Spectral response of gram varieties under variable soil conditions

Verma, K. S.; Saxena, R.K.; Hajare, T. N.; Kharche, V. K.; Kumari, Preeti

doi:10.1080/01431160010014792

Cited by 12 publications

(5 citation statements)

References 19 publications

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“…But such a parallel increase was not expressed in NDVI values at 90 DAS. This inconsistency could be attributed to the masking effect of profuse yellow flowers which are maximum at 90 DAS (Verma et al, 2001). The yellow flowers change the reflection pattern at 90 DAS in safflower.…”

Section: Relationship Between Biometric Parameters and Spectral Indicesmentioning

confidence: 81%

On spectral indices as a function of soil variability in safflower crop

Hajare

Patil

Verma

2008

J Indian Soc Remote Sens

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Spectral indices as an indicator of physiological traits affecting safflower yield in relation to soil variability were evaluated in a two year experiment (1997)(1998)(1999). Reflectance, biometric and phonological data were collected. Two indices namely normalized differential vegetation index (NDVI) and ratio of spectral reflectance in infrared region to red region (1R/R) were derived from radiometric observation. Yield data indicated significant difference in different soils. Temporal NDVI behaviour as a function of soil type was not prominent especially in early stages of crop growth. However NDVI at 75 days after sowing (DAS) was found to be relatively better indicator of plant status and yield. IR/R was relatively less effective in indicating the differential response of crop to soil types. Effect of soil and crop interaction on spectral indices was significant at 75 and 90 DAS, which was attributed to attainment of maximum leaf area and leaf area at these stages of growth. Regression analysis showed strong positive relationship between NDVI and leaf area, dry matter and yield. IR/R and leaf area had the strongest and consistent relationship (r = 0.96). A single regression equation accounted for yield variability in the dataset. Thus possible transformation of NDVI maps (satellite data) to LAI units and consequently applications like yield forecasting was indicated. Utility of spectra-temporal data as a pointer of plant development status and yield was also demonstrated.

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Section: Relationship Between Biometric Parameters and Spectral Indicesmentioning

confidence: 81%

On spectral indices as a function of soil variability in safflower crop

Hajare

Patil

Verma

2008

J Indian Soc Remote Sens

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“…Seasonal variation in community‐level spectra results from various leaf‐, plant‐ and canopy‐level sources; however, disentangling their contributions remains difficult even with the most advanced remote sensing tools (Hashimoto et al, 2021; Klosterman et al, 2018; Schweiger et al, 2018). Such contributing drivers include changes in physiological traits and biochemistry of individual leaves over their lifespan (Chavana‐Bryant et al, 2017; Niinemets, 2016; Serbin & Townsend, 2020; Ustin & Jacquemoud, 2020; Wilson et al, 2001), shifts in leaf angle distributions and shadows (de Moura et al, 2017; Huemmrich, 2013; Wu et al, 2016), flowering and fruiting events (Chen et al, 2019; Shen et al, 2014; Verma et al, 2002; Zeng et al, 2020), environmental stressors, pests, and disturbance (Ainsworth et al, 2014; Cotrozzi et al, 2018; Fairbanks & McGwire, 2004), among others. Understanding relative importance of such factors is also challenged by species commonalities in metabolism, biochemistry and responses to stressors (Schrodt et al, 2020).…”

Section: Improving Mechanistic Understanding Of Phenology Across Scalesmentioning

confidence: 99%

Remote sensing of phenology: Towards the comprehensive indicators of plant community dynamics from species to regional scales

Dronova

Taddeo

2022

Journal of Ecology

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1. Remote sensing of vegetation phenology has long been used to characterize ecosystem functions and responses to climate at spatial and temporal scales unfeasible to field surveys. However, the potential of remote sensing to elucidate mechanistic drivers of phenology and the underlying plant community processes at such scales remains under-discussed.2. This review synthesizes possibilities to advance this knowledge using multitemporal remote sensing and discusses remaining challenges and progress in instruments and analytical tools. Recent evidence indicates that, besides documenting vegetation seasonality and responses to climate, remote sensing of phenology can help meet emerging needs for indicators of plant diversity, vegetation structure and ecosystem change. 3. Responses of phenological metrics to stressors over large, heterogeneous regions may provide clues on ecological resilience manifested in asynchronies, recovery of vegetation cycles and stable microrefugia. At the same time, important barriers persist in relation to choosing among phenological estimation methods and paradigms, characterizing phenological events beyond changes in photosynthetically active biomass, and mechanistic interpretation of phenological patterns. 4. Synthesis. Increasing temporal frequency of products, opportunities for multi-sensor data fusion, and advances in historically less available hyperspectral, active microwave and lidar instruments promise to help navigate these barriers and enable more comprehensive assessments of seasonality. Progress in customizable local platforms such as unoccupied aerial vehicles and phenocams may further enrich ground-level understanding of phenology and validate satellite-based assessments. However, remote sensing analyses alone are insufficient for mechanistic interpretation of phenology, which can be challenged by artefacts in remote sensing data and sensitivity of estimated metrics to landscape structure and spatial resolution of the inputs. Robust and informative phenological assessments call for rigorous collaborations with field ecological studies, strategic selection of ancillary | 1461

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“…Shen et al (2009), for example, found that yellow flowers (Ranunculaceae) increase reflectance measurements in the red band, resulting in variations in NDVI and EVI values. In another study, Verma et al (2002) found that abundant pink and purple flowers on Cicer arietinum plants mask canopyderived signals and consequently attenuate the relationship between NDVI and LAI. From this, it can be said that the flowering in canopy variability is influential on vegetation indices, and therefore, estimates such as the aboveground biomass (Chen et al 2009), chlorophyll content (Ge et al 2006) and LAI (Sulik and Long 2015) are affected by flowers.…”

Section: Introductionmentioning

confidence: 97%

Monitoring of Canopy Reflectance Change Based on Flowering Rate

Karakoç

Karabulut

2020

J Indian Soc Remote Sens

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Hyperspectral remote sensing data have certain disadvantages as well as being a widely used tool for investigating biophysical and biochemical characteristics in grasslands due to its many advantages. Most importantly, some external influences have negative effects on the signals obtained from the canopy. Studies conducted in recent years have revealed that one of these negative externalities is the flowering on the canopy. The purpose of this study is to show how spectral reflectance readings are affected in samples with different flowering rates. The following procedure in the given order was carried out, and this procedure was repeated for a total of 46 measurements from within 10 quadrats: (1) placing quadrats of 50 9 50 cm on selected sampling areas, (2) performing spectral measurements in the quadrats, (3) measuring the chlorophyll content, (4) taking photographs of the quadrats and (5) subtilization of some of the flowers in the quadrats. Vegetation indices were also generated from collected spectral data during the data processing stage, and the flowering rate in each canopy was determined by the supervised classification method. Relations between flowering rate and spectral data were analyzed by Pearson's correlation coefficient and linear regression models. The results show that there was a linear relationship between the flowering rate and the spectral reflectance in the red and green regions, whereas there was no statistically significant relationship with the reflectance in the NIR region. Moreover, all vegetation indices, especially REP, were affected from flowering variations. This effect was found to be lower in heterogeneous samples and higher in homogeneous samples. Evidence was found that the basic factor governing this effect was the fact that the flowers formed an obstacle to the detection of chlorophyll content by covering a certain part of the canopy.

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Spectral response of gram varieties under variable soil conditions

Cited by 12 publications

References 19 publications

On spectral indices as a function of soil variability in safflower crop

On spectral indices as a function of soil variability in safflower crop

Remote sensing of phenology: Towards the comprehensive indicators of plant community dynamics from species to regional scales

Monitoring of Canopy Reflectance Change Based on Flowering Rate

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