-m eight-day time-series AGB in the growing season. The results showed: (1) The synthetic NDVI was correlated closely with the observed GF-1 NDVI, with an average R of 0.825 and a RMSE of 0.087. The temporal trend of the synthetic NDVI for each grassland type was highly consistent with that of the MODIS NDVI in the growing season with a correlation higher than 0.9. (2) The synthetic NDVI reduced the spatial difference between field samples and images to 16-fold, and the temporal difference was controlled to within four days under ideal conditions. (3) The synthetic NDVI improved the estimation accuracy of grassland AGB by about 15.9% and 19.7% (R2), and 13.7% and 17.5% (RMSE) relative to MODIS NDVI and GF-1 NDVI, respectively. (4) The time-series AGB revealed accurately the spatial distribution of and seasonal temporal variations in the grassland biomass. The results of this study may serve as scientific guidance for timely monitoring of grassland conditions and precise management of grassland resources in China.

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.

Response of grassland growing season length to extreme climatic events on the Qinghai-Tibetan Plateau

Extreme Climatic Events (ECEs) are increasing in intensity, frequency, and duration as the earth warms, which greatly affects the vegetation phenology. However, the response of vegetation phenology to different types of ECEs (e.g., extreme hot, extreme cold, extreme drought, and extreme wet) has not been extensively studied. To fill this knowledge gap, we investigated the relationship between the length of growing season (LOS) of grassland and ECEs on the Qinghai-Tibetan Plateau (QTP). First, we analyzed the spatial distribution and interannual trends of phenology based on the MODIS Normalized Difference Vegetation Index (NDVI). Second, we used Coincidence Rate (CR) analysis to quantify the relationship between LOS anomalies and ECEs. Finally, we analyzed the sensitivity of LOS to the intensity of ECEs. The results indicated that the spatial distribution of LOS was closely related to local hydrothermal conditions, with longer LOS in places with more precipitation or higher temperatures during the growing season, and LOS extended by 0.28 days/year from 2000 to 2022. Moreover, we found that the CR of negative LOS anomalies to ECEs notably exhibited variations along climatic gradients, with higher CR to extreme hot generally occurring in warmer areas. Meanwhile, the CR of extreme wet increased while the CR of extreme drought decreased with increasing precipitation. We also found that the sensitivity of LOS to ECEs changed more markedly, along the climatic gradients, in alpine ecoregions compared to temperate ecoregions. Overall, the sensitivities of LOS ranked in descending order of absolute sensitivity to extreme drought, extreme wet, extreme hot, and extreme cold. This study furthers our understanding of the grassland response to ECEs under different hydrothermal conditions, which can provide valuable reference for the management and conservation of grassland ecosystems in QTP under future climate change scenarios.

The impacts of armed conflict on vegetation cover degradation in Tigray, northern Ethiopia

Efforts made to restore the degraded landscape of the Tigray region, Northern Ethiopia, over the last three decades have been relatively successful. However, an armed conflict that broke out in the region in November 2020 has significantly destroyed the restored vegetation, either directly associated with conflict (environment, pollution, fire) or indirectly (agricultural abandonment). This study aimed at assessing spatio-temporal changes in vegetation cover in a 50 km radius zone centered on Mekelle city, Tigray. Vegetation cover dynamics was evaluated using Landsat Enhanced Thematic Mapper Plus (ETM+) and Operational Land Imager (OLI) datasets for the years 2000, 2020, and 2022 and analysed using ENVI 5.3 and ArcGIS 10.8.1 software. These data were analysed using the Modified Normalized Difference Vegetation Index (MNDVI), Optimized Soil Adjusted Vegetation Index (OSAVI), and Moisture Adjusted Vegetation Index (MAVI). Based on the MNDVI, results show that vegetation cover increased in the period 2000–2020 by 179 km2 or 2% of the area, whereas in the period 2020–2022, there was a decrease in vegetation cover by 403 km2 or 5% of the area. This was accompanied by a decrease in vegetation density. These vegetation changes in 2020–2022 are attributed to the impact of armed conflict on the land surface which can include farmlands and village abandonment, spread of weeds and scrub vegetation, or failure to harvest crops. Monitoring vegetation change using Landsat data can help understand the environmental impacts of armed conflict in rural agricultural landscapes, including potential food security risks.

Analysis of spatial and temporal variation of vegetation NPP in Daning River Basin and its driving forces

ABSTRACT The Daning River Basin is a typical representative of the ‘mountain forest’ in the Three Gorges Reservoir (TGR) area of the Yangtze River. In recent years, with the completion of the Three Gorges Project, the local vegetation has degraded, soil erosion has become severe, and there is an urgent need to assess the environmental quality. Data were fused using the MODIS Normalized Difference Vegetation Index (NDVI) (250 m) and Landsat NDVI (30 m) through an Enhanced Spatial and Temporal Adaptive Reflectance Fusion Model (ESTARFM) to obtain ESTARFM NDVI (30 m). This data was then entered into the Carnegie Ames Stanford Approach (CASA) model, along with meteorological and land use data, to calculate vegetation net primary productivity (NPP) and trends in the Daning River Basin. The detection of drivers with a high impact on vegetation NPP was done using a Geodetector. The results show that: (1) In terms of spatial and temporal changes, the annual average NPP of the watershed during the 13 years from 2008 to 2020 generally showed an upward trend, with the average yearly vegetation NPP being 512.33gC·m−2·a−1, exhibiting a low southwest to surrounding increasing trend along the river. (2) The spatial and temporal variation of vegetation NPP is influenced by several factors synergistically, with elevation, temperature, and distance from settlements being the dominant factors. The interaction of these two factors can enhance the explanatory power of vegetation NPP. Through the estimation of vegetation NPP and the analysis of influencing factors in the Daning River Basin, this study can provide a reference for the ecological restoration and management of vegetation NPP in small watersheds under similar environments.

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

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

Effects of hydrothermal factors and human activities on the vegetation coverage of the Qinghai-Tibet Plateau

A systematic understanding of the spatio-temporal changes and driving factors in the Qinghai-Tibet Plateau holds significant scientific reference value for the future of ecological sustainable development. This paper utilizes MODIS normalized difference vegetation index (NDVI) and meteorological data to investigate the spatio-temporal changes and driving factors of vegetation coverage in the Qinghai-Tibet Plateau from 2001 to 2020. Methods employed include the dimidiate pixel model, trend analysis, partial correlation analysis, and residual analysis. The results demonstrate a generally fluctuating upward trend in vegetation coverage across the Tibetan Plateau over the past two decades, with spatial expansion occurring from northwest to southeast. Vegetation coverage exhibits a positive correlation with climate factors. Approximately 60.7% of the area showed a positive correlation between vegetation fractional cover (FVC) and precipitation, with 8.66% of the area demonstrating extremely significant (p < 0.05) and significant (p < 0.01) positive correlation. Human activities, on the whole, have contributed to the enhancement of vegetation cover in the Qinghai-Tibet Plateau. The areas where human activities have positively impacted vegetation cover are primarily situated in north-central Qinghai and north of Ngari, while areas experiencing degradation include certain grassland regions in central-eastern Yushu, Nagqu, and Lhasa.

Land surface phenology and its response to climate change in the Guangdong-Hong Kong-Macao Greater Bay Area during 2001–2020

Land surface phenology (LSP), an important branch of vegetation phenology, which is sensitive to climate change and play important role in ecosystem. As a typical subtropical area, in spite of its complex vegetation structure, the Guangdong-Hong Kong-Macao Greater Bay Area (GBA) LSP was not focused fully. Hence, In this study, a long time series LSP data from 2001 to 2020 was obtained based on MODIS normalized difference vegetation index (NDVI) using classical Savitzky-Golay filter. Subsequently, phenology spatial pattern and spatiotemporal trends and their correlation with climate change were analyzed for different vegetation types in the Guangdong-Hong Kong-Macao Greater Bay Area. The results revealed an obvious spatial heterogeneity and significant annual variation trend in vegetation phenology. The start of growing season (SOS) and the end of growing season (EOS) appeared delayed trend and the length of growing season (LOS) showed a prolonged trend, with average slope values of 0.55, 0.52 and 0.81 days per year, respectively. In general, preseason precipitation and solar radiation are the main climatic factors regulating vegetation phenology in the Greater Bay Area, and the enhancement of preseason precipitation and solar radiation is the main reason for the delay of vegetation withering and yellowing period and the extension of growing season. Ecosystems of the Greater Bay Area were mainly depending on preseason precipitation and solar radiation to regulate the leaf senescence and control plant growth duration. The present study can enhance performance of vegetation phenology models and provide useful information on ecological protection in subtropical areas.

Reconstruction of a Monthly 1 km NDVI Time Series Product in China Using Random Forest Methodology

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.

Monitoring vegetation dynamics (2010–2020) in Shengnongjia Forestry District with cloud-removed MODIS NDVI series by a spatio-temporal reconstruction method

Shengnongjia Forestry District is the national natural reserve with abundant biological resources in China. Monitoring its variations of vegetation for the intimate connection with eco-environmental changes is of great significance. In this paper, the 16-day composite MODIS normalized difference vegetation index (NDVI) products (MOD13A1) with 500 m resolution from 2011 to 2020 were selected to investigate the vegetation dynamics in Shengnongjia Forestry District. To alleviate the cloud contamination in NDVI products, a spatio-temporal prefill method with harmonic analysis of time series (ST-HANTS) is proposed. ST-HANTS first prefills the raw NDVI time series using spatio-temporal information to reduce data gaps, which effectively improves the reconstruction performance of the subsequent HANTS algorithm. In the simulation experiments, ST-HANTS shows the highest average correlation coefficient and the lowest average root mean square error compared with other mainstream methods, including HANTS, Savitzky–Golay filter, wavelet transform, and data assimilation. The reconstruction curves are close to the upper envelope of the NDVI time series, which is more consistent with the vegetation phenology and can effectively capture the key points in the growth cycle. By analyzing the cloud-free NDVI time series reconstructed with ST-HANTS, results reveal the overall trend of Shennongjia vegetation coverage is high in the middle while low at the edge. Except for the population centers and Hongping Airport, the NDVI of most areas is above 0.7 and shows a remarkable increasing tendency. Moreover, the fluctuation degree of NDVI in the whole study area is low, indicating that the ecological environment of Shennongjia is relatively stable. The vegetation variations are influenced by land surface temperature and precipitation, and the vegetation growth response to precipitation exhibits an apparent hysteresis.

Using Enhanced Gap-Filling and Whittaker Smoothing to Reconstruct High Spatiotemporal Resolution NDVI Time Series Based on Landsat 8, Sentinel-2, and MODIS Imagery

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.

Spatial Prediction and Mapping of Soil Water Content by TPE-GBDT Model in Chinese Coastal Delta Farmland with Sentinel-2 Remote Sensing Data

Soil water content is an important indicator used to maintain the ecological balance of farmland. The efficient spatial prediction of soil water content is crucial for ensuring crop growth and food production. To this end, 104 farmland soil samples were collected in the Yellow River Delta (YRD) in China, and the soil water content was determined using the drying method. A gradient boosting decision tree (GBDT) model based on a tree-structured Parzen estimator (TPE) hyperparametric optimization was developed, and then the soil water content was predicted and mapped based on the soil texture and vegetation index from Sentinel-2 remote sensing images. The results of statistical analysis showed that the soil water content had a high coefficient of variation (55.30%), a non-normal distribution, and complex spatial variability. Compared with other models, the TPE-GBDT model had the highest prediction accuracy (RMSE = 6.02% and R2 = 0.71), and its mapping results showed that the areas with high soil water content were distributed on both sides of the river and near the estuary. Furthermore, the results of Shapley additive explanation (SHAP) analysis showed that the soil texture (PC2 and PC5), modified normalized difference vegetation index (MNDVI), and Sentinel-2 red edge position (S2REP) index provided important contributions to the spatial prediction of soil water content. We found that the hydraulic physical properties of soil texture and the vegetation characteristics (such as vegetation coverage, root action, and transpiration) are the key factors affecting the spatial migration and heterogeneity of the soil water content in the study area. The above results show that the TPE algorithm can quickly capture the hyperparameters that are most suitable for the GBDT model, so that the GBDT model can ensure prediction accuracy, reduce the loss function with less training data, and accurately learn of the nonlinear relationship between soil water content and environmental factors. This paper proposes a machine learning method for hyperparameter optimization that shows considerable potential to predict the spatial heterogeneity of soil water content, which can effectively support regional farmland soil and water conservation and high-quality agricultural development.