scholarly journals LINKING PHOTOSYNTHETIC LIGHT USE EFFICIENCY AND OPTICAL VEGETATION ACTIVE INDICATORS: IMPLICATIONS FOR GROSS PRIMARY PRODUCTION ESTIMATION BY REMOTE SENSING

2020 ◽  
Vol V-3-2020 ◽  
pp. 571-578
Author(s):  
S. Wang ◽  
Z. Li ◽  
Y. Zhang ◽  
D. Yang ◽  
C. Ni
Keyword(s):  
Remote Sensing ◽  
Primary Productivity ◽  
Seasonal Changes ◽  
Gross Primary Production ◽  
Vegetation Indices ◽  
Canopy Structure ◽  
Remote Sensing Data ◽  
Growing Season ◽  
Light Use Efficiency ◽  
Use Efficiency

Abstract. Over the last 40 years, the light use efficiency (LUE) model has become a popular approach for gross primary productivity (GPP) estimation in the carbon and remote sensing communities. Despite the fact that the LUE model provides a simple but effective way to approximate GPP at ecosystem to global scales from remote sensing data, when implemented in real GPP modelling, however, the practical form of the model can vary. By reviewing different forms of LUE model and their performances at ecosystem to global scales, we conclude that the relationships between LUE and optical vegetation active indicators (OVAIs, including vegetation indices and sun-induced chlorophyll fluorescence-based products) across time and space are key for understanding and applying the LUE model. In this work, the relationships between LUE and OVAIs are investigated at flux-tower scale, using both remotely sensed and simulated datasets. We find that i) LUE-OVAI relationships during the season are highly site-dependent, which is complexed by seasonal changes of leaf pigment concentration, canopy structure, radiation and Vcmax; ii) LUE tends to converge during peak growing season, which enables applying pure OVAI-based LUE models without specifically parameterizing LUE and iii) Chlorophyll-sensitive OVAIs, especially machine-learning-based SIF-like signal, exhibits a potential to represent spatial variability of LUE during the peak growing season.We also show the power of time-series model simulations to improve the understanding of LUE-OVAI relationships at seasonal scale.

scholarly journals Estimation of Gross Primary Productivity (GPP) Phenology of a Short-Rotation Plantation Using Remotely Sensed Indices Derived from Sentinel-2 Images

2020 ◽  
Vol 12 (13) ◽  
pp. 2104
Author(s):  
Maral Maleki ◽  
Nicola Arriga ◽  
José Miguel Barrios ◽  
Sebastian Wieneke ◽  
Qiang Liu ◽  
...  
Keyword(s):  
Primary Productivity ◽  
Seasonal Pattern ◽  
Vegetation Indices ◽  
Canopy Structure ◽  
Growing Season ◽  
Gross Primary Productivity ◽  
Imaging Spectrometer ◽  
Short Rotation ◽  
Medium Resolution ◽  
Sentinel 2

This study aimed to understand which vegetation indices (VIs) are an ideal proxy for describing phenology and interannual variability of Gross Primary Productivity (GPP) in short-rotation coppice (SRC) plantations. Canopy structure- and chlorophyll-sensitive VIs derived from Sentinel-2 images were used to estimate the start and end of the growing season (SOS and EOS, respectively) during the period 2016–2018, for an SRC poplar (Populus spp.) plantation in Lochristi (Belgium). Three different filtering methods (Savitzky–Golay (SavGol), polynomial (Polyfit) and Harmonic Analysis of Time Series (HANTS)) and five SOS- and EOS threshold methods (first derivative function, 10% and 20% percentages and 10% and 20% percentiles) were applied to identify the optimal methods for the determination of phenophases. Our results showed that the MEdium Resolution Imaging Spectrometer (MERIS) Terrestrial Chlorophyll Index (MTCI) had the best fit with GPP phenology, as derived from eddy covariance measurements, in identifying SOS- and EOS-dates. For SOS, the performance was only slightly better than for several other indices, whereas for EOS, MTCI performed markedly better. The relationship between SOS/EOS derived from GPP and VIs varied interannually. MTCI described best the seasonal pattern of the SRC plantation’s GPP (R2 = 0.52 when combining all three years). However, during the extreme dry year 2018, the Chlorophyll Red Edge Index performed slightly better in reproducing growing season GPP variability than MTCI (R2 = 0.59; R2 = 0.49, respectively). Regarding smoothing functions, Polyfit and HANTS methods showed the best (and very similar) performances. We further found that defining SOS as the date at which the 10% or 20% percentile occurred, yielded the best agreement between the VIs and the GPP; while for EOS the dates of the 10% percentile threshold came out as the best.

scholarly journals Improving Potato Yield Prediction by Combining Cultivar Information and UAV Remote Sensing Data Using Machine Learning

2021 ◽  
Vol 13 (16) ◽  
pp. 3322
Author(s):  
Dan Li ◽  
Yuxin Miao ◽  
Sanjay K. Gupta ◽  
Carl J. Rosen ◽  
Fei Yuan ◽  
...  
Keyword(s):  
Machine Learning ◽  
Remote Sensing ◽  
Vegetation Indices ◽  
Remote Sensing Data ◽  
Growing Season ◽  
Potato Yield ◽  
Yield Variability ◽  
Yield Prediction ◽  
Learning Models ◽  
Machine Learning Models

Accurate high-resolution yield maps are essential for identifying spatial yield variability patterns, determining key factors influencing yield variability, and providing site-specific management insights in precision agriculture. Cultivar differences can significantly influence potato (Solanum tuberosum L.) tuber yield prediction using remote sensing technologies. The objective of this study was to improve potato yield prediction using unmanned aerial vehicle (UAV) remote sensing by incorporating cultivar information with machine learning methods. Small plot experiments involving different cultivars and nitrogen (N) rates were conducted in 2018 and 2019. UAV-based multi-spectral images were collected throughout the growing season. Machine learning models, i.e., random forest regression (RFR) and support vector regression (SVR), were used to combine different vegetation indices with cultivar information. It was found that UAV-based spectral data from the early growing season at the tuber initiation stage (late June) were more correlated with potato marketable yield than the spectral data from the later growing season at the tuber maturation stage. However, the best performing vegetation indices and the best timing for potato yield prediction varied with cultivars. The performance of the RFR and SVR models using only remote sensing data was unsatisfactory (R2 = 0.48–0.51 for validation) but was significantly improved when cultivar information was incorporated (R2 = 0.75–0.79 for validation). It is concluded that combining high spatial-resolution UAV images and cultivar information using machine learning algorithms can significantly improve potato yield prediction than methods without using cultivar information. More studies are needed to improve potato yield prediction using more detailed cultivar information, soil and landscape variables, and management information, as well as more advanced machine learning models.

scholarly journals Soil Water Content and High-Resolution Imagery for Precision Irrigation: Maize Yield

Agronomy ◽  
2019 ◽  
Vol 9 (4) ◽  
pp. 174 ◽  
Author(s):  
Alfonso de Lara ◽  
Louis Longchamps ◽  
Raj Khosla
Keyword(s):  
Remote Sensing ◽  
Grain Yield ◽  
Soil Water ◽  
Vegetation Indices ◽  
Remote Sensing Data ◽  
Maize Yield ◽  
Maize Grain Yield ◽  
Use Efficiency ◽  
Maize Grain ◽  
Precision Irrigation

Improvement in water use efficiency of crops is a key component in addressing the increasing global water demand. The time and depth of the soil water monitoring are essential when defining the amount of water to be applied to irrigated crops. Precision irrigation (PI) is a relatively new concept in agriculture, and it provides a vast potential for enhancing water use efficiency, while maintaining or increasing grain yield. Neutron probes (NPs) have consistently been used as a robust and accurate method to estimate soil water content (SWC). Remote sensing derived vegetation indices have been successfully used to estimate variability of Leaf Area Index and biomass, which are related to root water uptake. Crop yield has not been evaluated on a basis of SWC, as explained by NPs in time and at different depths. The objectives of this study were (1) to determine the optimal time and depth of SWC and its relationship to maize grain yield (2) to determine if satellite-derived vegetation indices coupled with SWC could further improve the relationship between maize grain yield and SWC. Soil water and remote sensing data were collected throughout the crop season and analyzed. The results from the automated model selection of SWC readings, used to assess maize yield, consistently selected three dates spread around reproductive growth stages for most depths (p value < 0.05). SWC readings at the 90 cm depth had the highest correlation with maize yield, followed closely by the 120 cm. When coupled with remote sensing data, models improved by adding vegetation indices representing the crop health status at V9, right before tasseling. Thus, SWC monitoring at reproductive stages combined with vegetation indices could be a tool for improving maize irrigation management.

Seasonal changes in photochemistry, light use efficiency and net photosynthetic rates of wild blueberry (Vaccinium angustifolium Ait.)

2012 ◽  
Vol 92 (6) ◽  
pp. 1135-1143 ◽  
Author(s):  
David Percival ◽  
Jatinder Kaur ◽  
Lindsay J. Hainstock ◽  
Jean-Pierre Privé
Keyword(s):  
Chlorophyll Fluorescence ◽  
Chlorophyll A ◽  
Seasonal Changes ◽  
Growing Season ◽  
Light Use Efficiency ◽  
Vaccinium Angustifolium ◽  
Carotenoid Concentration ◽  
Chlorophyll A And B ◽  
Use Efficiency ◽  
Wild Blueberry

Percival, D., Kaur, J., Hainstock, L. J. and Privé, J.-P. 2012. Seasonal changes in photochemistry, light use efficiency and net photosynthetic rates of wild blueberry (Vaccinium angustifolium Ait.). Can. J. Plant Sci. 92: 1135–1143. The seasonal variation of carotenoid concentration, chlorophyll a and b levels, dark- (Fv/Fm) and light-adapted (Fv′/Fm′) variable to maximal chlorophyll fluorescence (an indication of the quantum efficiency of PSII photochemistry) and net photosynthesis of the wild blueberry (Vaccinium angustifolium Ait.) was examined in the vegetative and cropping phases of production. Chlorophyll levels ranged from 2.0 to 12 µg cm−2 and from 0.042 to 1.4 µg cm−2 for chlorophyll a and b, respectively, were significantly lower in the cropping phase of production, and were also lower in the latter stages of the growing season. Similarly, carotenoid concentrations ranged from 0.67 to 4.1 µg cm−2 and were lower in the cropping phase of production. However, carotenoid concentration and dark- and light-adapted variable to maximal chlorophyll fluorescence (Fv/Fm and Fv′/Fm′) decreased marketably at the mid-point of the growing season, presumably as a result of photoinhibition. Net photosynthetic values of upright stems ranged from 2.1 to 7.6 µmol m−2 s−1, were substantially higher in the vegetative phase of production and also decreased significantly in the latter part of the growing season. Results from this investigation indicate that the wild blueberry has a relatively low photosynthetic rate, which may be prone to photoinhibition and limited carbohydrate supply (i.e., source) when compared with other temperate fruit crops.

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