Relationship of cotton nitrogen and yield with Normalized Difference Vegetation Index and plant height

2014 ◽  
Vol 100 (2) ◽  
pp. 147-160 ◽  
Author(s):  
Guisu Zhou ◽  
Xinhua Yin
Keyword(s):  
Plant Height ◽  
Vegetation Index ◽  
Normalized Difference Vegetation Index ◽  
Relationship Of

Inferring Relationship of Landslides, Tectonics, and Climate

2020 ◽  
pp. 169-179
Author(s):  
Imlirenla Jamir ◽  
Pranaya Diwate ◽  
Vipin Kumar ◽  
Gambhir Singh Chauhan
Keyword(s):  
Vegetation Index ◽  
Normalized Difference Vegetation Index ◽  
Daily Rainfall ◽  
Feedback System ◽  
Distributional Pattern ◽  
Relative Significance ◽  
Tectonic Erosion ◽  
Relationship Of ◽  
Climate Proxies

Landslides, despite being the surficial impression of climate-tectonic-erosion linkage, are rarely explored in this context in Himalaya. The need for such study becomes more crucial in the evaluation of the regional hillslope denudation budget. We are of the understanding that the distributional pattern of landslides can reveal the relative significance of tectonic and climate. To test this hypothesis, ~ 55 landslides of the Tons River valley, Himalaya along with the tectonic and climate proxies are used in the present study. Steepness index and valley floor width to valley height ratio are used to infer the tectonic regime whereas; Tropical Rainfall Measurement Mission based daily rainfall data and swath profile of Normalized Difference Vegetation Index are used to deduce spatial variability in climate. The study revealed the possible existence of a positive feedback system in the Higher Himalaya Crystalline and the simultaneous role of tectonic-climate in the Lesser Himalaya Crystalline. The LHS is found to possess a zone of landslide cluster, possibly due to local fault.

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>

scholarly journals Hyperspectral field reflectance measurements to estimate wheat grain yield and plant height

2006 ◽  
Vol 63 (2) ◽  
pp. 130-138 ◽  
Author(s):  
Alexandre Cândido Xavier ◽  
Bernardo Friedrich Theodor Rudorff ◽  
Mauricio Alves Moreira ◽  
Brummer Seda Alvarenga ◽  
José Guilherme de Freitas ◽  
...  
Keyword(s):  
Grain Yield ◽  
Plant Height ◽  
Vegetation Index ◽  
Normalized Difference Vegetation Index ◽  
Narrow Band ◽  
Broad Band ◽  
Growth Stages ◽  
Sensing Applications ◽  
Field Reflectance ◽  
Reflectance Measurements

Hyperspectral crop reflectance data are useful for several remote sensing applications in agriculture, but there is still a need for studies to define optimal wavebands to estimate crop biophysical parameters. The objective of this work is to analyze the use of narrow and broad band vegetation indices (VI) derived from hyperspectral field reflectance measurements to estimate wheat (Triticum aestivum L.) grain yield and plant height. A field study was conducted during the winter growing season of 2003 in Campinas, São Paulo State, Brazil. Field canopy reflectance measurements were acquired at six wheat growth stages over 80 plots with four wheat cultivars (IAC-362, IAC-364, IAC-370, and IAC-373), five levels of nitrogen fertilizer (0, 30, 60, 90, and 120 kg of N ha-1) and four replicates. The following VI were analyzed: a) hyperspectral or narrow-band VI (1. optimum multiple narrow-band reflectance, OMNBR; 2. narrow-band normalized difference vegetation index, NB_NDVI; 3. first- and second-order derivative of reflectance; and 4. four derivative green vegetation index); and b) broad band VI (simple ratio, SR; normalized difference vegetation index, NDVI; and soil-adjusted vegetation index, SAVI). Hyperspectral indices provided an overall better estimate of biophysical variables when compared to broad band VI. The OMNBR with four bands presented the highest R² values to estimate both grain yield (R² = 0.74; Booting and Heading stages) and plant height (R² = 0.68; Heading stage). Best results to estimate biophysical variables were observed for spectral measurements acquired between Tillering II and Heading stages.

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

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.

scholarly journals The Combined Effect of Drought Stress and Nitrogen Fertilization on Soybean

Agronomy ◽  
2020 ◽  
Vol 10 (3) ◽  
pp. 384
Author(s):  
Oqba Basal ◽  
András Szabó
Keyword(s):  
Drought Stress ◽  
Plant Height ◽  
Vegetation Index ◽  
Normalized Difference Vegetation Index ◽  
Seed Quality ◽  
Yield Component ◽  
Stress Conditions ◽  
Irrigation Regimes ◽  
Pod Number ◽  
Plant Plant

Soybean is one of the most important crops worldwide; however, its production and produced seed quality are challenged by the increasingly reported drought waves because of its relative susceptibility to drought stress conditions. Nitrogen (N) is a major macronutrient that has distinctive influence on soybean, especially if applied in correct rates. Moreover, N has an additive importance under drought stress conditions. An experiment was carried out in Debrecen, Hungary in 2017, 2018, and 2019 to investigate the sole and the combined effects of N application under different irrigation regimes on soybean physiology, yield, and its components in addition to the quality of the produced yield. Results showed that the morpho-physiological traits, in addition to the yield component traits were influenced by both fertilization rates and irrigation regimes. Most importantly, high N rate is not recommended with the absence of drought conditions as, compared to low rate, it decreased flower and pod number per plant, plant height, and seed yield. On the other hand, high N rate enhanced most traits under drought stress conditions. 100-seed weight had the highest correlation with yield, followed by flower and pod number per plant, plant height, and Normalized Difference Vegetation Index (NDVI).

scholarly journals Normalized Difference Vegetation Index Temporal Responses to Temperature and Precipitation in Arid Rangelands

2021 ◽  
Vol 13 (5) ◽  
pp. 840
Author(s):  
Ernesto Sanz ◽  
Antonio Saa-Requejo ◽  
Carlos H. Díaz-Ambrona ◽  
Margarita Ruiz-Ramos ◽  
Alfredo Rodríguez ◽  
...  
Keyword(s):  
Vegetation Index ◽  
Normalized Difference Vegetation Index ◽  
Time Lag ◽  
Vegetation Indices ◽  
Aridity Index ◽  
Rangeland Degradation ◽  
Temperature And Precipitation ◽  
Lag Effect ◽  
Relationship Of ◽  
The Relationship

Rangeland degradation caused by increasing misuses remains a global concern. Rangelands have a remarkable spatiotemporal heterogeneity, making them suitable to be monitored with remote sensing. Among the remotely sensed vegetation indices, Normalized Difference Vegetation Index (NDVI) is most used in ecology and agriculture. In this paper, we research the relationship of NDVI with temperature, precipitation, and Aridity Index (AI) in four different arid rangeland areas in Spain’s southeast. We focus on the interphase variability, studying time series from 2002 to 2019 with regression analysis and lagged correlation at two different spatial resolutions (500 × 500 and 250 × 250 m2) to understand NDVI response to meteorological variables. Intraseasonal phases were defined based on NDVI patterns. Strong correlation with temperature was reported in phases with high precipitations. The correlation between NDVI and meteorological series showed a time lag effect depending on the area, phase, and variable observed. Differences were found between the two resolutions, showing a stronger relationship with the finer one. Land uses and management affected the NDVI dynamics heavily strongly linked to temperature and water availability. The relationship between AI and NDVI clustered the areas in two groups. The intraphases variability is a crucial aspect of NDVI dynamics, particularly in arid regions.

scholarly journals Application of Optical Sensors for Nitrogen Management in Chrysanthemum

HortScience ◽  
2016 ◽  
Vol 51 (7) ◽  
pp. 915-920 ◽  
Author(s):  
Amir Ali Khoddamzadeh ◽  
Bruce L. Dunn
Keyword(s):  
Plant Height ◽  
Optical Sensors ◽  
Crop Production ◽  
Vegetation Index ◽  
Normalized Difference Vegetation Index ◽  
Dry Weight ◽  
Fertilizer Treatment ◽  
Shoot Dry Weight ◽  
Fertilizer Rates ◽  
Leaf N

Nitrogen (N) is an important component of proteins and chlorophyll, and has been correlated with optical sensors as a means to determine N status during crop production. In this experiment, chrysanthemum ‘Amico Bronze’ and ‘Jacqueline Yellow’ had initial controlled-release fertilizer rates of 0, 5, 10, 15, or 20 g. Normalized Difference Vegetation Index (NDVI), Soil Plant Analytical Development (SPAD), and atLEAF sensor readings were taken at 10, 17, 24, 31, 38, and 45 days after adding initial fertilizer treatments (DAT). NDVI was correlated with leaf N concentration at all sampling dates except 17 DAT. Values for NDVI increased linearly up to 31 DAT for all treatments then plateaued at 45 DAT. Values for SPAD were only correlated with leaf N at 24 DAT, whereas, NDVI was correlated as early as 10 DAT. The atLEAF sensor was not correlated with leaf N at any sampling date. With weeks combined, correlation analysis showed correlations among leaf N and fertilizer rates, fertilizer rates and SPAD, and SPAD with NDVI and atLEAF. Thirty-one days after initial fertilizer treatment, 10 pots per treatment per cultivar were supplemented as following: 15 g supplemented to the 0 g treatment, 10 g to the 5 g treatment, and 5 g to the 10 g treatment at 31 DAT. With supplemented fertilizer treatments (SFTs), NDVI increased weekly until 45 DAT for ‘Amico Bronze’, while SPAD values did not increase in any treatments. The greatest atLEAF values occurred with 10 (+5) g and 0 (+15) g N in both cultivars. All sensor readings were only taken on leaves without any flowers. The greatest number of flowers, plant height, and shoot dry weight occurred with 10 (+5) g of additional N, but no differences occurred between 5 (+10) g and 0 (+15) g for height and shoot dry weight. No correlations existed between fertilizer rates, SPAD, NDVI, and leaf N for SFT in either cultivar. In summary, results indicated that NDVI values correlated greater (P ≤ 0.05 and P ≤ 0.01) with leaf N than SPAD and atLEAF chlorophyll sensors. Supplemental fertilizer application improved plant quality in terms of number of flowers, plant height, and shoot dry weight for all treatments, indicating that SFT could be used to correct N deficiency during crop production; however, not in combination with nondestructive sensor readings because of inconsistencies in the ability of all three sensors to separate among fertilizer treatments during a short production schedule.

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