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.

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

A climatological study of soil moisture under corn crop at Campina Grande (NE Brazil)

MAUSAM ◽  
2021 ◽  
Vol 48 (3) ◽  
pp. 437-442
Author(s):  
K.KARUNA KUMAR ◽  
JOSE ANTONIO TOMAS DA SILVA ◽  
VIRGINIA DE FATIMA BEZERRA
Keyword(s):  
Soil Moisture ◽  
Moisture Content ◽  
Markov Chain Model ◽  
Moisture Loss ◽  
Growth Stages ◽  
Chain Model ◽  
Growing Period ◽  
Corn Crop ◽  
Order Markov Chain ◽  
Different Growth Stages

ABSTRACT. Results of a climatological study of soil moisture under corn crop at Campina Grande (NE Brazil) are presented in this paper. Daily values of available moisture content during the crop growing period are evaluated for a period of 25 years. A six zone versatile soil moisture budget model is used for this purpose and approximately 5. 7.5, 12.5, 25, 25 and 25% of the available water capacity (AWC) are attributed to zones one to six respectively. Different root activity coefficients are assumed for the six zones in different growth stages and the dependence of these coefficients on moisture content is taken into consideration. The same moisture releasing characteristics are assumed for all soil zones. On rainy days moisture loss due to evapotranspiration is assumed to take place before precipitation. Four AWC values and three corn growing periods between March and September are considered in this study. A first order Markov chain model is applied to the daily soil moisture data. Soil moisture averages and probabilities are used to identify the optimum growing period for corn at this station. The irrigation requirements of the crop are briefly discussed.  

scholarly journals Assessing the Effect of Drought on Winter Wheat Growth Using Unmanned Aerial System (UAS)-Based Phenotyping

2021 ◽  
Vol 13 (6) ◽  
pp. 1144
Author(s):  
Mahendra Bhandari ◽  
Shannon Baker ◽  
Jackie C. Rudd ◽  
Amir M. H. Ibrahim ◽  
Anjin Chang ◽  
...  
Keyword(s):  
Winter Wheat ◽  
Vegetation Index ◽  
Normalized Difference Vegetation Index ◽  
Vegetation Indices ◽  
Grain Filling ◽  
Unmanned Aerial Systems ◽  
High Temporal Resolution ◽  
Wheat Growth ◽  
Spatial Domains ◽  
Temporal And Spatial

Drought significantly limits wheat productivity across the temporal and spatial domains. Unmanned Aerial Systems (UAS) has become an indispensable tool to collect refined spatial and high temporal resolution imagery data. A 2-year field study was conducted in 2018 and 2019 to determine the temporal effects of drought on canopy growth of winter wheat. Weekly UAS data were collected using red, green, and blue (RGB) and multispectral (MS) sensors over a yield trial consisting of 22 winter wheat cultivars in both irrigated and dryland environments. Raw-images were processed to compute canopy features such as canopy cover (CC) and canopy height (CH), and vegetation indices (VIs) such as Normalized Difference Vegetation Index (NDVI), Excess Green Index (ExG), and Normalized Difference Red-edge Index (NDRE). The drought was more severe in 2018 than in 2019 and the effects of growth differences across years and irrigation levels were visible in the UAS measurements. CC, CH, and VIs, measured during grain filling, were positively correlated with grain yield (r = 0.4–0.7, p < 0.05) in the dryland in both years. Yield was positively correlated with VIs in 2018 (r = 0.45–0.55, p < 0.05) in the irrigated environment, but the correlations were non-significant in 2019 (r = 0.1 to −0.4), except for CH. The study shows that high-throughput UAS data can be used to monitor the drought effects on wheat growth and productivity across the temporal and spatial domains.

scholarly journals Improving Spatial Soil Moisture Representation through the Integration of SMAP and PROBA-V Products

2018 ◽  
Vol 10 (10) ◽  
pp. 3459
Author(s):  
Shu-Di Fan ◽  
Yue-Ming Hu ◽  
Lu Wang ◽  
Zhen-Hua Liu ◽  
Zhou Shi ◽  
...  
Keyword(s):  
Soil Moisture ◽  
Spatial Resolution ◽  
Vegetation Index ◽  
Factor Model ◽  
Original Method ◽  
Vegetation Coverage ◽  
Estimation Accuracy ◽  
Modified Model ◽  
The North ◽  
Moderate Resolution Imaging Spectroradiometer

To increase the spatial resolution of Soil Moisture Active Passive (SMAP), this study modifies the downscaling factor model based on the Temperature Vegetation Drought Index (TVDI) using data from the Project for On-Board Autonomy (PROBA-V). In the modified model, TVDI parameters were derived from the temperature-vegetation space and the Enhanced Vegetation Index (EVI). This study was conducted in the north China region using SMAP, PROBA-V, and Moderate Resolution Imaging Spectroradiometer satellite images. The 9-km spatial resolution SMAP data was downscaled to 0.3-km spatial resolution soil moisture using a modified downscaling method. Downscaling accuracies from the original and modified downscaling factor models were compared based on field observations. The results show that both methods generated similar spatial distributions in which soil moisture estimates increased as vegetation coverage increased from built-up areas to forest. However, based on the root mean square error between observations and estimations, the modified model demonstrated an increased estimation accuracy of 4.2% for soil moisture compared to the original method. This study also implies that downscaled soil moisture shows promise as a data source for subsequent watershed scale studies.

scholarly journals Resistance to Puccinia triticina at different stages of wheat

2009 ◽  
pp. 183-190
Author(s):  
Marina Putnik-Delic
Keyword(s):  
Leaf Senescence ◽  
Latency Period ◽  
Infection Intensity ◽  
Puccinia Triticina ◽  
Growth Stages ◽  
Infection Frequency ◽  
Strongly Correlated ◽  
Wheat Growth ◽  
Reaction Type ◽  
Different Growth Stages

Ten wheat genotypes were tested for resistance characteristics to Puccinia triticina. Infection intensity in the field was evaluated at different growth stages, and time of spike appearance and leaf senescence were recorded. At seedling stage, under the controlled conditions of greenhouse, latency period, infection frequency and reaction type were determined. Resistance characteristics at different wheat growth stages were strongly correlated. Correlation coefficient between LP x RT x IF and AUDPC values, was 0.828. The highest coefficients of correlation between particular resistance characteristics and maximal intensity in the field were determined with the last evaluation in the field (0.665, 0.476 and 0.834). Time of spike appearance was very variable for different genotypes, whereas leaf senescence was recorded concomitantly for near all genotypes. The exception was Rusalka, as the most resistant in the field. All genotypes included in this three-year long experiment expressed stability with respect to infection intensity at different growth stages. Genotype Timson showed the highest level of resistance according to all tested characteristics, while genotype Pkb krupna showed the lowest.

scholarly journals Use of an Active Canopy Sensor Mounted on an Unmanned Aerial Vehicle to Monitor the Growth and Nitrogen Status of Winter Wheat

2020 ◽  
Vol 12 (22) ◽  
pp. 3684
Author(s):  
Jie Jiang ◽  
Zeyu Zhang ◽  
Qiang Cao ◽  
Yan Liang ◽  
Brian Krienke ◽  
...  
Keyword(s):  
Winter Wheat ◽  
Dry Matter ◽  
Vegetation Index ◽  
Nutrition Status ◽  
Growth Stages ◽  
Growth Indices ◽  
N Accumulation ◽  
N Nutrition ◽  
Red Edge ◽  
Aerial Vehicle

Using remote sensing to rapidly acquire large-area crop growth information (e.g., shoot biomass, nitrogen status) is an urgent demand for modern crop production; unmanned aerial vehicle (UAV) acts as an effective monitoring platform. In order to improve the practicability and efficiency of UAV based monitoring technique, four field experiments involving different nitrogen (N) rates (0–360 kg N ha−1) and seven winter wheat (Triticum aestivum L.) varieties were conducted at different eco-sites (Sihong, Rugao, and Xinghua) during 2015–2019. A multispectral active canopy sensor (RapidSCAN CS-45; Holland Scientific Inc., Lincoln, NE, USA) mounted on a multirotor UAV platform was used to collect the canopy spectral reflectance data of winter wheat at key growth stages, three growth parameters (leaf area index (LAI), leaf dry matter (LDM), plant dry matter (PDM)) and three N indicators (leaf N accumulation (LNA), plant N accumulation (PNA) and N nutrition index (NNI)) were measured synchronously. The quantitative linear relationships between spectral data and six growth indices were systematically analyzed. For monitoring growth and N nutrition status at Feekes stages 6.0–10.0, 10.3–11.1 or entire growth stages, red edge ratio vegetation index (RERVI), red edge chlorophyll index (CIRE) and difference vegetation index (DVI) performed the best among the red edge band-based and red-based vegetation indices, respectively. Across all growth stages, DVI was highly correlated with LAI (R2 = 0.78), LDM (R2 = 0.61), PDM (R2 = 0.63), LNA (R2 = 0.65) and PNA (R2 = 0.73), whereas the relationships between RERVI (R2 = 0.62), CIRE (R2 = 0.62) and NNI had high coefficients of determination. The developed models performed better in monitoring growth indices and N status at Feekes stages 10.3–11.1 than Feekes stages 6.0–10.0. To sum it up, the UAV-mounted active sensor system is able to rapidly monitor the growth and N nutrition status of winter wheat and can be deployed for UAV-based remote-sensing of crops.

scholarly journals Differences in xylem development between Dutch and Japanese tomato (Solanum lycopersicum) correlate with cytokinin levels in hypocotyls

2020 ◽  
Vol 126 (2) ◽  
pp. 315-322 ◽  
Author(s):  
Xiaohua Qi ◽  
Hirokazu Takahashi ◽  
Yasushi Kawasaki ◽  
Yuya Ohta ◽  
Masahide Isozaki ◽  
...  
Keyword(s):  
High Yield ◽  
Growth Stages ◽  
Cytokinin Biosynthesis ◽  
Xylem Development ◽  
Isopentenyl Adenine ◽  
Use Efficiency ◽  
The Difference ◽  
Leaf Photosynthetic Rate ◽  
The Relationship ◽  
Different Growth Stages

Abstract Background and Aims Dutch tomato cultivars tend to have a greater yield than Japanese cultivars even if they are grown under the same conditions. Factors contributing to the increased yield of the Dutch cultivars were a greater light use efficiency and greater leaf photosynthetic rate. On the other hand, the relationship between tomato yields and anatomical traits is still unclear. The aim of this study is to identify the anatomical traits related to the difference in yield between Dutch and Japanese cultivars. Methods Anatomical properties were compared during different growth stages of Dutch and Japanese tomatoes. Hormone profiles and related gene expression in hypocotyls of Dutch and Japanese cultivars were compared in the hypocotyls of 3- and 4-week-old plants. Key results Dutch cultivars have a more developed secondary xylem than Japanese cultivars, which would allow for greater transport of water, mineral nutrients and phytohormones to the shoots. The areas and ratios of the xylem in the hypocotyls of 3- to 6-week-old plants were larger in the Dutch cultivars. In reciprocal grafts of the Japanese and Dutch cultivars, xylem development at the scion and rootstock depended on the scion cultivar, suggesting that some factors in the scion are responsible for the difference in xylem development. The cytokinin content, especially the level of N6-(Δ 2-isopentenyl) adenine (iP)-type cytokinin, was higher in the Dutch cultivars. This result was supported by the greater expression of Sl-IPT3 (a cytokinin biosynthesis gene) and Sl-RR16/17 (a cytokinin-responsive gene) in the Dutch cultivars. Conclusions These results suggest that iP-type cytokinins, which are locally synthesized in the hypocotyl, contribute to xylem development. The greater xylem development in Dutch cultivars might contribute to the high yield of the tomato.

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