scholarly journals Changes of soil moisture during apple growth based on TVDI index

2021 ◽  
pp. 955-961
Author(s):  
Hui Kong ◽  
Dan Wu
Keyword(s):  
Remote Sensing ◽  
Soil Moisture ◽  
Vegetation Index ◽  
Growth Stages ◽  
Temporal Consistency ◽  
Vegetation Water Content ◽  
Content Index ◽  
Research Findings ◽  
Vegetation Water ◽  
Different Growth Stages

Based on MODIS data, soil moisture data and field survey data from 2014 to 2018, the consistency of temperature vegetation drought index (TVDL), normalized vegetation water content index (NDWL), vegetation water supply index (VSWI) and soil moisture at 15cm depth (SM) in apple growth in Fuxian county was investigated. Results showed that the spatial and temporal consistency between VSWI and SM calculated by the enhanced vegetation index (EVI) was best; the sensitivity of remote sensing indexes to soil moisture was different in different apple growth stages. The sensitivity of VSWI was the most obvious in different growth stages, and the sensitivity of soil moisture was higher than that of germination, flowering, fruit expansion and maturity. The research findings were consistent with the law of water demand in different growth stages of apple in Fuxian county and the characteristics of precipitation and drought in Fuxian county. The present results could provide a reference for soil moisture monitoring of apple growth by remote sensing. Bangladesh J. Bot. 50(3): 955-961, 2021 (September) Special

scholarly journals The Modification of Difference Vegetation Index (DVI) in middle and late growing period of winter wheat and its application in soil moisture inversion

2019 ◽  
Vol 131 ◽  
pp. 01098
Author(s):  
Zhang Hong-wei ◽  
Huai-liang Chen ◽  
Fei-na Zha
Keyword(s):  
Soil Moisture ◽  
Winter Wheat ◽  
Vegetation Index ◽  
Vegetation Coverage ◽  
Growth Stages ◽  
Wheat Growth ◽  
Inversion Result ◽  
Growing Period ◽  
The Difference ◽  
Different Growth Stages

In the middle and late growing period of winter wheat, soil moisture is easily affected by saturation when using MODIS data to retrieve soil moisture. In this paper, in order to reduce the effect of the saturation caused by increasing vegetation coverage in middle and late stage of winter wheat, the Difference Vegetation Index (DVI) model was modified with different coefficients in different growth stages of winter wheat based on MODIS spectral data and LAI characteristics of variation. LAI was divided into three stages, LAI ≤ 1 < LAI ≤, 3 < LAI, and the adjusting coefficient of α=1, α=3, α=5, were taken to modifying the Difference Vegetation Index(DVI). The results show that the Modified Difference Vegetation Index (MDVIα) can effectively reduce the interference of saturation, and the inversion result of soil moisture in the middle and late period of winter wheat growth is obviously superior to the uncorrected inversion model of DVI.

Interpreting Vegetation Optical Depth (VOD) using detailed destructive vegetation sampling of a corn canopy

2020 ◽  
Author(s):  
Saeed Khabbazan ◽  
Ge Gao ◽  
Paul Vermunt ◽  
Susan Steele-Dunne ◽  
Jasmeet Judge ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Soil Moisture ◽  
Vertical Distribution ◽  
Water Content ◽  
Optical Depth ◽  
Microwave Remote Sensing ◽  
Growing Season ◽  
Vegetation Water Content ◽  
Wide Range ◽  
Vegetation Water

&lt;p&gt;Vegetation Optical Depth (VOD) is directly related to Vegetation Water Content (VWC), which can be used in different applications including crop health monitoring, water resources management and drought detection. Moreover, VOD is used to account for the attenuating effect of vegetation in soil moisture retrieval using microwave remote sensing.&lt;/p&gt;&lt;p&gt;Commonly, to retrieve soil moisture and VOD from microwave remote sensing, VWC is considered to be vertically homogeneous and relatively static.&amp;#160; However, nonuniform vertical distribution of water inside the vegetation may lead to unrealistic retrievals in agricultural areas. Therefore, it is important to improve the understanding of the relation between vegetation optical depth and distribution of bulk vegetation water content during the entire growing season.&lt;/p&gt;&lt;p&gt;The goal of this study is to investigate the effect of different factors such as phenological stage, different crop elements and nonuniform distribution of internal vegetation water content on VOD. Backscatter data were collected every 15 minutes using a tower-based, fully polarimetric, L-band radar. The methodology of Vreugdenhil et al. [1] was adapted to estimate VOD from single-incidence angle backscatter data in each polarization.&lt;/p&gt;&lt;p&gt;In order to characterize the vertical distribution of VWC, pre-dawn destructive sampling was conducted three times a week for a full growing season. VWC could therefore be analyzed by constituent (leaf, stem, ear) or by height.&lt;/p&gt;&lt;p&gt;A temporal correlation analysis showed that the relation between VOD and VWC during the growing season is not constant. The assumed linear relationship is only valid during the vegetative growth stages for corn.&amp;#160; Furthermore, the sensitivity of VOD to various plant components (leaf, stem and ear) varies between phenological stages and depends on polarization.&lt;/p&gt;&lt;p&gt;Improved understanding of VOD can contribute to improved consideration of vegetation in soil moisture retrieval algorithms. More importantly, it is essential for the interpretation of VOD data in a wide range of vegetation monitoring applications.&lt;/p&gt;&lt;p&gt;[1] M. Vreugdenhil,W. A. Dorigo,W.Wagner, R. A. De Jeu, S. Hahn, andM. J. VanMarle, &amp;#8220;Analyzing the vegetation parameterization in the TU-Wien ASCAT soil moisture retrieval,&amp;#8221; IEEE Transactions on Geoscience and Remote Sensing, vol. 54, no. 6, pp. 3513&amp;#8211;3531, 2016.&lt;/p&gt;

scholarly journals Monitoring Cotton (Gossypium sps.) Crop Condition through Synergy of Optical and Radar Remote Sensing

2018 ◽  
Author(s):  
Dipanwita Haldar ◽  
Rojalin Tripathy ◽  
Viral Dave ◽  
Rucha Dave ◽  
Bimal Bhattacharya ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Spatial Variability ◽  
Water Content ◽  
Vegetation Index ◽  
Broad Band ◽  
Plant Water ◽  
Area Index ◽  
Vegetation Water Content ◽  
Vegetation Water ◽  
Crop Condition

Morphological parameters like cotton height, branches, Leaf Area Index and biomass are mainly affected by the vegetation water content (VWC). Periodical assessment of the VWC and crop parameters is required for timely management of the crop for maximizing yield. The study aimed at using both optical and microwave remotely sensed data to assess cotton crop condition based on the above mentioned traits. Vegetation indices (VI) derived from ground based measurements (5 narrow band and 2 broad band VIs) as well as satellite derived reflectance (2 broad band VIs) were assessed. Regression models were derived for estimating LAI, biomass and plant water content using the ground based indices and applied to the satellite derived spectral index (from LISS-III) map to estimate the respective parameters. HH and HV polarization from RISAT-1 were used to derive Radar Vegetation Index (RVI). The coefficient of determination of the model for estimating LAI, biomass and vegetation water content of cotton with optical vegetation index as input parameter were found to be 0.42, 0.51 and 0.52, respectively. The correlation between RVI and plant height, date of planting in terms of the age of the crop and vegetation water content were found to range between 0.4 to 0.6. The fresh biomass from RVI showed spatial variability from 100 gm-2 to 4000 gm-2 while the dry biomass map derived from NDVI showed spatial variability of 50 to 950 g m-2 for the study area. Plant water content in the district varied from 65 to 85%. The correlation between optical vegetation index and RVI was not significant. Hence a multiple linear regression model using both optical index (NDVI and LSWI) and SAR index (RVI) was developed to assess the LAI, biomass and plant water content. The model showed a R2 of 0.5 for LAI estimation but not significant for biomass and water content. This study show cased the use of combined optical and microwave (C band) remote sensing for cotton condition assessment.

Optical Remote Sensing of Vegetation Water Content

2018 ◽  
pp. 183-200
Author(s):  
Colombo Roberto ◽  
Busetto Lorenzo ◽  
Meroni Michele ◽  
Rossini Micol ◽  
Panigada Cinzia
Keyword(s):  
Remote Sensing ◽  
Water Content ◽  
Optical Remote Sensing ◽  
Vegetation Water Content ◽  
Vegetation Water

scholarly journals ANALYZING SPECTRAL CHARACTERISTICS OF SHADOW AREA FROM ADS-40 HIGH RADIOMETRIC RESOLUTION AERIAL IMAGES

2016 ◽  
Vol XLI-B7 ◽  
pp. 223-227 ◽  
Author(s):  
Yi-Ta Hsieh ◽  
Shou-Tsung Wu ◽  
Chaur-Tzuhn Chen ◽  
Jan-Chang Chen
Keyword(s):  
Remote Sensing ◽  
Land Cover ◽  
Vegetation Index ◽  
Spectral Characteristics ◽  
Aerial Images ◽  
Aerial Image ◽  
Optical Remote Sensing ◽  
Shadow Area ◽  
Vegetation Indexes ◽  
Research Findings

The shadows in optical remote sensing images are regarded as image nuisances in numerous applications. The classification and interpretation of shadow area in a remote sensing image are a challenge, because of the reduction or total loss of spectral information in those areas. In recent years, airborne multispectral aerial image devices have been developed 12-bit or higher radiometric resolution data, including Leica ADS-40, Intergraph DMC. The increased radiometric resolution of digital imagery provides more radiometric details of potential use in classification or interpretation of land cover of shadow areas. Therefore, the objectives of this study are to analyze the spectral properties of the land cover in the shadow areas by ADS-40 high radiometric resolution aerial images, and to investigate the spectral and vegetation index differences between the various shadow and non-shadow land covers. According to research findings of spectral analysis of ADS-40 image: (i) The DN values in shadow area are much lower than in nonshadow area; (ii) DN values received from shadowed areas that will also be affected by different land cover, and it shows the possibility of land cover property retrieval as in nonshadow area; (iii) The DN values received from shadowed regions decrease in the visible band from short to long wavelengths due to scattering; (iv) The shadow area NIR of vegetation category also shows a strong reflection; (v) Generally, vegetation indexes (NDVI) still have utility to classify the vegetation and non-vegetation in shadow area. The spectral data of high radiometric resolution images (ADS-40) is potential for the extract land cover information of shadow areas.

Inferring the effects of surface canopy water on VOD estimation from L-band backscatter 

2021 ◽  
Author(s):  
Saeed Khabbazan ◽  
Paul.C. Vermunt ◽  
Susan.C. Steele Dunne ◽  
Ge Gao ◽  
Mariette Vreugdenhil ◽  
...  
Keyword(s):  
Soil Moisture ◽  
Linear Relationship ◽  
Water Content ◽  
Growing Season ◽  
Empirical Parameter ◽  
Vegetation Water Content ◽  
L Band ◽  
Vegetation Water ◽  
Canopy Water ◽  
Effect Of Surface

&lt;p&gt;Quantification of vegetation parameters such as Vegetation Optical Depth (VOD) and Vegetation Water Content (VWC) can be used for better irrigation management, yield forecasting, and soil moisture estimation. Since VOD is directly related to vegetation water content and canopy structure, it can be used as an indicator for VWC. Over the past few decades, optical and passive microwave satellite data have mostly been used to monitor VWC. However, recent research is using active data to monitor VOD and VWC benefitting from their high spatial and temporal resolution.&lt;/p&gt;&lt;p&gt;Attenuation of the microwave signal through the vegetation layer is parametrized by the VOD. VOD is assumed to be linearly related to VWC with the proportionality constant being an empirical parameter b. For a given wavelength and polarization, b is assumed static and only parametrized as a function of vegetation type. The hypothesis of this study is that the VOD is not similar for dry and wet vegetation and the static linear relationship between attenuation and vegetation water content is a simplification of reality.&lt;/p&gt;&lt;p&gt;The aim of this research is to understand the effect of surface canopy water on VOD estimation and the relationship between VOD and vegetation water content during the growing season of a corn canopy. In addition to studying the dependence of VOD on bulk VWC for dry and wet vegetation, the effect of different factors, such as different growth stages and internal vegetation water content is investigated using time series analysis.&lt;/p&gt;&lt;p&gt;A field experiment was conducted in Florida, USA, for a full growing season of sweet corn. The corn field was scanned every 30 minutes with a truck-mounted, fully polarimetric, L-band radar. Pre-dawn vegetation water content was measured using destructive sampling three times a week for a full growing season. VWC could therefore be analyzed by constituent (leaf, stem, ear) or by height. Meteorological data, surface canopy water (dew or interception), and soil moisture were measured every 15 minutes for the entire growing season.&lt;/p&gt;&lt;p&gt;The methodology of Vreugdenhil et al.&amp;#160; [1], developed by TU Wien for ASCAT data, was adapted to present a new technique to estimate VOD from single-incidence angle backscatter data in each polarization. The results showed that the effect of surface canopy water on the VOD estimation increased by vegetation biomass accumulation and the effect was higher in the VOD estimated from the co-pol compared with the VOD estimated from the cross-pol.&amp;#160;Moreover, the surface canopy water considerably affected the regression coefficient values (b-factor) of the linear relationship between VOD and VWC from dry and wet vegetation. This finding suggests that considering a similar b-factor for the dry and the wet vegetation will introduce errors in soil moisture retrievals. Furthermore, it highlights the importance of considering canopy wetness conditions when using tau-omega.&lt;/p&gt;&lt;ul&gt;&lt;li&gt;[1] Vreugdenhil,W. A. Dorigo,W.Wagner, R. A. De Jeu, S. Hahn, andM. J. VanMarle, &amp;#8220;Analyzing the vegetation parameterization in the TU-Wien ASCAT soil moisture retrieval,&amp;#8221; IEEE Transactions on Geoscience and Remote Sensing, vol. 54, pp. 3513&amp;#8211;3531, 2016&lt;/li&gt; &lt;/ul&gt;

Active Microwave Systems for Monitoring Drought Stress

2005 ◽  
Author(s):  
Anne M. Smith
Keyword(s):  
Remote Sensing ◽  
Soil Moisture ◽  
Value Added ◽  
Agricultural Drought ◽  
Sensor System ◽  
Spatial And Temporal Scales ◽  
Satellite Systems ◽  
Radio Detection ◽  
Vegetation Water ◽  
Radar Satellite

Remote sensing can provide timely and economical monitoring of large areas. It provides the ability to generate information on a variety of spatial and temporal scales. Generally, remote sensing is divided into passive and active depending on the sensor system. The majority of remote-sensing studies concerned with drought monitoring have involved visible–infrared sensor systems, which are passive and depend on the sun’s illumination. Radar (radio detection and ranging) is an active sensor system that transmits energy in the microwave region of the electromagnetic spectrum and measures the energy reflected back from the landscape target. The energy reflected back is called backscatter. The attraction of radar over visible– infrared remote sensing (chapters 5 and 6) is its independence from the sun, enabling day/night operations, as well as its ability to penetrate cloud and obtain data under most weather conditions. Thus, unlike visible–infrared sensors, radar offers the opportunity to acquire uninterrupted information relevant to drought such as soil moisture and vegetation stress. Drought conditions manifest in multiple and complex ways. Accordingly, a large number of drought indices have been defined to signal abnormally dry conditions and their effects on crop growth, river flow, groundwater, and so on (Tate and Gustard, 2000). In the field of radar remote sensing, much work has been devoted to developing algorithms to retrieve geophysical parameters such as soil moisture, crop biomass, and vegetation water content. In principle, these parameters would be highly relevant for monitoring agricultural drought. However, despite the existence of a number of radar satellite systems, progress in the use of radar in environmental monitoring, particularly in respect to agriculture, has been slower than anticipated. This may be attributed to the complex nature of radar interactions with agricultural targets and the suboptimal configuration of the satellite sensors available in the 1990s (Ulaby, 1998; Bouman et al., 1999). Because most attention is still devoted to the problem of deriving high-quality soil moisture and vegetation products, there have been few investigations on how to combine such radar products with other data and models to obtain value-added agricultural drought products.

scholarly journals Estimating Grassland Curing with Remotely Sensed Data

2017 ◽  
Author(s):  
Wasin Chaivaranont ◽  
Jason P. Evans ◽  
Yi Y. Liu ◽  
Jason J. Sharples
Keyword(s):  
Vegetation Index ◽  
Field Measurements ◽  
Fire Danger ◽  
Remotely Sensed Data ◽  
Vegetation Water Content ◽  
Grassland Ecosystems ◽  
Normalised Difference Vegetation Index ◽  
Dead Material ◽  
Degree Of Curing ◽  
Vegetation Water

Abstract. Wildfire can become a catastrophic natural hazard, especially during dry summer seasons in Australia. Severity is influenced by various meteorological, geographical, and fuel characteristics. Modified Mark 4 McArthur's Grassland Fire 10 Danger Index (GFDI) is a commonly used approach to determine the fire danger level in grassland ecosystems. The degree of curing (DOC, i.e. proportion of dead material) of the grass is one key ingredient in determining the fire danger. It is difficult to collect accurate DOC information in the field, therefore, ground observed measurements are rather limited. In this study, we used satellite observed vegetation greenness (Normalised Difference Vegetation Index, NDVI) and vegetation water content (Vegetation Optical Depth, VOD) information to improve the accuracy of the DOC estimation. First, a statistically 15 significant relationship is established between selected ground observed DOC and satellite observed vegetation datasets (NDVI and VOD) with an r2 of 0.67. DOC levels estimated using satellite observations were then evaluated using field measurements with an r2 of 0.55. Results suggest that satellite based DOC estimation can reasonably reproduce ground based observations in space and time. Comparison with currently available satellite based DOC products shows that our model has a comparable and arguably more balanced performance.

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