Improving estimation of summer maize nitrogen status with red edge-based spectral vegetation indices

2014 ◽  
Vol 157 ◽  
pp. 111-123 ◽  
Author(s):  
Fei Li ◽  
Yuxin Miao ◽  
Guohui Feng ◽  
Fei Yuan ◽  
Shanchao Yue ◽  
...  
Keyword(s):  
Vegetation Indices ◽  
Summer Maize ◽  
Nitrogen Status ◽  
Red Edge ◽  
Spectral Vegetation Indices ◽  
Edge Based

scholarly journals COMPARING BROAD-BAND AND RED EDGE-BASED SPECTRAL VEGETATION INDICES TO ESTIMATE NITROGEN CONCENTRATION OF CROPS USING CASI DATA

2016 ◽  
Vol XLI-B7 ◽  
pp. 137-143
Author(s):  
Yanjie Wang ◽  
Qinhong Liao ◽  
Guijun Yang ◽  
Haikuan Feng ◽  
Xiaodong Yang ◽  
...  
Keyword(s):  
Nitrogen Concentration ◽  
Vegetation Indices ◽  
Broad Band ◽  
Image Data ◽  
N Fertilization ◽  
Physiological Status ◽  
Red Edge ◽  
Spectral Vegetation Indices ◽  
Crop Types ◽  
Edge Based

In recent decades, many spectral vegetation indices (SVIs) have been proposed to estimate the leaf nitrogen concentration (LNC) of crops. However, most of these indices were based on the field hyperspectral reflectance. To test whether they can be used in aerial remote platform effectively, in this work a comparison of the sensitivity between several broad-band and red edge-based SVIs to LNC is investigated over different crop types. By using data from experimental LNC values over 4 different crop types and image data acquired using the Compact Airborne Spectrographic Imager (CASI) sensor, the extensive dataset allowed us to evaluate broad-band and red edge-based SVIs. The result indicated that NDVI performed the best among the selected SVIs while red edge-based SVIs didn’t show the potential for estimating the LNC based on the CASI data due to the spectral resolution. In order to search for the optimal SVIs, the band combination algorithm has been used in this work. The best linear correlation against the experimental LNC dataset was obtained by combining the 626.20nm and 569.00nm wavebands. These wavelengths correspond to the maximal chlorophyll absorption and reflection position region, respectively, and are known to be sensitive to the physiological status of the plant. Then this linear relationship was applied to the CASI image for generating an LNC map, which can guide farmers in the accurate application of their N fertilization strategies.

scholarly journals COMPARING BROAD-BAND AND RED EDGE-BASED SPECTRAL VEGETATION INDICES TO ESTIMATE NITROGEN CONCENTRATION OF CROPS USING CASI DATA

2016 ◽  
Vol XLI-B7 ◽  
pp. 137-143 ◽  
Author(s):  
Yanjie Wang ◽  
Qinhong Liao ◽  
Guijun Yang ◽  
Haikuan Feng ◽  
Xiaodong Yang ◽  
...  
Keyword(s):  
Nitrogen Concentration ◽  
Vegetation Indices ◽  
Broad Band ◽  
Image Data ◽  
N Fertilization ◽  
Physiological Status ◽  
Red Edge ◽  
Spectral Vegetation Indices ◽  
Crop Types ◽  
Edge Based

In recent decades, many spectral vegetation indices (SVIs) have been proposed to estimate the leaf nitrogen concentration (LNC) of crops. However, most of these indices were based on the field hyperspectral reflectance. To test whether they can be used in aerial remote platform effectively, in this work a comparison of the sensitivity between several broad-band and red edge-based SVIs to LNC is investigated over different crop types. By using data from experimental LNC values over 4 different crop types and image data acquired using the Compact Airborne Spectrographic Imager (CASI) sensor, the extensive dataset allowed us to evaluate broad-band and red edge-based SVIs. The result indicated that NDVI performed the best among the selected SVIs while red edge-based SVIs didn’t show the potential for estimating the LNC based on the CASI data due to the spectral resolution. In order to search for the optimal SVIs, the band combination algorithm has been used in this work. The best linear correlation against the experimental LNC dataset was obtained by combining the 626.20nm and 569.00nm wavebands. These wavelengths correspond to the maximal chlorophyll absorption and reflection position region, respectively, and are known to be sensitive to the physiological status of the plant. Then this linear relationship was applied to the CASI image for generating an LNC map, which can guide farmers in the accurate application of their N fertilization strategies.

Monitoring crop N status by using red edge-based indices

2017 ◽  
Vol 8 (2) ◽  
pp. 338-342 ◽  
Author(s):  
J. González-Piqueras ◽  
H. Lopez-Corcoles ◽  
S. Sánchez ◽  
J. Villodre ◽  
V. Bodas ◽  
...  
Keyword(s):  
Vegetation Indices ◽  
Growth Cycle ◽  
N Content ◽  
Spectral Bands ◽  
Red Edge ◽  
Use Efficiency ◽  
Edge Based ◽  
Linear Correlations ◽  
N Status ◽  
Sentinel 2

Intensive agriculture has the objective to increase nutrients use efficiency. Nitrogen (N) is a key nutrient for crops and the estimations of crop N status allow adjusting the fertilization levels to crop requirements, while reducing the environmental costs and optimizing the benefits for farmers. In this work the N status of wheat in a commercial plot has been monitored, varying the N supply taking into account the variability of the soil. The N content in the cover has been monitored simultaneously by sampling at field level and by using vegetation indices based on reflectance in the red-edge band. The results of the field campaign along a crop growth cycle show that the REP, MTCI, AIVI and CCCI calculated from narrow spectral bands show good linear correlations (R2>0.93) with respect to N content (g·m−2). These indices are stable when passing to broad bands as the case of Sentinel 2 with R2>0.9.

Using portable RapidSCAN active canopy sensor for rice nitrogen status diagnosis

2017 ◽  
Vol 8 (2) ◽  
pp. 349-352 ◽  
Author(s):  
J. Lu ◽  
Y. Miao ◽  
W. Shi ◽  
J. Li ◽  
J. Wan ◽  
...  
Keyword(s):  
Northeast China ◽  
Vegetation Index ◽  
Normalized Difference Vegetation Index ◽  
Stem Elongation ◽  
Vegetation Indices ◽  
Sanjiang Plain ◽  
N Nutrition ◽  
Red Edge ◽  
Edge Based ◽  
On Farm

The objective of this study was to determine how much improvement red edge-based vegetation indices (VIs) obtained with the RapidSCAN sensor would achieve for estimating rice nitrogen (N) nutrition index (NNI) at stem elongation stage (SE) as compared with commonly used normalized difference vegetation index (NDVI) and ratio vegetation index (RVI) in Northeast China. Sixteen plot experiments and seven on-farm experiments were conducted from 2014 to 2016 in Sanjiang Plain, Northeast China. The results indicated that the performance of red edge-based VIs for estimation of rice NNI was better than NDVI and RVI. N sufficiency index calculated with RapidSCAN VIs (NSI_VIs) (R2=0.43–0.59) were more stable and more strongly related to NNI than the corresponding VIs (R2=0.12–0.38).

Sentinel-2 based prediction of spruce budworm defoliation using red-edge spectral vegetation indices

2020 ◽  
Vol 11 (8) ◽  
pp. 777-786 ◽  
Author(s):  
Rajeev Bhattarai ◽  
Parinaz Rahimzadeh-Bajgiran ◽  
Aaron Weiskittel ◽  
David A. MacLean
Keyword(s):  
Vegetation Indices ◽  
Spruce Budworm ◽  
Red Edge ◽  
Spectral Vegetation Indices ◽  
Sentinel 2

scholarly journals Assessing Leaf Biomass of Agave sisalana Using Sentinel-2 Vegetation Indices

2021 ◽  
Vol 13 (2) ◽  
pp. 233
Author(s):  
Ilja Vuorinne ◽  
Janne Heiskanen ◽  
Petri K. E. Pellikka
Keyword(s):  
Near Infrared ◽  
Vegetation Indices ◽  
Fibre Production ◽  
Additive Models ◽  
Leaf Biomass ◽  
Ground Vegetation ◽  
Perennial Crop ◽  
Red Edge ◽  
Agave Sisalana ◽  
Sentinel 2

Biomass is a principal variable in crop monitoring and management and in assessing carbon cycling. Remote sensing combined with field measurements can be used to estimate biomass over large areas. This study assessed leaf biomass of Agave sisalana (sisal), a perennial crop whose leaves are grown for fibre production in tropical and subtropical regions. Furthermore, the residue from fibre production can be used to produce bioenergy through anaerobic digestion. First, biomass was estimated for 58 field plots using an allometric approach. Then, Sentinel-2 multispectral satellite imagery was used to model biomass in an 8851-ha plantation in semi-arid south-eastern Kenya. Generalised Additive Models were employed to explore how well biomass was explained by various spectral vegetation indices (VIs). The highest performance (explained deviance = 76%, RMSE = 5.15 Mg ha−1) was achieved with ratio and normalised difference VIs based on the green (R560), red-edge (R740 and R783), and near-infrared (R865) spectral bands. Heterogeneity of ground vegetation and resulting background effects seemed to limit model performance. The best performing VI (R740/R783) was used to predict plantation biomass that ranged from 0 to 46.7 Mg ha−1 (mean biomass 10.6 Mg ha−1). The modelling showed that multispectral data are suitable for assessing sisal leaf biomass at the plantation level and in individual blocks. Although these results demonstrate the value of Sentinel-2 red-edge bands at 20-m resolution, the difference from the best model based on green and near-infrared bands at 10-m resolution was rather small.

scholarly journals Aboveground Biomass Mapping of Crops Supported by Improved CASA Model and Sentinel-2 Multispectral Imagery

2021 ◽  
Vol 13 (14) ◽  
pp. 2755
Author(s):  
Peng Fang ◽  
Nana Yan ◽  
Panpan Wei ◽  
Yifan Zhao ◽  
Xiwang Zhang
Keyword(s):  
Aboveground Biomass ◽  
Production Management ◽  
Vegetation Index ◽  
Net Primary Productivity ◽  
Vegetation Indices ◽  
Biophysical Parameters ◽  
Edge Information ◽  
Casa Model ◽  
Red Edge ◽  
Biomass Mapping

The net primary productivity (NPP) and aboveground biomass mapping of crops based on remote sensing technology are not only conducive to understanding the growth and development of crops but can also be used to monitor timely agricultural information, thereby providing effective decision making for agricultural production management. To solve the saturation problem of the NDVI in the aboveground biomass mapping of crops, the original CASA model was improved using narrow-band red-edge information, which is sensitive to vegetation chlorophyll variation, and the fraction of photosynthetically active radiation (FPAR), NPP, and aboveground biomass of winter wheat and maize were mapped in the main growing seasons. Moreover, in this study, we deeply analyzed the seasonal change trends of crops’ biophysical parameters in terms of the NDVI, FPAR, actual light use efficiency (LUE), and their influence on aboveground biomass. Finally, to analyze the uncertainty of the aboveground biomass mapping of crops, we further discussed the inversion differences of FPAR with different vegetation indices. The results demonstrated that the inversion accuracies of the FPAR of the red-edge normalized vegetation index (NDVIred-edge) and red-edge simple ratio vegetation index (SRred-edge) were higher than those of the original CASA model. Compared with the reference data, the accuracy of aboveground biomass estimated by the improved CASA model was 0.73 and 0.70, respectively, which was 0.21 and 0.13 higher than that of the original CASA model. In addition, the analysis of the FPAR inversions of different vegetation indices showed that the inversion accuracies of the red-edge vegetation indices NDVIred-edge and SRred-edge were higher than those of the other vegetation indices, which confirmed that the vegetation indices involving red-edge information can more effectively retrieve FPAR and aboveground biomass of crops.

scholarly journals Estimating and Monitoring Land Surface Phenology in Rangelands: A Review of Progress and Challenges

2021 ◽  
Vol 13 (11) ◽  
pp. 2060
Author(s):  
Trylee Nyasha Matongera ◽  
Onisimo Mutanga ◽  
Mbulisi Sibanda ◽  
John Odindi
Keyword(s):  
Land Surface ◽  
Vegetation Index ◽  
Critical Role ◽  
Spectral Characteristics ◽  
Vegetation Indices ◽  
Machine Learning Algorithms ◽  
Special Focus ◽  
Land Surface Phenology ◽  
Spectral Vegetation Indices ◽  
Satellite Sensors

Land surface phenology (LSP) has been extensively explored from global archives of satellite observations to track and monitor the seasonality of rangeland ecosystems in response to climate change. Long term monitoring of LSP provides large potential for the evaluation of interactions and feedbacks between climate and vegetation. With a special focus on the rangeland ecosystems, the paper reviews the progress, challenges and emerging opportunities in LSP while identifying possible gaps that could be explored in future. Specifically, the paper traces the evolution of satellite sensors and interrogates their properties as well as the associated indices and algorithms in estimating and monitoring LSP in productive rangelands. Findings from the literature revealed that the spectral characteristics of the early satellite sensors such as Landsat, AVHRR and MODIS played a critical role in the development of spectral vegetation indices that have been widely used in LSP applications. The normalized difference vegetation index (NDVI) pioneered LSP investigations, and most other spectral vegetation indices were primarily developed to address the weaknesses and shortcomings of the NDVI. New indices continue to be developed based on recent sensors such as Sentinel-2 that are characterized by unique spectral signatures and fine spatial resolutions, and their successful usage is catalyzed with the development of cutting-edge algorithms for modeling the LSP profiles. In this regard, the paper has documented several LSP algorithms that are designed to provide data smoothing, gap filling and LSP metrics retrieval methods in a single environment. In the future, the development of machine learning algorithms that can effectively model and characterize the phenological cycles of vegetation would help to unlock the value of LSP information in the rangeland monitoring and management process. Precisely, deep learning presents an opportunity to further develop robust software packages such as the decomposition and analysis of time series (DATimeS) with the abundance of data processing tools and techniques that can be used to better characterize the phenological cycles of vegetation in rangeland ecosystems.

Effect of multi-level and multi-scale spectral data source on vineyard state assessment via spectral vegetation indices

2021 ◽  
Author(s):  
Antonello Bonfante ◽  
Arturo Erbaggio ◽  
Eugenia Monaco ◽  
Rossella Albrizio ◽  
Pasquale Giorio ◽  
...  
Keyword(s):  
Climate Change ◽  
Data Collection ◽  
State Estimation ◽  
Spectral Data ◽  
Spatial Resolution ◽  
Spectral Region ◽  
Vegetation Indices ◽  
Plant Management ◽  
Spectral Vegetation Indices ◽  
Data Source

<p>Currently, the main goal of agriculture is to promote the resilience of agricultural systems in a sustainable way through the improvement of use efficiency of farm resources, increasing crop yield and quality, under climate change conditions. Climate change is one of the major challenges for high incomes crops, as the vineyards for high-quality wines, since it is expected to drastically modify plant growth, with possible negative effects especially in arid and semi-arid regions of Europe. In this context, the reduction of negative environmental impacts of intensive agriculture (e.g. soil degradation), can be realized by means of high spatial and temporal resolution of field crop monitoring, aiming to manage the local spatial variability.</p><p>The monitoring of spatial behaviour of plants during the growing season represents an opportunity to improve the plant management, the farmer incomes and to preserve the environmental health, but it represents an additional cost for the farmer.</p><p>The UAS-based imagery might provide detailed and accurate information across visible and near infrared spectral regions to support monitoring (crucial for precision agriculture) with limitation in bands and then on spectral vegetation indices (Vis) provided. VIs are a well-known and widely used method for crop state estimation. The ability to monitor crop state by such indices is an important tool for agricultural management. While differences in imagery and point-based spectroscopy are obvious, their impact on crop state estimation by VIs is not well-studied. The aim of this study was to assess the performance level of the selected VIs calculated from reconstructed high-resolution satellite (Sentinel-2A) multispectral imagery (13 bands across 400-2500nm with spatial resolution of <2m) through Convolutional Neural Network (CNN) approach (Brook et al., 2020), UAS-based multispectral (5 bands across 450-800nm spectral region with spatial resolution of 5cm) imagery and point-based field spectroscopy (collecting 600 wavelength across  400-1000nm spectral region with a surface footprint of 1-2cm) in application to crop state estimation.</p><p>The test site is a portion of vineyard placed in southern Italy cultivated on Greco cultivar, in which the soil-plant and atmosphere system has been monitored during the 2020 vintage also through ecophysiological analyses. The data analysis will follow the methodology presented in a recently published paper (Polinova et al., 2018).</p><p>The study will connect the method and scale of spectral data collection with in vivo plant monitoring and prove that it has a significant impact on the vegetation state estimation results. It should be noted that each spectral data source has its advantages and drawbacks. The plant parameter of interest should determine not only the VIs type suitable for analysis but also the method of data collection.</p><p>The contribution has been realized within the CNR BIO-ECO project.</p>

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