Seasonal Vegetation Dynamics Research Articles

Numerical modelling of biogeomorphological processes in salt marsh development: Do short-term vegetation dynamics influence long-term development?

Salt marshes are critical coastal ecosystems that provide numerous services. They are governed by complex biogemorphological interactions on multiple spatiotemporal scales. To simulate long-term salt marsh development in numerical models, smaller- and shorter-term vegetation processes are often neglected. This study investigates the importance of spatiotemporally and seasonally varying vegetation dynamics on decadal salt marsh development in an integrated numerical biogeomorphological model simulating a representative, idealized case of the Dutch Western Scheldt Estuary. The focus lies on the influence of seasonal vegetation dynamics, interspecific interactions between two salt marsh species, and the stabilizing effect of below-ground biomass with increasing vegetation age on critical shear stress, which results in spatially non-uniform erosion resistance of sediment and vegetation. By incorporating seasonal growth periods and varying characteristics of vegetation, we demonstrate the impact of species-specific traits on vegetation stability and morphological changes. Introducing an additional pioneer species that can establish further seaward reveals the sheltering effect and the long-term implications of this zonation for marsh development. Additionally, the influence of spatially and temporally varying below-ground biomass on sediment stability and vegetation's resistance to uprooting is significant for the biogeomorphological interactions. Comparison of the different model simulations to topographical data from the Dutch Western Scheldt Estuary shows that the model results are representing the morphological variability occurring in the field. Our findings highlight the complex biogeomorphological feedback that govern salt marsh development and show that models that neglect shorter-term seasonal and grid-scale (25 m2) spatially varying processes may overlook critical interactions that influence the lateral extent and overall morphological development of salt marshes. Therefore, this study underscores the need to integrate seasonal dynamics, species diversity, and below-ground biomass variability into biogeomorphological models to enhance predictive accuracy. This approach provides a more comprehensive understanding of the large-scale, long-term consequences of seasonal and spatiotemporally varying vegetation processes, which is essential for informing conservation and management strategies under changing climatic conditions.

Spatial and temporal variability of soil moisture active and passive (SMAP) droughts and their impacts on vegetation in the Central Highlands of Vietnam.

Drought is a reoccurring natural phenomenon that presents significant challenges to agricultural production, ecosystem stability, and water resource management. The Central Highlands of Vietnam, a major region of industrial crops and vegetation ecosystems, has become increasingly vulnerable to drought impacts. Despite this vulnerability, limited research has explored the specific characteristics of drought and its seasonal effects on vegetation ecosystems in the region. This study addressed these gaps by providing a detailed analysis of recent soil moisture drought characteristics and their seasonal impacts on vegetation from 2015 to 2023 using weekly soil moisture active passive (SMAP) and moderate resolution imaging spectroradiometer (MODIS) satellite time series observations. This analysis derived the soil moisture anomaly index as a proxy to assess drought characteristics and used correlation analysis to quantify their impacts on seasonal vegetation dynamics. Our spatial analysis identified the most significant drought years in 2015 and 2019 in the study region, while the wettest conditions were detected in 2017 and 2022 over the study period. Notably, significant soil moisture deficits were observed in August and October throughout the study period, even though these months typically fall within the rainy season. On average, nearly 25 drought events were detected in the region from 2015 to 2023 due to soil moisture deficits, each lasting approximately 6 weeks. The impact of drought events on the vegetation ecosystem was seasonally pronounced in spring and winter, where droughts were notably higher. Our results provide valuable insights into informed decision-making and sustainable agricultural practices in the region. Understanding the spatial and temporal patterns of drought and its seasonal effects on vegetation can help policymakers and farmers develop targeted strategies to mitigate the adverse impacts, enhance water management practices, and promote drought-resistant crop varieties, thereby maintaining agricultural productivity and ecosystem health amidst increasing climate variability.

Flash Drought and Its Characteristics in Northeastern South America during 2004–2022 Using Satellite-Based Products

The term flash drought describes a special category of drought with rapid onset and strong intensity over the course of days or weeks. To characterize the impact of flash droughts on vegetation coverage, this study assessed the influence of soil water deficits on vegetation dynamics in the northeastern South America region by combining time series of vegetation index, rainfall, and soil moisture based on satellite products at a daily time scale. An 18-year analysis, from 2004 to 2022, of the Normalized Difference Vegetation Index (NDVI), Standard Precipitation Index (SPI), and surface soil moisture (SSM) was performed based on three different satellite remote sensing estimates: the spinning enhanced visible and infrared imager (SEVIRI) and the integrated multi-satellite retrievals algorithm (IMERG), and the soil moisture and ocean salinity (SMOS). The results revealed that flash drought events exerted dramatic impacts on terrestrial ecosystems in the study region during the first two decades of the 2000s, with changes in seasonal and regional vegetation dynamics. Further, the fixed-threshold values to characterize flash drought events were suggested as the timing when the water deficit was less than −1.0 units and vegetation index reached the value equal to +0.3 during five consecutive weeks or more, coupled with soil moisture rates below 40% percentile, leading to a strong region-wide drought throughout the entire region. Additionally, the results of linear least squares trend analyses revealed a negative trend in the pentad-SEVIRI radiance for the solar channel 1 within the semiarid ecosystems of the study region (i.e., the Caatinga biome) that was suggested as a reduction in clouds in the 18 years of the study. Developing combined threshold measures of flash drought based on satellite remote sensing may lead to an accurate assessment of flash drought mitigation.

Detecting the Spatiotemporal Variation of Vegetation Phenology in Northeastern China Based on MODIS NDVI and Solar-Induced Chlorophyll Fluorescence Dataset

Vegetation phenology is a crucial biological indicator for monitoring changes in terrestrial ecosystems and global climate. Currently, there are limitations in using traditional vegetation indices for phenology monitoring (e.g., greenness saturation in high-density vegetation areas). Solar-induced chlorophyll fluorescence (SIF), a novel remote sensing product, has great potential in depicting seasonal vegetation dynamics across various regions with different vegetation covers and latitudes. In this study, based on the GOSIF and MODIS NDVI data from 2001 to 2020, we extracted vegetation phenological parameters in Northeastern China by using Double Logistic (D-L) fitting function and the dynamic threshold method. Then, we analyzed the discrepancy in phenological period and temporal trend derived from SIF and NDVI data at multiple spatiotemporal scales. Furthermore, we explored the response of vegetation phenology to climate change and the persistence of phenological trends (Hurst exponent) in Northeastern China. Generally, there is a significant difference in trends between SIF and NDVI, but with similar spatial patterns of phenology. However, the dates of key phenological parameters are distinct based on SIF and MODIS NDVI data. Specifically, the start of season (SOS) of SIF started later (about 10 days), and the end of season (EOS) ended earlier (about 36 days on average). In contrast, the fall attenuation of SIF showed a lag process compared to NDVI. This implies that the actual period of photosynthesis, that is, length of season (LOS), was shorter (by 46 days on average) than the greenness index. The position of peak (POP) is almost the same between them. The great difference in results from SIF and NDVI products indicated that the vegetation indexes seem to overestimate the time of vegetation photosynthesis in Northeastern China. The Hurst exponent identified that the future trend of SOS, EOS, and POP is dominated by weak inverse sustainability, indicating that the future trend may be opposite to the past. The future trend of LOSSIF and LOSNDVI are opposite; the former is dominated by weak inverse sustainability, and the latter is mainly weak positive sustainability. In addition, we speculate that the difference between SIF and NDVI phenology is closely related to their different responses to climate. The vegetation phenology estimated by SIF is mainly controlled by temperature, while NDVI is mainly controlled by precipitation and relative humidity. Different phenological periods based on SIF and NDVI showed inconsistent responses to pre-season climate. This may be the cause of the difference in the phenology of SIF and NDVI extraction. Our results imply that canopy structure-based vegetation indices overestimate the photosynthetic cycle, and the SIF product can better track the phenological changes. We conclude that the two data products provide a reference for monitoring the phenology of photosynthesis and vegetation greenness, and the results also have a certain significance for the response of plants to climate change.

Land surface phenology indicators retrieved across diverse ecosystems using a modified threshold algorithm

Land surface phenology (LSP), the study of the seasonal vegetation dynamics from remote sensing imagery, provides crucial information for plant monitoring and reflects the responses of ecosystems to climate change. The Moderate Resolution Imaging Spectroradiometer (MODIS) phenology product (MCD12Q2) provides global LSP information, but it has large spatial gaps in many regions, especially in ecosystems where rainfall influences phenology more than temperature. This study aimed to improve spatial coverage of LSP retrieval in these ecosystems. To do so, we used a regionally modified threshold algorithm for LSP retrievals, which were tested over continental Australia as it includes diverse landscapes of arid, mesic, and forest environments. We generated LSP metrics annually from 2003 to 2018 using satellite Enhanced Vegetation Index (EVI) time series at 500 m resolution, including the start, peak, end, and length of growing seasons, the minimum EVI value prior to and after the peak date, the seasonal maximum EVI value, the integral EVI value during the growing season (an approximation of productivity), and seasonal amplitude (maximum EVI value minus minimum EVI). Our regionally optimised algorithm improved the spatial coverage of LSP information in Australia from only 26 % of the continent to 70 % averaged across 16 years. Our results showed that the growing season amplitude was low (EVI < 0.1) over arid/semi-arid shrublands and savannas, tropical and subtropical savannas, and temperate evergreen forests, whose LSP metrics were captured by our regional algorithm and not by the global product. Some ecosystems, such as arid/semi-arid shrublands and savannas, showed more irregular phenology with low seasonal dynamics, and the growing seasons could skip a year or occur more than once in a year depending on climate conditions. Our algorithm was more sensitive to ecosystems with low seasonal amplitudes. We found that the detectability of LSP increases as the growing season amplitude increases, regardless of vegetation cover. Evaluation of the LSP metrics using eddy covariance flux tower measurements of gross primary productivity (GPP) demonstrated the reliability and accuracy of the algorithm. These improved LSP retrievals provide a greater understanding of the vegetation phenology across diverse ecosystems, especially savanna, shrubland, and evergreen forest ecosystems that cover more than 30 % of the land globally. The LSP provides essential information for ecological and agricultural studies such as quantifying bushfire fuel accumulation and forest carbon cycling, whilst enhancing our capacity for quantifying ecological responses to climate change.

Seasonal Dynamics of Vegetation in the Wetlands of the Lower Dniester NNP Based on Remote Data from the Landsat-8 Satellite

Over the season of 2020, the vegetation dynamics of the wetlands in the Lower Dniester National Nature Park were examined using the indices &lt;i&gt;NDVI &lt;/i&gt;(Normalized Difference Vegetation Index) and &lt;i&gt;VCI&lt;/i&gt; (Vegetation Condition Index) on the basis of Landsat-8 satellite remote sensing data. The results demonstrated that distribution of values throughout the vegetation season did not correspond to normal distribution, indicating variety of their habitat conditions. The vegetation development in spring was negatively affected by the pyrogenic factor, which formed some areas without vegetation and with poorly developed vegetation. Sharp increase in &lt;i&gt;NDVI&lt;/i&gt; values in early summer occurred due to development of wetland ecosystems' edifier &lt;i&gt;Phragmites australis&lt;/i&gt;, whose stems emerged from the pickle stage and developed a leaf lamina. In this period, the well-developed vegetation prevailed, its total area exceeded 97&amp;#37;. In autumn, the areas of the developed vegetation gradually decrease, whereas areas of the poorly developed increased. The results of the research showed effectiveness of remote sensing of wetland areas using &lt;i&gt;NDVI &lt;/i&gt;and &lt;i&gt;VCI&lt;/i&gt; to assess vegetation state; it also can be used for the purposes of the conservation, restoration and sustainable use of wetland ecosystems of the north-west Black Sea coastal area under increasing anthropogenic pressure and global climate change.

Interaction between wind speed and net radiation controls reference evapotranspiration variance in the inland river basin of Northwest China

AbstractExploring the variance in reference evapotranspiration (ET0) and its dominant influencing factors is important for climate change, hydrological cycles and water management. Temperature (T), wind speed (U2), net radiation (Rn) and actual vapour pressure (ea) are the major climatic input variables in Penman–Monteith equation. Previous studies have successfully applied the total differential method to determine the relative contributions of changes in these variables to variation in ET0 on different timescales. However, the interaction of climatic variables has not been incorporated into this method. Taking the inland river basin of Northwest China as the study area, we extended the total differential method to decompose the ET0 variance into temporal variance and covariance of T, U2, Rn and ea on intra‐annual and annual scales during 1960–2017. The results indicated that the variance in ET0 on the intra‐annual scale was larger than that on the annual scale. Among the four single climatic variables, U2 variance made larger contributions to intra‐annual and annual ET0 variance with relative contributions of 6.8% and 1.1%, respectively. However, the interaction of climatic variables played a dominant role in the variation in ET0. Specifically, on intra‐annual scale, coupled U2 and Rn primarily controlled the ET0 variance (76.1%), followed by coupled Rn and ea (9.6%); but their positive effects were weakened by the negative effects of coupled T and ea (−2.3%). On annual scale, coupled U2 and Rn still governed the variance in ET0 (97.3%), but the effects were also weakened by other groups of interaction effects. Furthermore, ET0 was highly related to NDVI on an intra‐annual scale (R2 = 0.68, p = 0.00), indicating the strong effect of seasonal vegetation dynamics on ET0 variance. This study provides new insights to quantitatively assess the interaction effects of environmental factors on key hydrometeorological variables.

Influence of seasonal vegetation dynamics on hydrological connectivity in tropical drylands

AbstractSeasonally dry forests in tropical regions show over 300% inter‐annual biomass variability that directly affects the runoff and erosion dynamics. However, biomass fluctuation is mostly overlooked in hydrosedimentological analysis, including in connectivity analysis. The aim of this paper is to understand how the dryland vegetation seasonality in Brazilian drylands affects the potential runoff and sediment connectivity using the Index of Connectivity (stream and outlet targets). Two main analytical steps were used to identify the influence of dry forest biomass fluctuation on connectivity: Creation of vegetation scenarios based on the relationship between rainfall patterns and NDVI fluctuations (Landsat images); Identification of the effect of the vegetation scenarios on Index of Connectivity. The method was applied to a 90 km2 watershed in NE Brazil, creating a daily vegetation classification using five vegetation scenarios related to rainfall parameters, with average NDVI values from 0.18 during very dry scenarios (&lt;20 mm of antecedent rainfall) to 0.62 in very wet scenario (&gt;500 mm of antecedent rainfall). The primary connectivity behaviour is controlled by a continuous connectivity decrease, reaching 32%, related to increase of humidity and vegetation biomass. At the same time, due to rainfall irregularity, high magnitude rainfall events can occur even during very dry scenarios, when the watershed shows very high potential connectivity. It indicates that connectivity in runoff‐dominated regions is temporally variable due to the highly seasonal vegetation and variable incidence of intense rainstorms.

Seasonal Dynamics of Tropical Forest Vegetation in Ngoc Linh Nature Reserve, Vietnam Based on UAV Data

Seasonal dynamics in tropical forests are closely related to the variation in forest canopy gaps. The canopy gaps change continuously in shape and size between the rainy and dry seasons, leading to the variation in the vegetative indicators. To monitor the variation of the canopy gaps, UAVs were used to collect datas in the mentioned tropical forests at an altitude of over 1,000m in Ngoc Linh Nature Reserve, Vietnam with a post-processing image resolution of about 8cm, which allows the detection of relatively small gaps. The analysis results at 10 squares of 1 ha showed a decrease in the area of ​​ canopy gaps from the rainy season in September 2019 to the dry season of May 2020. The mixed broad-leaved or broadleaf forest dominates with a greater variation, when the area of ​​the gaps decreases significantly. The variation in forest canopy gaps and vegetative indicators are closely related to the high differentiation of terrain, the seasonal and the dry season climatic characteristics. The fluctuation of the vegetation cover affects the habitats of the species under the forest canopy such as animals, birds and soil fauna. This is one of the scientific bases that contributes to the management and conservation of flora and fauna biodiversity, especially in mountainous tropical forests such as Ngoc Linh Nature Reserve.

Predicting defoliator abundance and defoliation measurements using Landsat‐based condition scores

AbstractRemote sensing imagery can provide critical information on the magnitude and extent of damage caused by forest pests and pathogens. However, monitoring short‐term changes in deciduous forest condition caused by defoliating insects is challenging and requires approaches that directly account for seasonal vegetation dynamics. We implemented a previously published harmonic modeling approach for forest condition monitoring in Google Earth Engine and systematically assessed the relative ability of condition change products generated using various model parameterizations for predicting pest abundances and defoliation during the 2016–2018 gypsy moth (Lymantria dispar) outbreak in southern New England. Our comparisons revealed that most models made reasonable predictions of changes in canopy condition and egg and larval abundances of L. dispar, indicating a strong correlation between our harmonic‐based estimates of condition change and defoliator activity. The greatest differences in predictive ability were in the spectral domain, with assessments based on Tasseled Cap Greenness, Simple Ratio, and the Enhanced Vegetation Index ranking among the top models, and the commonly used Normalized Difference Vegetation Index consistently exhibiting poorer performance. We also observed notable differences in the magnitude of scores for different baseline periods. Additionally, we found that Landsat‐based condition scores better explained larval abundance than egg mass counts, which have historically been used as a proxy for later‐season larval abundance, indicating that our remote sensing approach may be more accurate and cost‐effective for generating consistent retrospective assessments of L. dispar population abundance in addition to estimates of canopy damage. These findings provide important linkages between spectral changes detected using a harmonic modeling approach and biophysical aspects of defoliator activity, with potential to extend monitoring and prediction to regional or even continental scales.

Classification and Observed Seasonal Phenology of Broadleaf Deciduous Forests in a Tropical Region by Using Multitemporal Sentinel-1A and Landsat 8 Data

Broadleaf deciduous forests (BDFs) or dry dipterocarp forests play an important role in biodiversity conservation in tropical regions. Observations and classification of forest phenology provide valuable inputs for ecosystem models regarding its responses to climate change to assist forest management. Remotely sensed observations are often used to derive the parameters corresponding to seasonal vegetation dynamics. Data acquired from the Sentinel-1A satellite holds a great potential to improve forest type classification at a medium-large scale. This article presents an integrated object-based classification method by using Sentinel-1A and Landsat 8 OLI data acquired during different phenological periods (rainy and dry seasons). The deciduous forest and nondeciduous forest areas are classified by using NDVI (normalized difference vegetation index) from Landsat 8 cloud-free composite images taken during dry (from February to April) and rainy (from June to October) seasons. Shorea siamensis Miq. (S. siamensis), Shorea obtusa Wall. ex Blume (S. obtusa), and Dipterocarpus tuberculatus Roxb. (D. tuberculatus) in the deciduous forest area are classified based on the correlation between phenology of BDFs in Yok Don National Park and backscatter values of time-series Sentinel-1A imagery in deciduous forest areas. One hundred and five plots were selected during the field survey in the study area, consisting of dominant deciduous species, tree height, and canopy diameter. Thirty-nine plots were used for training to decide the broadleaf deciduous forest areas of the classified BDFs by the proposed method, and the other sixty-six plots were used for validation. Our proposed approach used the changes of backscatter in multitemporal SAR images to implement BDF classification mapping with acceptable accuracy. The overall accuracy of classification is about 79%, with a kappa coefficient of 0.7. Accurate classification and mapping of the BDFs using the proposed method can help authorities implement forest management in the future.

Spatial and transient modelling of land use/land cover (LULC) dynamics in a Sahelian landscape under semi-arid climate in northern Burkina Faso

Sahelian landscapes have gone through rapid changes over the past decades under high pressure from an ever-growing population, climate hazards and land degradation. Understanding drivers of change and forecasting LULC dynamics in time and space are critical for sustainable land management. This research aimed at analyzing LULC patterns and forecasting future changes in the landscape at the watershed level in semi-arid climate in northern Burkina Faso. Three LULC categories (natural vegetation, degradation soils, and cultivated areas) were considered. Seasonal vegetation dynamics were assessed through the monitoring of radiometric indices over the years 2015, 2016 and 2017. The post-harvest period (October-November) was found to be optimal for single-date LULC mapping. Furthermore, LULC maps for 1986, 1999, 2009 and 2017 were produced through a supervised classification of Landsat satellite images. For land change modelling and prediction, a Multilayer Perceptron (MLP) neural network was calibrated between 1999 and 2009 and validated against 2017 reference map, then used to simulate prediction maps for 2030 and 2050, in conjunction with population growth forecasts. Analysis of LULC changes revealed degradation soils were the major contributor to cultivated areas expansion between 1986 and 2017. Land use maps predicted for 2030 and 2050 indicated an expected increase in cultivated areas of 10.4 % and 22.7 % respectively. These results highlight the strength of the human-environment relationship in the watershed and call for careful planning and conservation actions for a sustainable balance between sustainable agricultural production and natural resources conservation.

Remote sensing strategies to characterization of drought, vegetation dynamics in relation to climate change from 1983 to 2016 in Tibet and Xinjiang Province, China.

Due to various land cover changes, vegetation dynamics, and climate, drought is the most complex climate-related disaster problem in Tibet and Xinjiang, China. The purpose of the present study is to analyze the performance of the AVHRR Normalized Vegetation Index (NDVI) and the temporal and spatial differences of seasonal vegetation dynamics by correlating the results with rainfall and temperature data of NASA's MERRA to examine the vegetation dynamics and droughts in Tibet and the Xinjiang Province of China. Our method is based on the use of AVHRR NDVI data and NASA MERRA temperature and precipitation during 1983-2016. Due to the dryness and low vegetation, NDVI is more useful to describe the drought conditions in Tibet and Xinjiang of China. The NDVI, TCI, VHI, NVSWI, VCI, TVDI, and NAP from April to October increased rapidly. While the NDVI, TCI, VHI, NVSWI, NAP, TVDI, and VCI are stable every month in September, again improve in October, and then confirm downward trend in December. The NDVI, TCI, VHI, NVSWI, NAP, VCI, and TVDI monthly values indicate that Tibet and Xinjiang province of China suffered from severe drought in 2006, 2008, and 2012 which were the most drought years. For monitoring drought in Tibet and Xinjiang province of China, the NDVI, TVDI, NAP, VCI, and NVSWI values were selected as a tool for reporting drought events during different growing seasons. Seasonal values of TVDI, NDVI, NAP, NVSWI, and VCI confirmed that Tibet and Xinjiang province of China suffered from severe drought in 2006, 2008, and 2012 and led the durations of severe drought. The correlation between NDVI, TCI, VHI, NAP, TVDI, and VCI showed a significantly positive correlation, while the significantly negative correlation between NVSWI and NDVI showed a good indication for the assessment of drought, especially for the agricultural regions of Tibet and Xinjiang province of China. This shows that the positive sign to support NAP, NVSWI, and TVDI is good monitoring of the drought indexes in Tibet and the Xinjiang province of China.

ОЦЕНКА СЕЗОННОЙ ДИНАМИКИ НОРМАЛИЗОВАННОГО ДИФФЕРЕНЦИАЛЬНОГО ИНДЕКСА РАСТИТЕЛЬНОСТИ (NDVI) И ЕГО ВЗАИМОСВЯЗЬ С КЛИМАТИЧЕСКИМИ ПРЕДИКТОРАМИ В ВОДНО-БОЛОТНЫХ УГОДЬЯХ TЕМБЛАДЕРA (ЭКВАДОР)

Wetlands are considered critical ecosystems due to declining quality of their ecosystems services. Nevertheless, there have not been any climate related researched devoted to vegetation condition and biomass amount. Thus, this study examines the seasonal dynamics of vegetation and its correlations with climatic factors. This study is important for understanding of the regulatory function of this ecosystem during climate change. Two Landsat OLI8 images made in 2020 were analyzed. One image refers to the rainy season (April 12), and the other to the dry season (August 2). The radiometric and atmospheric corrections of the images and the determination of the boundaries of the study site (ROI) were developed in ENVI 5.3 program. The normalized differential vegetation index (NDVI) was calculated with ENVI 5.3 program (histograms allowed to determine biomass), and with ArcGIS 10.3 (for classification index). The Pearson coefficient (r) and the Statistica software were applied to determine the correlations between the variables. The linear relationship between the NDVI, the amount of biomass and the climatic variables was identified. In the rainy season (April) with higher temperature and precipitation, the NDVI was &gt;0.5 and the biomass was 372613.0 t in the major part of “la Tembladera”, while in the dry season (August) with a lower temperature and precipitation rate, both the NDVI (0.14–0.5) and the biomass (333856.95 t) decreased in a considerable area of the wetland. Consequently, the seasonal dynamics of vegetation and its biomass is caused by fluctuations in these climatic variables. Thus, the biomass increased during the rainy season (higher precipitation, temperature, and humidity). These results can be used to further modelling the effects of climate change in these ecosystems.

Using a Discrete Scattering Model to Constrain Water Cloud Model for Simulating Ground-Based Scatterometer Measurements and Retrieving Soil Moisture

Potential of constraining semiempirical model with physically based scatter model simulations has long been recognized. This study contributes to this topic through the assessment of backscattering coefficient ( σ o) simulations and soil moisture retrieval using the water cloud model (WCM) constrained by a discrete scattering model (i.e., Tor Vergata) under both frozen and thawed soil conditions. The WCM is coupled with Oh (hereafter “WCM+Oh”) and Dubois (WCM+Dubois) surface scattering models, respectively. The soil permittivity is obtained using the four-phase dielectric mixing model. One year of C -band copolarized σ o observations are collected by a ground-based scatterometer deployed in the seasonally frozen Tibetan meadow ecosystem. It is found that: the calibrated Tor Vergata (hereafter “TVG”) model simulates well the seasonal dynamics and magnitudes of scatterometer measurements, and the simulated scattering components and vegetation transmissivity agree well with the seasonal vegetation dynamics; the total scattering simulated by the TVG constrained WCMs shows a good consistency with the scatterometer measurements, and the simulated soil and vegetation scattering components are in line with the TVG simulations; and the retrieved soil moisture based on the constrained WCMs captures well the seasonal variability noted in the in situ measurements. An additional experiment is performed to calibrate the WCMs directly, and the results show that the calibrated WCMs achieve comparable results with the calibrated TVG model and the constrained WCMs in terms of the total σ o and soil moisture retrieved. However, the direct calibration of the WCMs leads to unrealistic characterization of individual soil and vegetation scattering contributions, of which an underestimation of the vegetation contribution at VV polarization is most notable. These findings demonstrate that usage of a physically based scatter model to constrain semiempirical models leads to results that provide a more robust representation of reality, which is needed for developing worldwide soil moisture monitoring from active microwave remote sensing.

Revealing the Surge Phenomena Contribution of the Heavy Metals Inflow to the River Don Delta

Purpose. The aim of the study is to evaluate the surge phenomena effect on the heavy metals inflow to the Don Delta based on the archival and expedition data analysis, as well as using mathematical modeling. Methods and Results. To achieve the purpose, the Hydrologic Engineering Center River Analysis System (HEC-RAS) mathematical hydrodynamic model and the original model of the heavy metal compounds’ transfer and transformation in the Don Delta, developed by S. V. Berdnikov were applied. The models included the irregular grid for the Don Delta region with the average resolution 100 × 100 m. The grid cells were grouped into the compartments according to the hydrological principle. Twelve scenarios of dynamics of the suspended solids, and the dissolved and suspended forms of Ni, Cu, Pb and Cd were calculated for the surges of various intensity under the conditions of variable water content and seasonal dynamics of near-water vegetation. In accordance with the scenarios, the graphs showing the changes in the suspended matter content and accumulation, and the maps of the deposited substance distribution resulted from the surges in the delta were constructed. During two days the calculations for which include the surges of varying repeatability and the variable water content, about 0.3–3 t of nickel compounds, 0.1–1.8 t of copper compounds, 0.2–1.8 t of lead compounds and 0.01–0.04 t of cadmium ones deposit in Don. The obtained results made it possible to reveal two regions where the increased accumulation of the precipitated suspended matter and the heightened concentrations of the heavy metal dissolved forms were observed: the interfluve of the Don shipping channel, and the systems of the Kalancha and Kuterma river branches. Conclusions. As for their influence upon formation of the flow of the heavy metal suspended forms, the surge phenomena surpass the river flow. The suspended matter concentration in the Taganrog Bay waters during the surges is the governing factor for the heavy metals inflow to the Don Delta. At the same time, the regions characterized by the highest suspended solids sedimentation and the increased concentrations of the heavy metal dissolved forms are the closest to the Taganrog Bay areas covered by reed vegetation.

DynaGraM: A process-based model to simulate multi-species plant community dynamics in managed grasslands

Permanent grasslands host a high plant diversity, which sustains many ecosystem services. Thus, understanding how composition of the plant community responds to different management practices under given soil and climatic conditions is crucial for making best use of grasslands. modeling approaches may be used to explain the manifold interactions involved to sustain this diversity. We developed the dynamic, process-based ecological model DynaGraM to simulate the seasonal aboveground vegetation dynamics of semi-natural grasslands. The model allows specifying plant community by any number of species. The predictive power of the model was assessed by simulating the dynamics of a virtual mountain grassland in response to four typical management scenarios under constant climatic conditions over several decades. In our experiments, we modelled an assemblage of seven species representing contrasted plant functional types. We compared model outputs to compositions inferred from floristic records conducted in the French Jura Mountains. Irrespective of initial conditions, the simulated community converged to four distinct compositions that primarily reflected management. Overall, the results matched the functional composition inferred for each of the scenarios from the botanical records. Convergence in functional composition was reached in less than 15 years under grazing scenarios, but not less than 50 years under mowing scenarios. At quasi-equilibrium, the highest vegetation diversity was obtained for extensive grazing and the lowest for extensive mowing. Overall, this study introduces a novel and relatively simple approach to model competition and adaptation processes in plant community dynamics, thus providing a response to the key challenge of modeling multi-species grasslands.

Vyyavleniye vklada nagonnykh yavleniy v postupleniye tyazhelykh metallov v del'tu Dona

Purpose. The aim of the investigation is to evaluate the surge phenomena effect upon the heavy metals inflow to the Don Delta based on analysis of the archival and expedition data, as well as using mathematical modeling. Methods and Results. To achieve the purpose, the Hydrologic Engineering Center River Analysis System (HEC-RAS) mathematical hydrodynamic model and the original model of the heavy metal compounds’ transfer and transformation in the Don Delta, developed by S. V. Berdnikov were applied. The models included the irregular grid for the Don Delta region with the average resolution 100 × 100 m. The grid cells were grouped into the compartments according to the hydrological principle. Twelve scenarios of dynamics of the suspended solids, and the dissolved and suspended forms of Ni, Cu, Pb and Cd were calculated for the surges of various intensity under the conditions of variable water content and seasonal dynamics of near-water vegetation. In accordance with the scenarios, the graphs showing the changes in the suspended matter content and accumulation, and the maps of the deposited substance distribution resulted from the surges in the delta were constructed. During two days the calculations for which include the surges of varying repeatability and the variable water content, about 0.3–3 t of nickel compounds, 0.1–1.8 t of copper compounds, 0.2–1.8 t of lead compounds and 0.01–0.04 t of cadmium ones deposit in Don. The obtained results made it possible to reveal two regions where the increased accumulation of the precipitated suspended matter and the heightened concentrations of the heavy metal dissolved forms were observed: the interfluve of the Don shipping channel, and the systems of the rivers Kalancha and Kuterma branches. Conclusions. As for their influence upon formation of the flow of the heavy metal suspended forms, the surge phenomena surpass the river flow. The suspended matter concentration in the Taganrog Bay waters during the surges is the governing factor for the heavy metals inflow to the Don Delta. At the same time, the regions characterized by the highest suspended solids sedimentation and the increased concentrations of the heavy metal dissolved forms are the closest to the Taganrog Bay areas covered by reed vegetation.

Phenology of short vegetation cycles in a Kenyan rangeland from PlanetScope and Sentinel-2

The short revisit times afforded by recently-deployed optical satellite sensors that acquire 3–30 m resolution imagery provide new opportunities to study seasonal vegetation dynamics. Previous studies demonstrated a successful retrieval of phenology with Sentinel-2 for relatively stable annual growing seasons. In semi-arid East Africa however, vegetation responds rapidly to a concentration of rainfall over short periods and consequently is subject to strong interannual variability. Obtaining a sufficient density of cloud-free acquisitions to accurately describe these short vegetation cycles is therefore challenging. The objective of this study is to evaluate if data from two satellite constellations, i.e., PlanetScope (3 m resolution) and Sentinel-2 (10 m resolution), each independently allow for accurate mapping of vegetation phenology under these challenging conditions. The study area is a rangeland with bimodal seasonality located at the 128-km2 Kapiti Farm in Machakos County, Kenya. Using all the available PlanetScope and Sentinel-2 imagery between March 2017 and February 2019, we derived temporal NDVI profiles and fitted double hyperbolic tangent models (equivalent to commonly-used logistic functions), separately for the two rainy seasons locally referred to as the short and long rains. We estimated start- and end-of-season for the series using a 50% threshold between minimum and maximum levels of the modelled time series (SOS50/EOS50). We compared our estimates against those obtained from vegetation index series from two alternative sources, i.e. a) greenness chromatic coordinate (GCC) series obtained from digital repeat photography, and b) MODIS NDVI. We found that both PlanetScope and Sentinel-2 series resulted in acceptable retrievals of phenology (RMSD of ~8 days for SOS50 and ~15 days for EOS50 when compared against GCC series) suggesting that the sensors individually provide sufficient temporal detail. However, when applying the model to the entire study area, fewer spatial artefacts occurred in the PlanetScope results. This could be explained by the higher observation frequency of PlanetScope, which becomes critical during periods of persistent cloud cover. We further illustrated that PlanetScope series could differentiate the phenology of individual trees from grassland surroundings, whereby tree green-up was found to be both earlier and later than for grass, depending on location. The spatially-detailed phenology retrievals, as achieved in this study, are expected to help in better understanding climate and degradation impacts on rangeland vegetation, particularly for heterogeneous rangeland systems with large interannual variability in phenology and productivity.

Using integrated modelling to understand seasonal vegetation dynamics and its relationship to runoff generation

AbstractVegetation dynamics and hydrological processes are major components of terrestrial ecosystems, and they interact strongly with each other. Studies of hydrological responses to vegetation dynamics are usually conducted on a long‐term scale, whereas the hydrological responses within a single year have rarely been studied. In the present study, Poyang Lake runoff (PYL‐R) model, a new hydrological model coupled with leaf area index (LAI) remote sensing products, was established and applied to simulate the runoff process in the Poyang Lake Watershed. The simulation results obtained in three sub‐watersheds of the Poyang Lake Watershed (Ganjiang Watershed, Xinjiang Watershed, and Fuhe Watershed) agreed well with the observations (Nash efficiency coefficient values and R values exceeded 0.6 and 0.9, respectively). The PYL‐R experiment (PYL‐R‐E) model was designed as a contrast model without considering the impact of LAI. The simulated monthly runoff results obtained using the PYL‐R and PYL‐R‐E models were compared, and the within‐year changes in the differences between the two results were analysed to evaluate and quantify the impact of vegetation dynamic on runoff. From January to July, when LAI values increased by around 2.6 m2 m−2, monthly runoff depth differences between PYL‐R and PYL‐R‐E results increased by 35.25, 27.98, and 29.14 mm in the Ganjiang, Xinjiang, and Fuhe watersheds, respectively. Dense vegetation caused high interception and evapotranspiration during summer, which largely reduced runoff. By contrast, during winter, the effect of vegetation was weaker on runoff process whereas the impacts of other factors (e.g., precipitation) were higher. The sensitivity of monthly runoff to vegetation dynamics varied greatly throughout the whole year. In particular, during August and September, the LAI‐caused runoff changes were very high, accounting for 28–42% of monthly runoff in the sub‐watersheds. Our findings clarify the effects of changes in vegetation on hydrological processes over short time scales, thereby providing insights into the effects of scale on eco‐hydrological processes.