scholarly journals Application of reflectance based vegetation index to derive dual crop co-efficient for summer sesame

2021 ◽  
Vol 16 (AAEBSSD) ◽  
pp. 62-72
Author(s):  
A. P. Lakkad ◽  
S. G. Patel ◽  
Vibhuti A. Patel ◽  
M. G. Varma
Keyword(s):  
Vegetation Index ◽  
Irrigation System ◽  
Vegetation Indices ◽  
Soil Surface ◽  
Water Requirement ◽  
Soil Evaporation ◽  
Growth Stages ◽  
Physiologic Parameters ◽  
Crop Growth Stages

Dual crop co-efficient approach was applied to estimate seasonal water requirement for summer sesame using reflectance based vegetation indices. Field experiment was conducted to collect the required various crop physiologic parameters and NDVI data for the study crop during 2018 and 2019. Basal crop co-efficients and soil evaporation co-efficients collected from FAO-56 for initial mid and end stages of summer sesame were adjusted for study area using local weather parameters. Spectrum® Field Scout CM 1000 NDVI Meter were used to collect the NDVI data at various stages of study crop. The NDVI was measured from crop canopy and soil surface at 7 days intervals between 12.00 to 13.00 clocks. NDVI Based Basal Crop Co-efficient and Soil evaporation co-efficient were derived using standard methods. FAO estimated crop co-efficients were compared with NDVI based crop co-efficients. The co-efficient of determination of the fitted regression equation was found to be 0.836 and 0.765 for drip irrigation and 0.783 and 0.867 for surface control irrigation system for summer sesame during 2018 and 2019, respectively. Crop growing stage wise water requirement per unit area was estimated for both treatments. Results indicates that among these two methods, NDVI method estimate lowest water requirement in both cases i.e. total water requirement and during all the crop growth stages for both irrigation systems while daily crop water requirement was lower for all growth stages in drip system as compare with control system.

scholarly journals TIME SERIES ANALYSIS OF REMOTE SENSING OBSERVATIONS FOR CITRUS CROP GROWTH STAGE AND EVAPOTRANSPIRATION ESTIMATION

2016 ◽  
Vol XLI-B8 ◽  
pp. 1037-1042 ◽  
Author(s):  
S. A. Sawant ◽  
M. Chakraborty ◽  
S. Suradhaniwar ◽  
J. Adinarayana ◽  
S. S. Durbha
Keyword(s):  
Remote Sensing ◽  
Time Series ◽  
Growth Stage ◽  
Crop Growth ◽  
Water Requirement ◽  
Growth Stages ◽  
Crop Water ◽  
Horticultural Crops ◽  
Crop Growth Stages ◽  
Crop Growth Stage

Satellite based earth observation (EO) platforms have proved capability to spatio-temporally monitor changes on the earth's surface. Long term satellite missions have provided huge repository of optical remote sensing datasets, and United States Geological Survey (USGS) Landsat program is one of the oldest sources of optical EO datasets. This historical and near real time EO archive is a rich source of information to understand the seasonal changes in the horticultural crops. Citrus (Mandarin / Nagpur Orange) is one of the major horticultural crops cultivated in central India. Erratic behaviour of rainfall and dependency on groundwater for irrigation has wide impact on the citrus crop yield. Also, wide variations are reported in temperature and relative humidity causing early fruit onset and increase in crop water requirement. Therefore, there is need to study the crop growth stages and crop evapotranspiration at spatio-temporal scale for managing the scarce resources. In this study, an attempt has been made to understand the citrus crop growth stages using Normalized Difference Time Series (NDVI) time series data obtained from Landsat archives (<a href="http://earthexplorer.usgs.gov/"target="_blank">http://earthexplorer.usgs.gov/</a>). Total 388 Landsat 4, 5, 7 and 8 scenes (from year 1990 to Aug. 2015) for Worldwide Reference System (WRS) 2, path 145 and row 45 were selected to understand seasonal variations in citrus crop growth. Considering Landsat 30 meter spatial resolution to obtain homogeneous pixels with crop cover orchards larger than 2 hectare area was selected. To consider change in wavelength bandwidth (radiometric resolution) with Landsat sensors (i.e. 4, 5, 7 and 8) NDVI has been selected to obtain continuous sensor independent time series. The obtained crop growth stage information has been used to estimate citrus basal crop coefficient information (Kcb). Satellite based Kcb estimates were used with proximal agrometeorological sensing system observed relevant weather parameters for crop ET estimation. The results show that time series EO based crop growth stage estimates provide better information about geographically separated citrus orchards. Attempts are being made to estimate regional variations in citrus crop water requirement for effective irrigation planning. In future high resolution Sentinel 2 observations from European Space Agency (ESA) will be used to fill the time gaps and to get better understanding about citrus crop canopy parameters.

scholarly journals THEORETICAL MODELLING STUDY ON THE RELATIONSHIP BETWEEN MULTI-FREQUENCY MICROWAVE VEGETATION INDEX AND VEGETATION PROPERTIES (OPTICAL DEPTH AND SINGLE SCATTERING ALBEDO)

2018 ◽  
Vol XLII-3 ◽  
pp. 1623-1627
Author(s):  
S. Talebi ◽  
J. Shi ◽  
T. Zhao
Keyword(s):  
Optical Depth ◽  
Vegetation Index ◽  
Vegetation Indices ◽  
Soil Surface ◽  
Single Scattering ◽  
Single Scattering Albedo ◽  
Theoretical Modelling ◽  
Vegetation Monitoring ◽  
Scattering Effects ◽  
The Relationship

This paper presents a theoretical study of derivation Microwave Vegetation Indices (MVIs) in different pairs of frequencies using two methods. In the first method calculating MVI in different frequencies based on Matrix Doubling Model (to take in to account multi scattering effects) has been done and analyzed in various soil properties. The second method was based on MVI theoretical basis and its independency to underlying soil surface signals. Comparing the results from two methods with vegetation properties (single scattering albedo and optical depth) indicated partial correlation between MVI from first method and optical depth, and full correlation between MVI from second method and vegetation properties. The second method to derive MVI can be used widely in global microwave vegetation monitoring.

scholarly journals Influence of Soil Background on Spectral Reflectance of Winter Wheat Crop Canopy

2019 ◽  
Vol 11 (16) ◽  
pp. 1932 ◽  
Author(s):  
Elena Prudnikova ◽  
Igor Savin ◽  
Gretelerika Vindeker ◽  
Praskovia Grubina ◽  
Ekaterina Shishkonakova ◽  
...  
Keyword(s):  
Winter Wheat ◽  
Soil Type ◽  
Spectral Reflectance ◽  
Vegetation Index ◽  
Vegetation Indices ◽  
Soil Surface ◽  
Wheat Crop ◽  
Crop Canopy ◽  
Winter Wheat Crop ◽  
Type Factor

The spectral reflectance of crop canopy is a spectral mixture, which includes soil background as one of the components. However, as soil is characterized by substantial spatial variability and temporal dynamics, its contribution to the spectral reflectance of crops will also vary. The aim of the research was to determine the impact of soil background on spectral reflectance of crop canopy in visible and near-infrared parts of the spectrum at different stages of crop development and how the soil type factor and the dynamics of soil surface affect vegetation indices calculated for crop assessment. The study was conducted on three test plots with winter wheat located in the Tula region of Russia and occupied by three contrasting types of soil. During field trips, information was collected on the spectral reflectance of winter wheat crop canopy, winter wheat leaves, weeds and open soil surface for three phenological phases (tillering, shooting stage, milky ripeness). The assessment of the soil contribution to the spectral reflectance of winter wheat crop canopy was based on a linear spectral mixture model constructed from field data. This showed that the soil background effect is most pronounced in the regions of 350–500 nm and 620–690 nm. In the shooting stage, the contribution of the soil prevails in the 620–690 nm range of the spectrum and the phase of milky ripeness in the region of 350–500 nm. The minimum contribution at all stages of winter wheat development was observed at wavelengths longer than 750 nm. The degree of soil influence varies with soil type. Analysis of variance showed that normalized difference vegetation index (NDVI) was least affected by soil type factor, the influence of which was about 30%–50%, depending on the stage of winter wheat development. The influence of soil type on soil-adjusted vegetation index (SAVI) and enhanced vegetation index (EVI2) was approximately equal and varied from 60% (shooting phase) to 80% (tillering phase). According to the discriminant analysis, the ability of vegetation indices calculated for winter wheat crop canopy to distinguish between winter wheat crops growing on different soil types changed from the classification accuracy of 94.1% (EVI2) in the tillering stage to 75% (EVI2 and SAVI) in the shooting stage to 82.6% in the milky ripeness stage (EVI2, SAVI, NDVI). The range of the sensitivity of the vegetation indices to the soil background depended on soil type. The indices showed the greatest sensitivity on gray forest soil when the wheat was in the phase of milky ripeness, and on leached chernozem when the wheat was in the tillering phase. The observed patterns can be used to develop vegetation indices, invariant to second-type soil variations caused by soil type factor, which can be applied for the remote assessment of the state of winter wheat crops.

scholarly journals TIME SERIES ANALYSIS OF REMOTE SENSING OBSERVATIONS FOR CITRUS CROP GROWTH STAGE AND EVAPOTRANSPIRATION ESTIMATION

2016 ◽  
Vol XLI-B8 ◽  
pp. 1037-1042 ◽  
Author(s):  
S. A. Sawant ◽  
M. Chakraborty ◽  
S. Suradhaniwar ◽  
J. Adinarayana ◽  
S. S. Durbha
Keyword(s):  
Remote Sensing ◽  
Time Series ◽  
Growth Stage ◽  
Crop Growth ◽  
Water Requirement ◽  
Growth Stages ◽  
Crop Water ◽  
Horticultural Crops ◽  
Crop Growth Stages ◽  
Crop Growth Stage

Satellite based earth observation (EO) platforms have proved capability to spatio-temporally monitor changes on the earth's surface. Long term satellite missions have provided huge repository of optical remote sensing datasets, and United States Geological Survey (USGS) Landsat program is one of the oldest sources of optical EO datasets. This historical and near real time EO archive is a rich source of information to understand the seasonal changes in the horticultural crops. Citrus (Mandarin / Nagpur Orange) is one of the major horticultural crops cultivated in central India. Erratic behaviour of rainfall and dependency on groundwater for irrigation has wide impact on the citrus crop yield. Also, wide variations are reported in temperature and relative humidity causing early fruit onset and increase in crop water requirement. Therefore, there is need to study the crop growth stages and crop evapotranspiration at spatio-temporal scale for managing the scarce resources. In this study, an attempt has been made to understand the citrus crop growth stages using Normalized Difference Time Series (NDVI) time series data obtained from Landsat archives (<a href="http://earthexplorer.usgs.gov/"target="_blank">http://earthexplorer.usgs.gov/</a>). Total 388 Landsat 4, 5, 7 and 8 scenes (from year 1990 to Aug. 2015) for Worldwide Reference System (WRS) 2, path 145 and row 45 were selected to understand seasonal variations in citrus crop growth. Considering Landsat 30 meter spatial resolution to obtain homogeneous pixels with crop cover orchards larger than 2 hectare area was selected. To consider change in wavelength bandwidth (radiometric resolution) with Landsat sensors (i.e. 4, 5, 7 and 8) NDVI has been selected to obtain continuous sensor independent time series. The obtained crop growth stage information has been used to estimate citrus basal crop coefficient information (Kcb). Satellite based Kcb estimates were used with proximal agrometeorological sensing system observed relevant weather parameters for crop ET estimation. The results show that time series EO based crop growth stage estimates provide better information about geographically separated citrus orchards. Attempts are being made to estimate regional variations in citrus crop water requirement for effective irrigation planning. In future high resolution Sentinel 2 observations from European Space Agency (ESA) will be used to fill the time gaps and to get better understanding about citrus crop canopy parameters.

An Efficient Pipeline for Crop Image Extraction and Vegetation Index Derivation Using Unmanned Aerial Systems

2020 ◽  
Vol 63 (4) ◽  
pp. 1133-1146
Author(s):  
Beichen Lyu ◽  
Stuart D. Smith ◽  
Yexiang Xue ◽  
Katy M. Rainey ◽  
Keith Cherkauer
Keyword(s):  
Data Processing ◽  
High Throughput ◽  
Large Scale ◽  
Vegetation Index ◽  
Vegetation Indices ◽  
Unmanned Aerial Systems ◽  
Growth Stages ◽  
Processing Pipeline ◽  
High Throughput Phenotyping ◽  
Aerial Systems

HighlightsThis study addresses two computational challenges in high-throughput phenotyping: scalability and efficiency.Specifically, we focus on extracting crop images and deriving vegetation indices using unmanned aerial systems.To this end, we outline a data processing pipeline, featuring a crop localization algorithm and trie data structure.We demonstrate the efficacy of our approach by computing large-scale and high-precision vegetation indices in a soybean breeding experiment, where we evaluate soybean growth under water inundation and temporal change.Abstract. In agronomy, high-throughput phenotyping (HTP) can provide key information for agronomists in genomic selection as well as farmers in yield prediction. Recently, HTP using unmanned aerial systems (UAS) has shown advantages in both cost and efficiency. However, scalability and efficiency have not been well studied when processing images in complex contexts, such as using multispectral cameras, and when images are collected during early and late growth stages. These challenges hamper further analysis to quantify phenotypic traits for large-scale and high-precision applications in plant breeding. To solve these challenges, our research team previously built a three-step data processing pipeline, which is highly modular. For this project, we present improvements to the previous pipeline to improve canopy segmentation and crop plot localization, leading to improved accuracy in crop image extraction. Furthermore, we propose a novel workflow based on a trie data structure to compute vegetation indices efficiently and with greater flexibility. For each of our proposed changes, we evaluate the advantages by comparison with previous models in the literature or by comparing processing results using both the original and improved pipelines. The improved pipeline is implemented as two MATLAB programs: Crop Image Extraction version 2 (CIE 2.0) and Vegetation Index Derivation version 1 (VID 1.0). Using CIE 2.0 and VID 1.0, we compute canopy coverage and normalized difference vegetation indices (NDVIs) for a soybean phenotyping experiment. We use canopy coverage to investigate excess water stress and NDVIs to evaluate temporal patterns across the soybean growth stages. Both experimental results compare favorably with previous studies, especially for approximation of soybean reproductive stage. Overall, the proposed methodology and implemented experiments provide a scalable and efficient paradigm for applying HTP with UAS to general plant breeding. Keywords: Data processing pipeline, High-throughput phenotyping, Image processing, Soybean breeding, Unmanned aerial systems, Vegetation indices.

scholarly journals Reflectance-Based Vegetation Index Assessment of Four Plant Species Exposed to Lithium Chloride

Sensors ◽  
2018 ◽  
Vol 18 (9) ◽  
pp. 2750
Author(s):  
Nicole Martinez ◽  
Julia Sharp ◽  
Thomas Johnson ◽  
Wendy Kuhne ◽  
Clay Stafford ◽  
...  
Keyword(s):  
Plant Species ◽  
Lithium Chloride ◽  
Vegetation Index ◽  
Vegetation Indices ◽  
Reflectance Spectra ◽  
Treatment Level ◽  
Length Of Treatment ◽  
Contact Probe ◽  
Relative Water

This study considers whether a relationship exists between response to lithium (Li) exposure and select vegetation indices (VI) determined from reflectance spectra in each of four plant species: Arabidopsis thaliana, Helianthus annuus (sunflower), Brassica napus (rape), and Zea mays (corn). Reflectance spectra were collected every week for three weeks using an ASD FieldSpec Pro spectroradiometer with both a contact probe (CP) and a field of view probe (FOV) for plants treated twice weekly in a laboratory setting with 0 mM (control) or 15 mM of lithium chloride (LiCl) solution. Plants were harvested each week after spectra collection for determination of relevant physical endpoints such as relative water content and chlorophyll content. Mixed effects analyses were conducted on selected endpoints and vegetation indices (VI) to determine the significance of the effects of treatment level and length of treatment as well as to determine which VI would be appropriate predictors of treatment-dependent endpoints. Of the species considered, A. thaliana exhibited the most significant effects and corresponding shifts in reflectance spectra. Depending on the species and endpoint, the most relevant VIs in this study were NDVI, PSND, YI, R1676/R1933, R750/R550, and R950/R750.

scholarly journals Assessment of crop water requirement of field pea (Pisum sativum L.) in foothills valley areas of Manipur, North East India

2021 ◽  
Vol 23 (3) ◽  
pp. 306-309
Author(s):  
LAISHRAM KANTA SINGH ◽  
INGUDAM BHUPENCHANDRA ◽  
S. ROMA DEVI
Keyword(s):  
Pisum Sativum ◽  
Water Demand ◽  
Crop Growth ◽  
Water Requirement ◽  
Crop Water Requirement ◽  
Field Pea ◽  
Growth Stages ◽  
Crop Water ◽  
Pisum Sativum L ◽  
Crop Growth Stages

The purpose of this study was to assess the evapotranspiration in field pea (Pisum sativum L.) in foothills valley areas of Manipur using the Hargreaves-Samani equation to predict the plant water demand. The crop coefficient (Kc) values ranged between 0.45 and 1.28 during the crop growth stages of field pea for the five crop seasons (2013-18). The average five-year effective rainfall was estimated to be 59.0 mm, with standard deviation (SD±) ranging between 4.4 to 35.1 mm. The average crop water requirement for field pea was estimated to be 221.0 mm and the average water demand for different crop growth stages of field pea was estimated to be 20.0 mm (initial stage), 52.0 mm (development stage), 100.0 mm (mid-season) and 49.0 mm (late season). Thus, the information generated may help in effective management of crop water requirements for sustainable crop production including field pea in the region.

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