Search Results (160)

In precision agriculture research, it is important to monitor maize growth conditions in real time for effective field diagnosis, management, and accurate yield prediction. This study aimed to develop an inverse model of vegetation index and maize yield using Unmanned Aerial Vehicle (UAV) multispectral imagery. The objective was to investigate how the vegetation index, maize growth stages, and growth parameters respond to plastic film mulching on the Loess Plateau. Annual field trials (2019–2020) employed a factorial design to evaluate mulch and nitrogen regimes. The results show that vegetation index long-time series curves, combined with maize phenological growth stages, can be used to monitor maize growth and yield estimation (R² > 0.9). The 13 vegetation indices (VIs) obtained by UAVs had a good regression relationship with the leaf area index, with the enhanced vegetation index 2 (EVI2) performing the best. The VIs obtained by UAVs at different stages of growth and development predicted yields, finding that EVI2 performed best with an R² of 0.92 and an RMSE of 0.52 t ha^-1 when maize entered the heading stage in 2019. The regression effect of VIs and yield based on maize without plastic film mulching management entering the heading stage was the best in 2020, with an R2 of 0.94 and an RMSE of 0.44 t ha⁻¹. When maize enters the heading stage, the best simulation results can be obtained by using the VIs to establish a yield prediction model. Spectral signatures during reproductive transition (VT-R1) proved most indicative of the final yield. Convergence of UAV-based spectral phenotyping with crop developmental physiology enables high-resolution growth diagnostics, providing empirical support for precision farming adaptations. Full article

Accurately extracting the phenology of maize, one of the three major staple crops, is crucial for assessing regional suitability under climate change, optimizing field management, predicting yield fluctuations, and ensuring food security. This study compares and validates the accuracy of various vegetation indices, including the normalized difference vegetation index (NDVI), the enhanced vegetation index (EVI), solar-induced chlorophyll fluorescence (SIF), and kernel NDVI (kNDVI), in extracting the phenological phases of summer maize at the sixth leaf (V6), tasseling (VT), and maturity (R6). Additionally, explainable machine learning methods were employed to elucidate how climate and stress factors influence the phenological sequences of summer maize. The results show that compared to NDVI and EVI, SIF and kNDVI are more suitable for extracting the summer maize phenological phase. SIF achieved the highest phenological extraction precision at the V6 and R6 phases, with root mean square errors (RMSEs) of 7.86 and 8.22 days, respectively. kNDVI provided the highest extraction accuracy for the VT phase, with an RMSE of 5 days. SHapley Additive exPlanations (SHAP) analysis revealed that temperature and radiation are the primary meteorological factors influencing maize phenology in the study area. Regarding stress factors, drought and heat stress delayed phenology at the V6 and VT phases, while heat stress prior to maturity accelerated summer maize maturation. In conclusion, this study reveals the potential of emerging vegetation indices for extracting maize phenology, offering both data and theoretical support for regional crop adaptability assessments. Full article

Forest aboveground biomass (AGB) serves as a crucial quantitative indicator that reflects the carbon sequestration capacity of forests, and accurately mapping AGB is pivotal for assessing forest ecosystem stability. However, mapping AGB in subtropical evergreen broadleaf forests in southern China presents challenges due to their complex canopy structure, stand heterogeneity, and spectral signal saturation. The phenological features reflecting seasonal vegetation dynamics are conducive to over-coming these challenges. By analyzing differential spectral reflectance patterns during the non-growing (Jan–Mar, Nov–Dec) versus growing (Apr–Oct) seasons, this study established a phenological feature-based methodology for improving AGB estimation in subtropical evergreen broadleaf forests. Subsequently, four time series vegetation indices (VI), namely NDVI, EVI2, NDPI, and IRECI were employed to extract phenological features (PFs) for mapping forest AGB using a multiple linear regression model (MLR), K-nearest neighbor model (KNN), support vector machine model (SVM), and random forest model (RF). The results demonstrated significant differences in Sentinel-2 spectral reflectance (740–1610 nm bands) between the growing and non-growing seasons. The PFs demonstrated the highest distance correlation coefficient (0.57), significantly outperforming other baseline feature types (0.44). Furthermore, seasonal changes in NDVI and NDPI were found to better reflect AGB accumulation in evergreen broadleaf forests compared to EVI2 and IRECI. Incorporating diverse PFs derived from all four VI significantly enhanced the accuracy of AGB mapping by yielding rRMSE values ranging from 21.01% to 25.06% and R² values ranging from 0.40 to 0.58. The results inferred that PFs can be considered a key factor for alleviating spectral signal saturation problems while effectively improving the accuracy of AGB estimation. Full article

Cropland suitability calculations quantify natural suitability according to abiotic conditions, thus making them crucial for sustainable land management. However, since ground-truth yield data are extremely scarce, there is a need to improve knowledge on the optimal proxy metric from satellite imagery, which represents cropland suitability and enables global applicability. This study evaluated four frequently used vegetation indices from Sentinel-2 image time-series (normalized difference vegetation index, enhanced vegetation index, enhanced vegetation index 2, and wide dynamic range vegetation index) with three phenology metrics for correlation analysis with maize and soybean yield. Four years (2019–2022) in two study areas (Iowa and Illinois) were utilized in this research, and 1000 ground-truth crop yield samples were created for each combination of study year and area. The combination of wide dynamic range vegetation index (WDRVI) and maximum vegetation index phenology metric (MAX) was an optimal proxy for maize yield prediction, while enhanced vegetation index 2 (EVI2) and MAX produced the highest correlation for soybean, producing Pearson’s correlation coefficient means of 0.506 and 0.519, respectively. This study improved our knowledge of the optimal proxy metric for cropland suitability by combining multiple large ground-truth crop yield datasets with 30 m spatial resolution satellite imagery, which can be further improved with the use of novel vegetation indices with improved resistance to a saturation effect. Full article

In the context of global warming, which is mainly due to the increasing atmospheric concentration of carbon dioxide, the prediction of climate change requires a good assessment of the involvement of vegetation in the global carbon cycle. In particular, determining when vegetative activity ceases in deciduous forests remains a great challenge. Remote sensing of solar-induced fluorescence (SIF) has been considered as a potential proxy for ecosystem photosynthesis and, therefore, a relevant indicator of the end of the vegetation period as compared to other vegetation indices, such as EVI (Enhanced Vegetation Index) and NDVI (Normalized Difference Vegetation Index). However, many challenges remain to be addressed due to the lack of knowledge of the response of SIF at different time scales, different species and different environmental conditions. The aim of this study was to explore the diurnal and seasonal variations in the SIFA and SIFB signals in a pubescent oak forest undergoing senescence. We show that apparent SIFA yield may be considered an earlier indicator of the end of the vegetation period compared to NDVI, which primarily reflects the ratio of SIFB/SIFA. The apparent SIFA yield signal was positively and highly correlated with PRI (Photochemical Reflectance Index), EVI and NDVI. Air contents in CO₂ and O₃ were similarly significantly correlated to SIFs emission but only during the growth phase of the phenology of Q. pubescens. At the seasonal scale, the results show that SIF variations were mainly driven by variations in PAR, air VPD and temperature. A higher dependence of the SIF signal on these last three variables was observed at the diurnal scale through Pearson correlation coefficients, which were greater than seasonal ones. Full article

Remote sensing technologies are essential for monitoring crop development and improving agricultural management. This study investigates the automation of Sentinel-2 satellite data processing to enhance wheat growth monitoring and provide actionable insights for smallholder farmers. The objectives include (i) analyzing vegetation indices across phenological stages to refine crop growth monitoring and (ii) developing a cost-effective user-friendly web application for automated Sentinel-2 data processing. The methodology introduces the “Area Under the Curve” (AUC) of vegetation indices as an independent variable for yield forecasting. Among the indices examined (NDVI, EVI, GNDVI, LAI, and a newly developed RE-PAP), GNDVI and LAI emerged as the most reliable predictors of wheat yield. The findings highlight the importance of the Tillering to the Grain Filling stage in predictive modeling. The developed web application, integrating Python with Google Earth Engine, enables real-time automated crop monitoring, optimizing resource allocation, and supporting precision agriculture. While the approach demonstrates strong predictive capabilities, further research is needed to improve its generalizability. Expanding the dataset across diverse regions and incorporating machine learning and Natural Language Processing (NLP) could enhance automation, usability, and predictive accuracy. Full article

Frequent droughts pose a severe threat to the ecological health and sustainable development of the Loess Plateau (LP). The accurate assessment of the impact of drought on vegetation is crucial for diagnosing ecological health. Traditional drought assessment methods often rely on coarse estimations based on averages of vegetation drought indices, overlooking the spatial differentiation of complex vegetation phenology. This study proposes a vegetative drought assessment method that considers vegetation phenological characteristics using MODIS EVI and LST data products. First, the start and end of the growing season timepoints were extracted from the Enhanced Vegetation Index (EVI) using Savitzky–Golay (S–G) filtering and the dynamic threshold method, determining the growing-time window for each pixel. Next, the Vegetation Health Index (VHI) series was calculated and extracted for each pixel within the growing season. The mean value of the VHI series was then used to construct the Growing Season Health Index (GSHI). Based on the GSHI, the long-term vegetation drought characteristics at LP were revealed. Finally, we integrated the Optimal Parameters-based Geographical Detector (OPGD) to identify and quantify the multiple driving forces of vegetation drought. The results showed that: (1) the spatio-temporal difference of vegetation phenology on the LP was significant, exhibiting distinct zonal characteristics; (2) the spatial distribution of growing season drought on the LP presented a “humid southeast, arid northwest” pattern, with the early 21st century being a period of high drought occurrence; (3) drought has been alleviated in large-scale natural areas, but the local drought effect under urbanization is intensifying; and (4) meteorology and topography influence vegetation drought by regulating water redistribution, while the drought effect of human activities is intensifying. Full article

Spring phenology (start of growing season, SOS) in boreal forests plays a crucial role in the global carbon cycle. At present, more and more researchers are using solar-induced chlorophyll fluorescence (SIF) to evaluate the land surface phenology of boreal forests, but few studies have utilized the primary SIF directly detected by satellites (e.g., GOME-2 SIF) to estimate phenology, and most SIF datasets used are high-resolution products (e.g., GOSIF and CSIF) constructed by models with vegetation indices (VIs) and meteorological data. Thus, the difference and consistency between them in detecting the seasonal dynamics of boreal forests remain unclear. In this study, a comparison of spring phenology from GOME-2 SIF, GOSIF, EVI2 (MCD12Q2), and FLUX tower sites, PEP725 phenology observation sites, was conducted. Compared with GOSIF and EVI2, the primary GOME-2 SIF indicated a slightly earlier spring phenology onset date (about 5 days earlier on average) in boreal forests, at a regional scale; however, SOSs and SOS-climate relationships from GOME-2 SIF, GOSIF, and EVI2 showed significant correlations with the ground observations at a site scale. Regarding the absolute values of spring phenology onset date, GOME-2 SIF and FLUX-GPP had an average difference of 8 days, while GOSIF and EVI2 differed from FLUX-GPP by 16 days and 12 days, respectively. GOME-2 SIF and PEP725 had an average difference of 38 days, while GOSIF and EVI2 differed from PEP725 by 24 days and 23 days, respectively. This demonstrated the complementary roles of the three remote sensing datasets when studying spring phenology and its relationship with climate in boreal forests, enriching the available remote sensing data sources for phenological research. Full article

Data assimilation can be used to predict crop yield by coupling remote sensing information with the crop growth model, but it often grapples with the challenge of enhancing the computational efficiency for the integrated model. To address this issue, particularly in regional-scale studies, simulation zone partitioning can offer a viable solution to improve computational efficiency. In this study, we first extracted high-resolution rice planting areas in Jiangsu Province (JP), then conducted simulation zone partitioning in JP based on the fuzzy c-means clustering algorithm (FCM) combined with soil data, meteorological indices, and EVI. Finally, the hierarchical assimilation system was developed by using phenology and leaf area index (LAI) as state variables to predict rice yield in JP. The results showed that the coefficient of variation (CV) of the small subregion after simulation zone partitioning obtained by using FCM was less than the overall CV of each subregion at different period. Compared with a single assimilation system that only used LAI as the state variable (R² was between 0.33 and 0.35, NRMSE was between 9.08 and 10.94%), the predicted yield of the hierarchical assimilation system (R² was between 0.44 and 0.51, NRMSE was between 7.23 and 8.44%) was in better agreement with the statistic yield. The research findings can provide technical support for the prediction of rice yield at the regional scale. Full article

Monitoring autumn vegetation dynamics in alpine regions is crucial for managing local livestock, understanding regional productivity, and assessing the responses of alpine regions to climate change. However, remote sensing-based vegetation monitoring is significantly affected by snowfall. The impact of autumn snowfall, particularly when vegetation has not fully entered dormancy, has been largely overlooked. To demonstrate the uncertainties caused by autumn snowfall in remote sensing-based vegetation monitoring, we analyzed 16 short-term snowfall events in the Qinghai–Tibet Plateau. We employed a synthetic difference-in-differences estimation framework and conducted simulated experiments to isolate the impact of snowfall from other factors, revealing its effects on vegetation indices (VIs) and autumn phenology estimation. Our findings indicate that autumn snowfall notably affects commonly used VIs and their associated phenology estimates. Modified VIs (i.e., Normalized Difference Infrared Index (NDII), Phenology Index (PI), Normalized Difference Phenology Index (NDPI), and Normalized Difference Greenness Index (NDGI)) revealed greater resilience to snowfall compared to conventional VIs (i.e., Normalized Difference Vegetation Index (NDVI) and Enhanced Vegetation Index (EVI)) in phenology estimation. Areas with remaining green vegetation in autumn showed more pronounced numerical changes in VIs due to snowfall. Furthermore, the impact of autumn snowfall closely correlated with underlying vegetation types. Forested areas experienced less impact from snowfall compared to grass- and shrub-dominated regions. Earlier snowfall onset and increased snowfall frequency further exacerbated deviations in estimated phenology caused by snowfall. This study highlights the significant impact of autumn snowfall on remote sensing-based vegetation monitoring and provides a scientific basis for accurate vegetation studies in high-altitude regions. Full article

The advent of precision agriculture has highlighted the necessity for the careful determination of crop phenology at increasingly smaller scales. Although remote sensing technology is extensively employed for the monitoring of crop growth, the acquisition of high-precision phenological data continues to present a significant challenge. This study, conducted in Youyi County, Shuangyashan City, Heilongjiang Province, China, employed time-series spectral index data derived from Sentinel-2 remote sensing images to investigate methodologies for the extraction of pivotal phenological phases during the primary growth stages of maize. The data were subjected to Savitzky–Golay (S-G) filtering and cubic spline interpolation in order to denoise and smooth them. The combination of dynamic thresholding with slope characteristic node recognition enabled the successful extraction of the jointing and tasseling stages of maize. Furthermore, a comparison of the extraction of phenophases based on the time-series curves of the NDVI, EVI, GNDVI, OSAVI, and MSR was conducted. The results showed that maize exhibited different sensitivities to the spectral indices during the jointing and tasseling stages: the OSAVI demonstrated the highest accuracy for the jointing stage, with a mean absolute error of 3.91 days, representing a 24.8% improvement over the commonly used NDVI. For the tasseling stage, the MSR was the most accurate, achieving an absolute error of 4.87 days, with an 8.6% improvement compared to the NDVI. In this study, further analysis was conducted based on maize cultivation data from Youyi County (2021–2023). The results showed that the maize phenology in Youyi County in 2021 was more advanced compared to 2022 and 2023, primarily due to the higher average temperatures in 2021. This study provides valuable support for the development of precision agriculture and maize phenology monitoring and also provides a useful data reference for future agricultural management. Full article

With global climate change, linking vegetation phenology with net ecosystem productivity (NEP) is crucial for assessing vegetation carbon storage capacity and predicting terrestrial ecosystem changes. However, there have been few studies investigating the relationship between vegetation phenology and NEP in the middle and high latitudes of the Northern Hemisphere. This study comprehensively analyzed vegetation phenological changes and their climate drivers using satellite data. It also investigated the spatial distribution and climate drivers of NEP and further analyzed the sensitivity of NEP to vegetation phenology. The results indicated that the average land surface phenology (LSP) was dominated by a monotonic trend in the study area. LSP derived from different satellite products and retrieval methods exhibited relatively consistent responses to climate. The average SOS and POS for different retrieval methods showed a higher negative correlation with nighttime temperatures compared to daytime temperatures. The average EOS exhibited a higher negative correlation with daytime temperatures than a positive correlation. The correlations between VPD and the average SOS, POS, and EOS showed that the proportion of negative correlations was higher than that of positive correlations. The average annual NEP ranged from 0 to 1000 gC·m⁻². The cumulative trends of NEP were mainly monotonically increasing, accounting for 61.04%, followed by monotonically decreasing trends, which accounted for 17.95%. In high-latitude regions, the proportion of positive correlation between VPD and NEP was predominant, while the proportion of negative correlation was predominant in middle-latitude regions. The positive and negative correlations between soil moisture and NEP (48.08% vs. 51.92%) were basically consistent in the study area. The correlation between SOS and POS with NEP was predominantly negative. The correlation between EOS and NEP was overall characterized by a greater proportion of negative correlations than positive correlations. The correlation between LOS and NEP exhibited a positive relationship in most areas. The sensitivity of NEP to vegetation phenological parameters (SOS, POS, and EOS) was negative, while the sensitivity of NEP to LOS was positive (0.75 gC·m⁻²/d for EVI vs. 0.63 gC·m⁻²/d for LAI vs. 0.30 gC·m⁻²/d for SIF). This study provides new insights and a theoretical basis for exploring the relationship between vegetation phenology and NEP under global climate change. Full article

The increased intensity and frequency of extreme climate events (ECEs) have significantly impacted vegetation phenology, further profoundly affecting the structure and functioning of terrestrial ecosystems. However, the mechanisms by which ECEs affect the end of the growing season (EOS), a crucial phenological phase, remain unclear. In this study, we first evaluated the temporal variations in the EOS anomalies in Northern China (NC) based on the Normalized Difference Vegetation Index (NDVI) and Enhanced Vegetation Index (EVI) from 2001 to 2018. We then used event coincidence analysis (ECA) to assess the susceptibility of EOS to four ECEs (i.e., extreme heat, extreme cold, extreme wet and extreme dry events). Finally, we examined the dependence of the response of EOS to ECEs on background climate conditions. Our results indicated a slight decrease in the proportion of areas experiencing extreme heat and dry events (1.10% and 0.66% per year, respectively) and a slight increase in the proportion of areas experiencing extreme wet events (0.77% per year) during the preseason period. Additionally, EOS exhibited a delaying trend at a rate of 0.25 days/a during the study period. The susceptibility of EOS to ECEs was closely related to local hydrothermal conditions, with higher susceptibility to extreme dry and extreme hot events in drier and warmer areas and higher susceptibility to extreme cold and extreme wet events in wetter regions. Grasslands, in contrast to forests, were more sensitive to extreme dry, hot and cold events due to their weaker resistance to water deficits and cold stress. This study sheds light on how phenology responds to ECEs across various ecosystems and hydrothermal conditions. Our results could also provide a valuable guide for ecosystem management in arid regions. Full article

With few measurement sites and a great need to validate satellite data to characterize the exchange of energy and carbon fluxes in tropical forest areas, quantified by the Net Ecosystem Exchange (NEE) and associated with phenological measurements, there is an increasing need for studies aimed at characterizing the Amazonian environment in its biosphere–atmosphere interaction, considering the accelerated deforestation in recent years. Using data from a flux measurement tower in the Caxiuanã-PA forest (2005–2008), climatic data, CO₂ exchange estimated by eddy covariance, as well as Gross Primary Productivity (GPP) data and satellite vegetation indices (from MODIS), this work aimed to describe the site’s energy, climatic and carbon cycle flux patterns, correlating its gross primary productivity with satellite vegetation indices. The results found were: (1) marked seasonality of climatic variables and energy flows, with evapotranspiration and air temperature on the site following the annual march of solar radiation and precipitation; (2) energy fluxes in phase and dependent on available energy; (3) the site as a carbon sink (−569.7 ± 444.9 gC m⁻² year⁻¹), with intensity varying according to the site’s annual water availability; (4) low correlation between productivity data and vegetation indices, corroborating data in the literature on these variables in this type of ecosystem. The results show the importance of preserving this type of environment for the mitigation of global warming and the need to improve satellite estimates for this region. NDVI and EVI patterns follow radiative availability, as does LAI, but without direct capture related to GPP data, which correlates better with satellite data only in the months with the highest LAI. The results show the significant difference at a point measurement to a satellite interpolation, presenting how important preserving any type of environment is, even related to its size, for the global climate balance, and also the need to improve satellite estimates for smaller areas. Full article

Flooding is a frequent environmental stress that reduces soybean growth and grain yield in many producing areas in the world, such as the United States, Southeast Asia, and Southern Brazil. In these regions, soybean is frequently cultivated in lowland areas in crop rotation with rice, which provides numerous technical, economic, and environmental benefits. In this context, the identification of the most important spectral variables for the selection of more flooding-tolerant soybean genotypes is a primary demand within plant phenomics, with faster and more reliable results enabled using multispectral sensors mounted on unmanned aerial vehicles (UAVs). Accordingly, this research aimed to identify the optimal UAV-based multispectral vegetation indices for characterizing the response of soybean genotypes subjected to flooding and to test the best linear model fit in predicting tolerance scores, relative maturity group, biomass, and grain yield based on phenomics analysis. Forty-eight soybean cultivars were sown in two environments (flooded and non-flooded). Ground evaluations and UAV-image acquisition were conducted at 13, 38, and 69 days after flooding and at grain harvest, corresponding to the phenological stages V8, R1, R3, and R8, respectively. Data were subjected to variance component analysis and genetic parameters were estimated, with stepwise regression applied for each agronomic variable of interest. Our results showed that vegetation indices behave differently in their suitability for more tolerant genotype selection. Using this approach, phenomics analysis efficiently identified indices with high heritability, accuracy, and genetic variation (>80%), as observed for MSAVI, NDVI, OSAVI, SAVI, VEG, MGRVI, EVI2, NDRE, GRVI, BNDVI, and RGB index. Additionally, variables predicted based on estimated genetic data via phenomics had determination coefficients above 0.90, enabling the reduction in the number of important variables within the linear model. Full article