scholarly journals Vegetation greenness response to water availability in northern China from 1982 to 2006

2016 ◽  
Author(s):  
Zhang Fengtai ◽  
An Youzhi
Keyword(s):  
Soil Moisture ◽  
Water Availability ◽  
Vegetation Index ◽  
Normalized Difference Vegetation Index ◽  
Growing Season ◽  
Northern China ◽  
Entire Study ◽  
Vegetation Greenness ◽  
Spatio Temporal ◽  
Using Data

Abstract. An evaluation of the relationship between satellite-observed Normalized Difference Vegetation Index (NDVI) data as a proxy for vegetation greenness and water availability (rainfall and soil moisture) can greatly improve our understanding of how vegetation greenness responds to water availability fluctuations. Using Sen and Pearson’s correlation methods, we analyzed the spatio-temporal variation of vegetation greenness for both the entire year and the growing season (GS,4-10) in northern China from 1982 to 2006. Although, vegetation greenness and soil moisture during the study period changed significantly for the entire study area, there was no change in rainfall. Linear correlation analysis between NDVI and rainfall revealed higher correlations using data for all seasons. Higher correlations for NDVI and soil moisture were obtained using growing season data. This study highlights how strongly vegetation greenness responds to water availability dynamics, especially in the growing season period.

scholarly journals Vegetation greenness response to water availability in northern China from 1982 to 2006

Solid Earth ◽  
2016 ◽  
Vol 7 (3) ◽  
pp. 995-1002 ◽  
Author(s):  
Fengtai Zhang ◽  
Youzhi An
Keyword(s):  
Soil Moisture ◽  
Water Availability ◽  
Vegetation Index ◽  
Normalized Difference Vegetation Index ◽  
Growing Season ◽  
Northern China ◽  
Entire Study ◽  
Vegetation Greenness ◽  
Using Data ◽  
Response To Water

Abstract. Vegetation and moisture are two key factors of soil genesis and development. An evaluation of the relationship between satellite-observed normalized difference vegetation index (NDVI) data as a proxy for vegetation greenness and water availability (rainfall and soil moisture) can greatly improve our understanding of how vegetation greenness responds to water availability fluctuations. Using Sen and Pearson's correlation methods, we analyzed the spatiotemporal variation of vegetation greenness for both the entire year and the growing season (GS, 4–10) in northern China from 1982 to 2006. Although vegetation greenness and soil moisture during the study period changed significantly for the entire study area, there was no change in rainfall. Linear correlation analysis between NDVI and rainfall revealed higher correlations using data for all seasons. Higher correlations for NDVI and soil moisture were obtained using growing season data. This study highlights how strongly vegetation greenness responds to water availability dynamics, especially in the growing season period.

scholarly journals Does Earlier and Increased Spring Plant Growth Lead to Reduced Summer Soil Moisture and Plant Growth on Landscapes Typical of Tundra-Taiga Interface?

2019 ◽  
Vol 11 (17) ◽  
pp. 1989 ◽  
Author(s):  
Alemu Gonsamo ◽  
Michael T. Ter-Mikaelian ◽  
Jing M. Chen ◽  
Jiaxin Chen
Keyword(s):  
Soil Moisture ◽  
Plant Growth ◽  
High Latitude ◽  
Boreal Forests ◽  
Vegetation Index ◽  
Normalized Difference Vegetation Index ◽  
Root Zone ◽  
Terrestrial Ecosystems ◽  
Growing Season ◽  
Northern High Latitude

Over the past four decades, satellite observations have shown intensified global greening. At the same time, widespread browning and reversal of or stalled greening have been reported at high latitudes. One of the main reasons for this browning/lack of greening is thought to be warming-induced water stress, i.e., soil moisture depletion caused by earlier spring growth and increased summer evapotranspiration. To investigate these phenomena, we use MODIS collection 6, Global Inventory Modeling and Mapping Studies third-generation (GIMMS) normalized difference vegetation index (NDVI3g), and Global Land Evaporation Amsterdam Model (GLEAM) satellite-based root-zone soil moisture data. The study area was the Far North of Ontario (FNO), 453,788 km2 of heterogeneous landscape typical of the tundra-taiga interface, consisting of unmanaged boreal forests growing on mineral and peat soils, wetlands, and the most southerly area of tundra. The results indicate that the increased plant growth in spring leads to decreased summer growth. Lower summer soil moisture is related to increased spring plant growth in areas with lower soil moisture content. We also found that earlier start of growing season leads to decreased summer and peak season maximum plant growth. In conclusion, increased spring plant growth and earlier start of growing season deplete summer soil moisture and decrease the overall summer plant growth even in temperature-limited high latitude ecosystems. Our findings contribute to evolving understanding of changes in vegetation dynamics in relation to climate in northern high latitude terrestrial ecosystems.

Relationship of soil moisture with solar-induced chlorophyll fluorescence and normalized difference vegetation index in different phenological stages

2020 ◽  
Author(s):  
Qiu Shen ◽  
Jianjun Wu ◽  
Leizhen Liu ◽  
Wenhui Zhao
Keyword(s):  
Soil Moisture ◽  
Chlorophyll Fluorescence ◽  
Vegetation Index ◽  
Normalized Difference Vegetation Index ◽  
Growing Season ◽  
Maturity Stage ◽  
Phenological Stages ◽  
Active Radiation ◽  
Relationship Of ◽  
The Relationship

<p>As an important part of water cycle in terrestrial ecosystem, soil moisture (SM) provides essential raw materials for vegetation photosynthesis, and its changes can affect the photosynthesis process and further affect vegetation growth and development. Thus, SM is always used to detect vegetation water stress and agricultural drought. Solar-induced chlorophyll fluorescence (SIF) is signal with close ties to photosynthesis and the normalized difference vegetation index (NDVI) can reflect the photosynthetic characteristics and photosynthetic yield of vegetations. However, there are few studies looking at the sensitivity of SIF and NDVI to SM changes over the entire growing season that includes multiple phenological stages. By making use of GLDAS-2 SM products along with GOME-2 SIF products and MODIS NDVI products, we discussed the detailed differences in the relationship of SM with SIF and NDVI in different phenological stages for a case study of Northeast China in 2014. Our results show that SIF integrates information from the fraction of photosynthetically active radiation (fPAR), photosynthetically active radiation (PAR) and SIF<sub>yield</sub>, and is more effective than NDVI for monitoring the spatial extension and temporal dynamics of SM on a short time scale during the entire growing season. Especially, SIF<sub>PAR_norm</sub> is the most sensitive to SM changes for eliminating the effects of seasonal variations in PAR. The relationship of SM with SIF and NDVI varies for different vegetation cover types and phenological stages. SIF is more sensitive to SM changes of grasslands in the maturity stage and  rainfed croplands  in the senescence stage than NDVI, and it has significant sensitivities to SM changes of forests in different phenological stages. The sensitivity of SIF and NDVI to SM changes in the senescence stages stems from the fact that vegetation photosynthesis is relatively weaker at this time than that in the maturity stage, and vegetations in the reproductive growth stage still need much water. Relevant results are of great significance to further understand the application of SIF in SM detection.</p>

Feedbacks of Vegetation on Summertime Climate Variability over the North American Grasslands. Part I: Statistical Analysis

2006 ◽  
Vol 10 (17) ◽  
pp. 1-27 ◽  
Author(s):  
Weile Wang ◽  
Bruce T. Anderson ◽  
Nathan Phillips ◽  
Robert K. Kaufmann ◽  
Christopher Potter ◽  
...  
Keyword(s):  
Soil Moisture ◽  
Climate Variability ◽  
Time Scales ◽  
North American ◽  
Vegetation Index ◽  
Normalized Difference Vegetation Index ◽  
Spectral Characteristics ◽  
Growing Season ◽  
Statistical Technique ◽  
The North

Abstract Feedbacks of vegetation on summertime climate variability over the North American Grasslands are analyzed using the statistical technique of Granger causality. Results indicate that normalized difference vegetation index (NDVI) anomalies early in the growing season have a statistically measurable effect on precipitation and surface temperature later in summer. In particular, higher means and/or decreasing trends of NDVI anomalies tend to be followed by lower rainfall but higher temperatures during July through September. These results suggest that initially enhanced vegetation may deplete soil moisture faster than normal and thereby induce drier and warmer climate anomalies via the strong soil moisture–precipitation coupling in these regions. Consistent with this soil moisture–precipitation feedback mechanism, interactions between temperature and precipitation anomalies in this region indicate that moister and cooler conditions are also related to increases in precipitation during the preceding months. Because vegetation responds to soil moisture variations, interactions between vegetation and precipitation generate oscillations in NDVI anomalies at growing season time scales, which are identified in the temporal and the spectral characteristics of the precipitation–NDVI system. Spectral analysis of the precipitation–NDVI system also indicates that 1) long-term interactions (i.e., interannual and longer time scales) between the two anomalies tend to enhance one another, 2) short-term interactions (less than 2 months) tend to damp one another, and 3) intermediary-period interactions (4–8 months) are oscillatory. Together, these results support the hypothesis that vegetation may influence summertime climate variability via the land–atmosphere hydrological cycles over these semiarid grasslands.

scholarly journals Phenology Response to Climatic Dynamic across China’s Grasslands from 1985 to 2010

2018 ◽  
Vol 7 (8) ◽  
pp. 290 ◽  
Author(s):  
Jun Wang ◽  
Tiancai Zhou ◽  
Peihao Peng
Keyword(s):  
Climate Change ◽  
Vegetation Index ◽  
Normalized Difference Vegetation Index ◽  
Climatic Factors ◽  
Growing Season ◽  
Northern China ◽  
Plant Phenology ◽  
Meadow Steppe ◽  
Typical Steppe ◽  
Alpine Steppe

Because the dynamics of phenology in response to climate change may be diverse in different grasslands, quantifying how climate change influences plant growth in different grasslands across northern China should be particularly informative. In this study, we explored the spatiotemporal variation of the phenology (start of the growing season [SOS], peak of the growing season [POS], end of the growing season [EOS], and length of the growing season [LOS]) across China’s grasslands using a dataset of the GIMMS3g normalized difference vegetation index (NDVI, 1985–2010), and determined the effects of the annual mean temperature (AMT) and annual mean precipitation (AMP) on the significantly changed phenology. We found that the SOS, POS, and EOS advanced at the rates of 0.54 days/year, 0.64 days/year, and 0.65 days/year, respectively; the LOS was shortened at a rate of 0.62 days/year across China’s grasslands. Additionally, the AMT combined with the AMP explained the different rates (ER) for the significantly dynamic SOS in the meadow steppe (R2 = 0.26, p = 0.007, ER = 12.65%) and typical steppe (R2 = 0.28, p = 0.005, ER = 32.52%); the EOS in the alpine steppe (R2 = 0.16, p < 0.05, ER = 6.22%); and the LOS in the alpine (R2 = 0.20, p < 0.05, ER = 6.06%), meadow (R2 = 0.18, p < 0.05, ER = 16.69%) and typical (R2 = 0.18, p < 0.05, ER = 19.58%) steppes. Our findings demonstrated that the plant phenology in different grasslands presented discrepant dynamic patterns, highlighting the fact that climate change has played an important role in the variation of the plant phenology across China’s grasslands, and suggested that the variation and relationships between the climatic factors and phenology in different grasslands should be explored further in the future.

scholarly journals Quantitative Retrieval of Soil Salinity Using Landsat 8 OLI Imagery

2021 ◽  
Vol 11 (23) ◽  
pp. 11145
Author(s):  
Ruolin Dong ◽  
Xiaodong Na
Keyword(s):  
Soil Moisture ◽  
Soil Salinity ◽  
Vegetation Index ◽  
Normalized Difference Vegetation Index ◽  
Soil Salinization ◽  
Landsat Tm ◽  
Landsat 8 ◽  
Entire Study ◽  
Field Samples ◽  
Spatial Differences

Soil salinization is the main reason for declining soil quality and a reduction in agricultural productivity. We derive the spatial distribution of soil moisture from the temperature vegetation dryness index (TVDI) of Landsat TM-8 OLI images to analyze the effect of spatial heterogeneity of soil moisture on the retrieval accuracy of soil salinity. We establish five soil salinity inversion models for different soil moisture levels (drought levels) based on the canopy response salinity index (CRSI), normalized difference vegetation index (NDVI), and automatic water extraction index (AWEI) derived from Landsat TM-8 OLI images. The inversion accuracy of soil salinity is assessed using 42 field samples. The results show that the average accuracies of the five inversion models are higher than that of the traditional soil salinity inversion model of the entire study area. The proposed model underestimates soil salinity in high-moisture areas and overestimates it in drought areas. Therefore, inversion models of soil salinization should consider spatial differences in soil moisture to improve the inversion accuracy.

scholarly journals Vegetation Phenological Changes in Multiple Landforms and Responses to Climate Change

2020 ◽  
Vol 9 (2) ◽  
pp. 111 ◽  
Author(s):  
Hongzhu Han ◽  
Jianjun Bai ◽  
Gao Ma ◽  
Jianwu Yan
Keyword(s):  
Climate Change ◽  
Vegetation Index ◽  
Normalized Difference Vegetation Index ◽  
Climatic Factors ◽  
Growing Season ◽  
Vegetation Phenology ◽  
The South ◽  
Response Characteristics ◽  
The North ◽  
Spatio Temporal

Vegetation phenology is highly sensitive to climate change, and the phenological responses of vegetation to climate factors vary over time and space. Research on the vegetation phenology in different climatic regimes will help clarify the key factors affecting vegetation changes. In this paper, based on a time-series reconstruction of Moderate-Resolution Imaging Spectroradiometer (MODIS) normalized difference vegetation index (NDVI) data using the Savitzky–Golay filtering method, the phenology parameters of vegetation were extracted, and the Spatio-temporal changes from 2001 to 2016 were analyzed. Moreover, the response characteristics of the vegetation phenology to climate changes, such as changes in temperature, precipitation, and sunshine hours, were discussed. The results showed that the responses of vegetation phenology to climatic factors varied within different climatic regimes and that the Spatio-temporal responses were primarily controlled by the local climatic and topographic conditions. The following were the three key findings. (1) The start of the growing season (SOS) has a regular variation with the latitude, and that in the north is later than that in the south. (2) In arid areas in the north, the SOS is mainly affected by the temperature, and the end of the growing season (EOS) is affected by precipitation, while in humid areas in the south, the SOS is mainly affected by precipitation, and the EOS is affected by the temperature. (3) Human activities play an important role in vegetation phenology changes. These findings would help predict and evaluate the stability of different ecosystems.

scholarly journals Mapping the Groundwater Level and Soil Moisture of a Montane Peat Bog Using UAV Monitoring and Machine Learning

2021 ◽  
Vol 13 (5) ◽  
pp. 907
Author(s):  
Theodora Lendzioch ◽  
Jakub Langhammer ◽  
Lukáš Vlček ◽  
Robert Minařík
Keyword(s):  
Machine Learning ◽  
Soil Moisture ◽  
Spatial Data ◽  
Groundwater Level ◽  
Vegetation Index ◽  
Normalized Difference Vegetation Index ◽  
Sampling Strategy ◽  
Peat Bog ◽  
Strong Impact ◽  
Ground Truth Data

One of the best preconditions for the sufficient monitoring of peat bog ecosystems is the collection, processing, and analysis of unique spatial data to understand peat bog dynamics. Over two seasons, we sampled groundwater level (GWL) and soil moisture (SM) ground truth data at two diverse locations at the Rokytka Peat bog within the Sumava Mountains, Czechia. These data served as reference data and were modeled with a suite of potential variables derived from digital surface models (DSMs) and RGB, multispectral, and thermal orthoimages reflecting topomorphometry, vegetation, and surface temperature information generated from drone mapping. We used 34 predictors to feed the random forest (RF) algorithm. The predictor selection, hyperparameter tuning, and performance assessment were performed with the target-oriented leave-location-out (LLO) spatial cross-validation (CV) strategy combined with forward feature selection (FFS) to avoid overfitting and to predict on unknown locations. The spatial CV performance statistics showed low (R2 = 0.12) to high (R2 = 0.78) model predictions. The predictor importance was used for model interpretation, where temperature had strong impact on GWL and SM, and we found significant contributions of other predictors, such as Normalized Difference Vegetation Index (NDVI), Normalized Difference Index (NDI), Enhanced Red-Green-Blue Vegetation Index (ERGBVE), Shape Index (SHP), Green Leaf Index (GLI), Brightness Index (BI), Coloration Index (CI), Redness Index (RI), Primary Colours Hue Index (HI), Overall Hue Index (HUE), SAGA Wetness Index (TWI), Plan Curvature (PlnCurv), Topographic Position Index (TPI), and Vector Ruggedness Measure (VRM). Additionally, we estimated the area of applicability (AOA) by presenting maps where the prediction model yielded high-quality results and where predictions were highly uncertain because machine learning (ML) models make predictions far beyond sampling locations without sampling data with no knowledge about these environments. The AOA method is well suited and unique for planning and decision-making about the best sampling strategy, most notably with limited data.

scholarly journals Relationship between MODIS Derived NDVI and Yield of Cereals for Selected European Countries

Agronomy ◽  
2021 ◽  
Vol 11 (2) ◽  
pp. 340
Author(s):  
Ewa Panek ◽  
Dariusz Gozdowski
Keyword(s):  
Linear Regression ◽  
Grain Yield ◽  
Vegetation Index ◽  
Normalized Difference Vegetation Index ◽  
Arable Land ◽  
Early Spring ◽  
European Countries ◽  
Yield Prediction ◽  
Country Level ◽  
Using Data

In this study, the relationships between normalized difference vegetation index (NDVI) obtained based on MODIS satellite data and grain yield of all cereals, wheat and barley at a country level were analyzed. The analysis was performed by using data from 2010–2018 for 20 European countries, where percentage of cereals is high (at least 35% of the arable land). The analysis was performed for each country separately and for all of the collected data together. The relationships between NDVI and cumulative NDVI (cNDVI) were analyzed by using linear regression. Relationships between NDVI in early spring and grain yield of cereals were very strong for Croatia, Czechia, Germany, Hungary, Latvia, Lithuania, Poland and Slovakia. This means that the yield prediction for these countries can be as far back as 4 months before the harvest. The increase of NDVI in early spring was related to the increase of grain yield by about 0.5–1.6 t/ha. The cumulative of averaged NDVI gives more stable prediction of grain yield per season. For France and Belgium, the relationships between NDVI and grain yield were very weak.

scholarly journals Analysis of Drought Characteristics in Xilingol Grassland of Northern China Based on SPEI and Its Impact on Vegetation

2017 ◽  
Vol 2017 ◽  
pp. 1-11 ◽  
Author(s):  
Siqin Tong ◽  
Yuhai Bao ◽  
Rigele Te ◽  
Qiyun Ma ◽  
Si Ha ◽  
...  
Keyword(s):  
Vegetation Index ◽  
Normalized Difference Vegetation Index ◽  
Pearson Correlation ◽  
Growing Season ◽  
Vegetation Coverage ◽  
Semiarid Region ◽  
Linear Regression Method ◽  
Upward Trend ◽  
Drought Effect ◽  
Positive Correlation

This research is based on the standardized precipitation evapotranspiration index (SPEI) and normalized difference vegetation index (NDVI) which represent the drought and vegetation condition on land. Take the linear regression method and Pearson correlation analysis to study the spatial and temporal evolution of SPEI and NDVI and the drought effect on vegetation. The results show that (1) during 1961–2015, SPEI values at different time scales showed a downward trend; SPEI-12 has a mutation in 1997 and the SPEI value significantly decreased after this year. (2) During 2000–2015, the annual growing season SPEI has an obvious upward trend in time and the apparent wetting spatially. (3) In the recent 16 years, the growing season NDVI showed an upward trend and more than 80% of the total area’s vegetation increased in Xilingol. (4) Vegetation coverage in Xilingol grew better in humid years and opposite in arid years. SPEI and NDVI had a significant positive correlation; 98% of the region showed positive correlation, indicating that meteorological drought affects vegetation growth more in arid and semiarid region. (5) The effect of drought on vegetation has lag effect, and the responses of different grassland types to different scales of drought were different.

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