Dependence of spectral characteristics on parameters describing CO2 exchange between crop species and the atmosphere

AbstractThe aim of this paper is to demonstrate that spectral vegetation indices are good indicators of parameters describing the intensity of CO2exchange between crops and the atmosphere. Measurements were conducted over 2011-2013 on plots of an experimental arable station on winter wheat, winter rye, spring barley, and potatoes. CO2fluxes were measured using the dynamic closed chamber system, while spectral vegetation indices were determined using SKYE multispectral sensors. Based on spectral data collected in 2011 and 2013, various models to estimate net ecosystem productivity and gross ecosystem productivity were developed. These models were then verified based on data collected in 2012. The R2for the best model based on spectral data ranged from 0.71 to 0.83 and from 0.78 to 0.92, for net ecosystem productivity and gross ecosystem productivity, respectively. Such high R2values indicate the utility of spectral vegetation indices in estimating CO2fluxes of crops. The effects of the soil background turned out to be an important factor decreasing the accuracy of the tested models.

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

Remote Sensing ◽

10.3390/rs13112060 ◽

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.

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

10.5194/egusphere-egu21-15815 ◽

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

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.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.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&#160; 400-1000nm spectral region with a surface footprint of 1-2cm) in application to crop state estimation.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).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.The contribution has been realized within the CNR BIO-ECO project.

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Estimating Nitrogen from Structural Crop Traits at Field Scale—A Novel Approach Versus Spectral Vegetation Indices

Remote Sensing ◽

10.3390/rs11172066 ◽

2019 ◽

Vol 11 (17) ◽

pp. 2066 ◽

Cited By ~ 3

Author(s):

Nora Tilly ◽

Georg Bareth

Keyword(s):

Spectral Data ◽

Spring Barley ◽

Vegetation Indices ◽

Structural Data ◽

Crop Growth ◽

Negative Consequences ◽

Field Scale ◽

Crop Height ◽

N Concentration ◽

Dry Biomass

A sufficient nitrogen (N) supply is mandatory for healthy crop growth, but negative consequences of N losses into the environment are known. Hence, deeply understanding and monitoring crop growth for an optimized N management is advisable. In this context, remote sensing facilitates the capturing of crop traits. While several studies on estimating biomass from spectral and structural data can be found, N is so far only estimated from spectral features. It is well known that N is negatively related to dry biomass, which, in turn, can be estimated from crop height. Based on this indirect link, the present study aims at estimating N concentration at field scale in a two-step model: first, using crop height to estimate biomass, and second, using the modeled biomass to estimate N concentration. For comparison, N concentration was estimated from spectral data. The data was captured on a spring barley field experiment in two growing seasons. Crop surface height was measured with a terrestrial laser scanner, seven vegetation indices were calculated from field spectrometer measurements, and dry biomass and N concentration were destructively sampled. In the validation, better results were obtained with the models based on structural data (R2 < 0.85) than on spectral data (R2 < 0.70). A brief look at the N concentration of different plant organs showed stronger dependencies on structural data (R2: 0.40–0.81) than on spectral data (R2: 0.18–0.68). Overall, this first study shows the potential of crop-specific across‑season two-step models based on structural data for estimating crop N concentration at field scale. The validity of the models for in-season estimations requires further research.

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Scaling photosynthetic function and CO2 dynamics from leaf to canopy level for maize – dataset combining diurnal and seasonal measurements of vegetation fluorescence, reflectance and vegetation indices with canopy gross ecosystem productivity

Data in Brief ◽

10.1016/j.dib.2021.107600 ◽

2021 ◽

pp. 107600

Author(s):

Petya Campbell ◽

Elizabeth Middleton ◽

Karl Huemmrich ◽

Lauren Ward ◽

Tommaso Julitta ◽

...

Keyword(s):

Vegetation Indices ◽

Ecosystem Productivity ◽

Gross Ecosystem Productivity ◽

Co2 Dynamics

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The Effect of Cover Crops on the Yield of Spring Barley in Estonia

Agriculture ◽

10.3390/agriculture9080172 ◽

2019 ◽

Vol 9 (8) ◽

pp. 172

Author(s):

Merili Toom ◽

Sirje Tamm ◽

Liina Talgre ◽

Ilmar Tamm ◽

Ülle Tamm ◽

...

Keyword(s):

Cover Crops ◽

Cover Crop ◽

Crop Production ◽

Nitrogen Availability ◽

Spring Barley ◽

Management Tool ◽

Winter Rye ◽

Hairy Vetch ◽

Hordeum Vulgare L ◽

Crop Species

Using cover crops in fallow periods of crop production is an important management tool for reducing nitrate leaching and therefore improving nitrogen availability for subsequent crops. We estimated the short-term effect of five cover crop species on the yield of successive spring barley (Hordeum vulgare L.) for two years in Estonia. The cover crop species used in the study were winter rye (Secale cereale L.), winter turnip rape (Brassica rapa spp. oleifera L.), forage radish (Raphanus sativus L. var. longipinnatus), hairy vetch (Vicia villosa Roth), and berseem clover (Trifolium alexandrinum L.). The results indicated that out of the five tested cover crops, forage radish and hairy vetch increased the yield of subsequent spring barley, whereas the other cover crops had no effect on barley yield. All cover crop species had low C:N ratios (11–17), suggesting that nitrogen (N) was available for barley early in the spring.

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Identification of season-dependent relationships between spectral vegetation indices and aboveground phytomass in alpine grassland by using field spectroscopy

Ekológia (Bratislava) ◽

10.2478/eko-2013-0016 ◽

2013 ◽

Vol 32 (2) ◽

Cited By ~ 1

Author(s):

Andrej Halabuk ◽

Katarina Gerhatova ◽

Frantisek Kohut ◽

Zuzana Ponecova ◽

Matej Mojses

Keyword(s):

Regression Models ◽

Vegetation Index ◽

Normalized Difference Vegetation Index ◽

Temporal Dynamics ◽

Spectral Characteristics ◽

Vegetation Indices ◽

Alpine Grassland ◽

Aboveground Phytomass ◽

Field Spectroscopy ◽

Spectral Vegetation Indices

AbstractHalabuk A., Gerhatova K., Kohut F., Ponecova Z., Mojses M.: Identification of season-dependent relationships between spectral vegetation indices and aboveground phytomass in alpine grassland by using field spectroscopy. Ekologia (Bratislava), Vol. 32, No. 2, p. 186-196, 2013.Spectral characteristics of alpine grasslands across the vegetation season (from May to September) are presented. The results are based on three year field spectroscopy monitoring of acid, nutrient poor grasslands at Kraľova hoľa research site, Low Tatras, Slovakia. Relationships between commonly used spectral vegetation indices (VIs) and field-based estimation of aboveground green phytomass (AG B) were analysed. Finally, season-dependent regression models were created in order to allow spatially extensive non-destructive monitoring of AG B. Spatial-temporal dynamics of background and standing litter markedly affect seasonal variations of relationships between VIs and AG B and predictability of the regression models. Because of a high proportion of litter during the whole season, this was a plant water-sensitive normalized difference water index (NDWI), which dominates as the predictive variable in the regression models across the whole season; except June, where chlorophyll absorption sensitive in normalized difference vegetation index (NDVI) performed the best (R2 = 0.57; rel. RMSE = 34%). However, the accuracy of the models was quite low (May: R2 = 0.45; rel. RMSE = 49%; July: R2 = 0.47; rel. RMSE = 26%; August: R2 = 0.13; rel. RMSE = 31%; September: R2 = 0.53; rel. RMSE = 40%).

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