Dynamics of Nocturnal Evapotranspiration and Its Biophysical Controls over a Desert Shrubland of Northwest China

Knowledge about the dynamics and biophysical controlling mechanism of nocturnal evapotranspiration (ETN) in desert-dwelling shrub ecosystem is still lacking. Using the eddy covariance measurements of latent heat flux in a dried shrubland in northwest China, we examined the dynamics of ETN and its biophysical controls at multiple timescales during growing-seasons from 2012 to 2014. The ETN was larger in the mid-growing season (usually in mid-summer) than in spring and autumn. The maximum daily ETN was 0.21, 0.17, and 0.14 mm night−1 in years 2012–2014, respectively. At the diel scale, ETN decreased from 21:00 to 5:00, then began to increase. ETN were mainly controlled by soil volumetric water content at 30 cm depth (VWC30), by vapor pressure deficit (VPD) and normalized difference vegetation index (NDVI) at leaf expanding and expanded stage, and by air temperature (Ta) and wind speed (Ws) at the leaf coloring stage. At the seasonal scale, variations of ETN were mainly driven by Ta, VPD, and VWC10. Averaged annual ETN was 4% of daytime ET. The summer drought in 2013 and the spring drought in 2014 caused the decline of daily evapotranspiration (ET). The present results demonstrated that ETN is a significant part of the water cycle and needs to be seriously considered in ET and related studies. The findings here can help with the sustainable management of water in desert ecosystems undergoing climate change.

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Variation of MODIS reflectance and vegetation indices with viewing geometry and soybean development

Anais da Academia Brasileira de Ciências ◽

10.1590/s0001-37652012005000018 ◽

2012 ◽

Vol 84 (2) ◽

pp. 263-274 ◽

Cited By ~ 3

Author(s):

Fábio M. Breunig ◽

Lênio S. Galvão ◽

Antônio R. Formaggio ◽

José C.N. Epiphanio

Keyword(s):

Vegetation Index ◽

Normalized Difference Vegetation Index ◽

Vegetation Indices ◽

Constant Factor ◽

The Other ◽

Large Field ◽

Phenological Stages ◽

Water Index ◽

Growing Seasons ◽

Moderate Resolution Imaging Spectroradiometer

Directional effects introduce a variability in reflectance and vegetation index determination, especially when large field-of-view sensors are used (e.g., Moderate Resolution Imaging Spectroradiometer - MODIS). In this study, we evaluated directional effects on MODIS reflectance and four vegetation indices (Normalized Difference Vegetation Index - NDVI; Enhanced Vegetation Index - EVI; Normalized Difference Water Index - NDWI1640 and NDWI2120) with the soybean development in two growing seasons (2004-2005 and 2005-2006). To keep the reproductive stage for a given cultivar as a constant factor while varying viewing geometry, pairs of images obtained in close dates and opposite view angles were analyzed. By using a non-parametric statistics with bootstrapping and by normalizing these indices for angular differences among viewing directions, their sensitivities to directional effects were studied. Results showed that the variation in MODIS reflectance between consecutive phenological stages was generally smaller than that resultant from viewing geometry for closed canopies. The contrary was observed for incomplete canopies. The reflectance of the first seven MODIS bands was higher in the backscattering. Except for the EVI, the other vegetation indices had larger values in the forward scattering direction. Directional effects decreased with canopy closure. The NDVI was lesser affected by directional effects than the other indices, presenting the smallest differences between viewing directions for fixed phenological stages.

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Assessing Snow Phenology and Its Environmental Driving Factors in Northeast China

Remote Sensing ◽

10.3390/rs14020262 ◽

2022 ◽

Vol 14 (2) ◽

pp. 262

Author(s):

Hui Guo ◽

Xiaoyan Wang ◽

Zecheng Guo ◽

Siyong Chen

Keyword(s):

Snow Cover ◽

Northeast China ◽

Vegetation Index ◽

Normalized Difference Vegetation Index ◽

Water Cycle ◽

Water Source ◽

Driving Factors ◽

Change Rate ◽

Mean Temperature ◽

Forested Areas

Snow cover is an important water source and even an Essential Climate Variable (ECV) as defined by the World Meteorological Organization (WMO). Assessing snow phenology and its driving factors in Northeast China will help with comprehensively understanding the role of snow cover in regional water cycle and climate change. This study presents spatiotemporal variations in snow phenology and the relative importance of potential drivers, including climate, geography, and the normalized difference vegetation index (NDVI), based on the MODIS snow products across Northeast China from 2001 to 2018. The results indicated that the snow cover days (SCD), snow cover onset dates (SCOD) and snow cover end dates (SCED) all showed obvious latitudinal distribution characteristics. As the latitude gradually increases, SCD becomes longer, SCOD advances and SCED delays. Overall, there is a growing tendency in SCD and a delayed trend in SCED across time. The variations in snow phenology were driven by mean temperature, followed by latitude, while precipitation, aspect and slope all had little effect on the SCD, SCOD and SCED. With decreasing temperature, the SCD and SCED showed upward trends. The mean temperature has negatively correlation with SCD and SCED and positively correlation with SCOD. With increasing latitude, the change rate of the SCD, SCOD and SCED in the whole Northeast China were 10.20 d/degree, −3.82 d/degree and 5.41 d/degree, respectively, and the change rate of snow phenology in forested areas was lower than that in nonforested areas. At the same latitude, the snow phenology for different underlying surfaces varied greatly. The correlations between the snow phenology and NDVI were mainly positive, but weak correlations accounted for a large proportion.

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Detecting Vegetation Recovery after Fire in A Fire-Frequented Habitat Using Normalized Difference Vegetation Index (NDVI)

Forests ◽

10.3390/f11070749 ◽

2020 ◽

Vol 11 (7) ◽

pp. 749

Author(s):

Danielle L. Lacouture ◽

Eben N. Broadbent ◽

Raelene M. Crandall

Keyword(s):

Satellite Imagery ◽

Vegetation Index ◽

Normalized Difference Vegetation Index ◽

Spatial Scales ◽

Vegetation Recovery ◽

Growing Seasons ◽

Different Seasons ◽

Dormant Season ◽

In Fire ◽

Subtropical Savanna

Research Highlights: Fire-frequented savannas are dominated by plant species that regrow quickly following fires that mainly burn through the understory. To detect post-fire vegetation recovery in these ecosystems, particularly during warm, rainy seasons, data are needed on a small, temporal scale. In the past, the measurement of vegetation regrowth in fire-frequented systems has been labor-intensive, but with the availability of daily satellite imagery, it should be possible to easily determine vegetation recovery on a small timescale using Normalized Difference Vegetation Index (NDVI) in ecosystems with a sparse overstory. Background and Objectives: We explore whether it is possible to use NDVI calculated from satellite imagery to detect time-to-vegetation recovery. Additionally, we determine the time-to-vegetation recovery after fires in different seasons. This represents one of very few studies that have used satellite imagery to examine vegetation recovery after fire in southeastern U.S.A. pine savannas. We test the efficacy of using this method by examining whether there are detectable differences between time-to-vegetation recovery in subtropical savannas burned during different seasons. Materials and Methods: NDVI was calculated from satellite imagery approximately monthly over two years in a subtropical savanna with units burned during dry, dormant and wet, growing seasons. Results: Despite the availability of daily satellite images, we were unable to precisely determine when vegetation recovered, because clouds frequently obscured our range of interest. We found that, in general, vegetation recovered in less time after fire during the wet, growing, as compared to dry, dormant, season, albeit there were some discrepancies in our results. Although these general patterns were clear, variation in fire heterogeneity and canopy type and cover skewed NDVI in some units. Conclusions: Although there are some challenges to using satellite-derived NDVI, the availability of satellite imagery continues to improve on both temporal and spatial scales, which should allow us to continue finding new and efficient ways to monitor and model forests in the future.

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Evaluation of vineyard growth under four irrigation regimes using vegetation and soil on-the-go sensors

SOIL ◽

10.5194/soil-1-459-2015 ◽

2015 ◽

Vol 1 (1) ◽

pp. 459-473 ◽

Cited By ~ 9

Author(s):

J. M. Terrón ◽

J. Blanco ◽

F. J. Moral ◽

L. A. Mancha ◽

D. Uriarte ◽

...

Keyword(s):

Precision Agriculture ◽

Vegetation Index ◽

Normalized Difference Vegetation Index ◽

Soil Depth ◽

Principal Component ◽

Temporal Analysis ◽

Vegetation Growth ◽

Growth Variability ◽

Growing Seasons ◽

Irrigation Treatments

Abstract. Precision agriculture is a useful tool to assess plant growth and development in vineyards. The present study focused on spatial and temporal analysis of vegetation growth variability, in four irrigation treatments with four replicates. The research was carried out in a vineyard located in the southwest of Spain during the 2012 and 2013 growing seasons. Two multispectral sensors mounted on an all-terrain vehicle (ATV) were used in the different growing seasons/stages in order to calculate the vineyard normalized difference vegetation index (NDVI). Soil apparent electrical conductivity (ECa) was also measured up to 0.8 m soil depth using an on-the-go geophysical sensor. All measured data were analysed by means of principal component analysis (PCA). The spatial and temporal NDVI and ECa variations showed relevant differences between irrigation treatments and climatological conditions.

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Variation in Temperature, Precipitation, and Vegetation Greenness Drive Changes in Seasonal Variation of Avian Diversity in an Urban Desert Landscape

Land ◽

10.3390/land10050480 ◽

2021 ◽

Vol 10 (5) ◽

pp. 480

Author(s):

Fábio Suzart de Albuquerque ◽

Heather L. Bateman ◽

Cameron Boehme ◽

Daniel C. Allen ◽

Luis Cayuela

Keyword(s):

Land Use ◽

Seasonal Variation ◽

Vegetation Index ◽

Normalized Difference Vegetation Index ◽

Effect Of Temperature ◽

Avian Diversity ◽

Environmental Predictors ◽

Desert Ecosystems ◽

Vegetation Greenness ◽

Avian Abundance

Previous studies in urban desert ecosystems have reported a decline in avian diversity. Herein, we expand and improve these studies by disentangling the effect of land-use and land-cover (LULC) types (desert, riparian desert, urban, riparian urban, agriculture), vegetation greenness (normalized difference vegetation index—NDVI), climate, and their interactions on avian seasonal variation abundance and richness. Avian community data were collected seasonally (winter and spring) from 2001 to 2016. We used generalized linear mixed models (GLMM) and multimodel inference to investigate how environmental predictors explain patterns of avian richness and abundance. Avian abundance and richness oscillated considerably among the years. GLMM indicated that LULC was the most important predictor of avian abundance and richness. Avian abundance was highest in urban riparian and urban LULC types, followed by agriculture. In contrast, avian richness was the highest in riparian environments (urban and desert), followed by agriculture, urban, and desert. NDVI was also strongly related to avian abundance and richness, whereas the effect of temperature and precipitation was moderate. The importance of environmental predictors is, however, dependent on LULC. The importance of LULC, vegetation cover, and climate in influencing the seasonal patterns of avian distribution highlights birds’ sensitivity to changes in land use and cover and temperature.

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Exploring the Use of Vegetation Indices for Validating Crop Transpiration Fluxes Computed with the MOHID-Land Model. Application to Vineyard

Agronomy ◽

10.3390/agronomy11061228 ◽

2021 ◽

Vol 11 (6) ◽

pp. 1228

Author(s):

Tiago B. Ramos ◽

Lucian Simionesei ◽

Ana R. Oliveira ◽

Ramiro Neves ◽

Hanaa Darouich

Keyword(s):

Remote Sensing ◽

Water Balance ◽

Soil Water ◽

Vegetation Index ◽

Normalized Difference Vegetation Index ◽

Vegetation Indices ◽

Remote Sensing Data ◽

Soil Water Balance ◽

Growing Seasons ◽

Crop Coefficients

The success of an irrigation decision support system (DSS) much depends on the reliability of the information provided to farmers. Remote sensing data can expectably help validate that information at the field scale. In this study, the MOHID-Land model, the core engine of the IrrigaSys DSS, was used to simulate the soil water balance in an irrigated vineyard located in southern Portugal during three growing seasons. Modeled actual basal crop coefficients and transpiration rates were then compared with the corresponding estimates derived from the normalized difference vegetation index (NDVI) computed from Sentinel-2 imagery. On one hand, the hydrological model was able to successfully estimate the soil water balance during the monitored seasons, exposing the need for improved irrigation schedules to minimize percolation losses. On the other hand, remote sensing products found correspondence with model outputs despite the conceptual differences between both approaches. With the necessary precautions, those products can be used to complement the information provided to farmers for irrigation of vine crop, further contributing to the regular validation of model estimates in the absence of field datasets.

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Green-gradient based canopy segmentation: A multipurpose image mining model with potential use in crop phenotyping and canopy studies

10.1101/241786 ◽

2018 ◽

Cited By ~ 2

Author(s):

Abbas Haghshenas ◽

Yahya Emam

Keyword(s):

Vegetation Index ◽

Normalized Difference Vegetation Index ◽

Canopy Temperature ◽

Image Mining ◽

Growing Seasons ◽

Gradient Based ◽

Semi Arid Region ◽

Mining Model ◽

Experimental Plots

AbstractEfficient quantification of the sophisticated shading patterns inside the 3D vegetation canopies may improve our understanding of canopy functions and status, which is possible now more than ever, thanks to the high-throughput phenotyping (HTP) platforms. In order to evaluate the option of quantitative characterization of shading patterns, a simple image mining technique named “green-gradient based canopy segmentation model (GSM)” was developed based on the relative variations in the level of RGB triplets under different illuminations. For this purpose, an archive of ground-based nadir images of heterogeneous wheat canopies (cultivar mixtures) was analyzed. The images were taken from experimental plots of a two-year field experiment conducted during 2014-15 and 2015-16 growing seasons in the semi-arid region of southern Iran. In GSM, the vegetation pixels were categorized into the maximum possible number of 255 groups based on their green levels. Subsequently, mean red and mean blue levels of each group were calculated and plotted against the green levels. It is evidenced that the yielded graph could be readily used for (i) identifying and characterizing canopies even as simple as one or two equation(s); (ii) classification of canopy pixels in accordance with the degree of exposure to sunlight; and (iii) accurately prediction of various quantitative properties of canopy including canopy coverage (CC), Normalized difference vegetation index (NDVI), canopy temperature, and also precise classification of experimental plots based on the qualitative characteristics such as subjecting to water and cold stresses, date of imaging, and time of irrigation. It seems that the introduced model may provide a multipurpose HTP platform and open new windows to canopy studies.

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Estimation of sugar beet biomass and yield comparing NDVI measurements and physical soil parameters

Sugar Industry ◽

10.36961/si26238 ◽

2021 ◽

pp. 100-109

Author(s):

Koç Mehmet Tuğrul

Keyword(s):

Sugar Beet ◽

Soil Sample ◽

Vegetation Index ◽

Normalized Difference Vegetation Index ◽

Soil Analysis ◽

Soil Parameters ◽

Growing Seasons ◽

Relationship Of ◽

The Relationship ◽

Moderate Positive Correlation

This study was conducted to estimate the relationship of soil sample analysis and satellite imagery with sugar beet yield (BY). The red NDVI obtained monthly from Landsat OLI satellite images during the 2017 and 2018 sugar beet growing seasons were used to establish relationships between imagery and georeferenced soil sample analyses and sugar beet harvest sites. The study was carried out in the field of Sugar Institute Ilgın Experiment Station, Turkey, in 2017 and 2018. Soil samples were obtained in a 0.4 ha grid, and sugar beet yield and recoverable sugar yield (RSY) were obtained from the same sampling areas. The results showed that there were relationships between some soil analysis factors and BY and beet quality. The overall results showed that the amount of clay, electric conductivity (EC), and organic matter in the field might be indicators of BY and beet quality. A statistically significant moderate positive correlation was also obtained between NDVI (Normalized Difference Vegetation Index) images and BY and RSY values in all images obtained by satellite near the harvest date.

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Capturing the Impact of the 2018 European Drought and Heat across Different Vegetation Types Using OCO-2 Solar-Induced Fluorescence

Remote Sensing ◽

10.3390/rs12193249 ◽

2020 ◽

Vol 12 (19) ◽

pp. 3249

Author(s):

Ankit Shekhar ◽

Jia Chen ◽

Shrutilipi Bhattacharjee ◽

Allan Buras ◽

Antony Oswaldo Castro ◽

...

Keyword(s):

Forest Fires ◽

Vegetation Index ◽

Normalized Difference Vegetation Index ◽

Coniferous Forest ◽

Mixed Forest ◽

Terrestrial Ecosystem ◽

Added Value ◽

Summer Drought ◽

Vegetation Types ◽

The Impact

The European heatwave of 2018 led to record-breaking temperatures and extremely dry conditions in many parts of the continent, resulting in widespread decrease in agricultural yield, early tree-leaf senescence, and increase in forest fires in Northern Europe. Our study aims to capture the impact of the 2018 European heatwave on the terrestrial ecosystem through the lens of a high-resolution solar-induced fluorescence (SIF) data acquired from the Orbiting Carbon Observatory-2 (OCO-2) satellite. SIF is proposed to be a direct proxy for gross primary productivity (GPP) and thus can be used to draw inferences about changes in photosynthetic activity in vegetation due to extreme events. We explore spatial and temporal SIF variation and anomaly in the spring and summer months across different vegetation types (agriculture, broadleaved forest, coniferous forest, and mixed forest) during the European heatwave of 2018 and compare it to non-drought conditions (most of Southern Europe). About one-third of Europe’s land area experienced a consecutive spring and summer drought in 2018. Comparing 2018 to mean conditions (i.e., those in 2015–2017), we found a change in the intra-spring season SIF dynamics for all vegetation types, with lower SIF during the start of spring, followed by an increase in fluorescence from mid-April. Summer, however, showed a significant decrease in SIF. Our results show that particularly agricultural areas were severely affected by the hotter drought of 2018. Furthermore, the intense heat wave in Central Europe showed about a 31% decrease in SIF values during July and August as compared to the mean over the previous three years. Furthermore, our MODIS (Moderate Resolution Imaging Spectroradiometer) and OCO-2 comparative results indicate that especially for coniferous and mixed forests, OCO-2 SIF has a quicker response and a possible higher sensitivity to drought in comparison to MODIS’s fPAR (fraction of absorbed photosynthetically active radiation) and the Normalized Difference Vegetation Index (NDVI) when considering shorter reference periods, which highlights the added value of remotely sensed solar-induced fluorescence for studying the impact of drought on vegetation.

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The Effect of Water Vapor Originating from Land on the 2018 Drought Development in Europe

10.20944/preprints202107.0450.v1 ◽

2021 ◽

Author(s):

Fares Al Hasan ◽

Andreas Link ◽

Ruud J. van der Ent

Keyword(s):

Water Vapor ◽

Land Surface ◽

Vegetation Index ◽

Normalized Difference Vegetation Index ◽

Relative Increase ◽

Minor Role ◽

Summer Drought ◽

Southeast Europe ◽

Time And Space ◽

A Minor

The 2018 summer drought in Europe was particularly extreme in terms of intensity and impact due to the combination of low rainfall and high temperatures. However, it remains unclear how this drought developed in time and space in such an extreme way. In this study we aimed to get a better understanding of the role of land-atmosphere interactions. More specifically, we investigated whether there was a change in water vapor originating from land, if that caused a reduction in rainfall, and by this mechanism possibly the propagation and intensification of the drought in Europe. Our first step was to use remote sensing products for soil moisture content (SMC) and the normalized difference vegetation index (NDVI) to see where the 2018 drought started and how it developed in time and space. Our SMC and NDVI analysis showed that the 2018 drought started to impact the soil and vegetation state in June in Scandinavia and the British Isles. After that it moved towards the West of Europe where it intensified in July and August. In September, it started to decay. In October, drought was observed in southeast Europe as well. Based on the observed patterns we divided Europe into six regions of similar spatiotemporal characteristics of SMC and NDVI. Then, we used a global gridded dataset of the fate of land evaporation (i.e., where it ends up as precipitation) to investigate whether the drought intensification and propagation was impacted by the reduction in water vapor transported from the regions that first experienced the drought. This impact was investigated by identifying the anomalies in the water vapor originating from land recycling, imports and exports within Europe during the spring, summer, and autumn seasons. From these regions we identified four drought regions and investigated the changes in water vapor originating from source regions on the development of drought in those regions. It was found that during the onset phase of the 2018 drought in Europe that the water vapor originating from land played an important role in mitigating the precipitation anomalies as, for example, the share of land evaporation contributing to precipitation increased from 27% (normal years) to 38% (2018) during July in West of Europe. Land evaporation played a minor role in amplifying it during the intensification phase of the drought as the share of land evaporation contribution to precipitation decreased from 23% (normal years) to 21% (2018) during August in West of Europe. These findings are somewhat in contrast to similar studies in other continents that found the land surface to play a strong amplifying role for drought development. Subsequently, we found that the relative increase in the amount of land water vapor originating from eastern half of Europe played a role in delaying the onset and accelerating the decay of the 2018 drought in West of Europe.

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