scholarly journals Estimation of Crop Growth Parameters Using UAV-Based Hyperspectral Remote Sensing Data

Sensors ◽  
2020 ◽  
Vol 20 (5) ◽  
pp. 1296 ◽  
Author(s):  
Huilin Tao ◽  
Haikuan Feng ◽  
Liangji Xu ◽  
Mengke Miao ◽  
Huiling Long ◽  
...  
Keyword(s):  
Growth Parameters ◽  
Vegetation Indices ◽  
Remote Sensing Data ◽  
Crop Growth ◽  
Growth Stages ◽  
Complex Data ◽  
Least Squares Regression ◽  
Area Index ◽  
Red Edge ◽  
Highly Correlated

Above-ground biomass (AGB) and the leaf area index (LAI) are important indicators for the assessment of crop growth, and are therefore important for agricultural management. Although improvements have been made in the monitoring of crop growth parameters using ground- and satellite-based sensors, the application of these technologies is limited by imaging difficulties, complex data processing, and low spatial resolution. Therefore, this study evaluated the use of hyperspectral indices, red-edge parameters, and their combination to estimate and map the distributions of AGB and LAI for various growth stages of winter wheat. A hyperspectral sensor mounted on an unmanned aerial vehicle was used to obtain vegetation indices and red-edge parameters, and stepwise regression (SWR) and partial least squares regression (PLSR) methods were used to accurately estimate the AGB and LAI based on these vegetation indices, red-edge parameters, and their combination. The results show that: (i) most of the studied vegetation indices and red-edge parameters are significantly highly correlated with AGB and LAI; (ii) overall, the correlations between vegetation indices and AGB and LAI, respectively, are stronger than those between red-edge parameters and AGB and LAI, respectively; (iii) Compared with the estimations using only vegetation indices or red-edge parameters, the estimation of AGB and LAI using a combination of vegetation indices and red-edge parameters is more accurate; and (iv) The estimations of AGB and LAI obtained using the PLSR method are superior to those obtained using the SWR method. Therefore, combining vegetation indices with red-edge parameters and using the PLSR method can improve the estimation of AGB and LAI.

An efficient and accurate method for obtaining regional scale rice growth conditions based on WOFOST model and satellite images

2021 ◽  
Author(s):  
Bingyu Zhao ◽  
Meiling Liu ◽  
Jiianjun Wu ◽  
Xiangnan Liu ◽  
Mengxue Liu ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Data Assimilation ◽  
Growth Model ◽  
Growth Parameters ◽  
Remote Sensing Data ◽  
Growth Conditions ◽  
Crop Growth ◽  
Crop Growth Model ◽  
Parallel Method ◽  
Rice Growth

<p>It is very important to obtain regional crop growth conditions efficiently and accurately in the agricultural field. The data assimilation between crop growth model and remote sensing data is a widely used method for obtaining vegetation growth information. This study aims to present a parallel method based on graphic processing unit (GPU) to improve the efficiency of the assimilation between RS data and crop growth model to estimate rice growth parameters. Remote sensing data, Landsat and HJ-1 images were collected and the World Food Studies (WOFOST) crop growth model which has a strong flexibility was employed. To acquire continuous regional crop parameters in temporal-spatial scale, particle swarm optimization (PSO) data assimilation method was used to combine remote sensing images and WOFOST and this process is accompanied by a parallel method based on the Compute Unified Device Architecture (CUDA) platform of NVIDIA GPU. With these methods, we obtained daily rice growth parameters of Zhuzhou City, Hunan, China and compared the efficiency and precision of parallel method and non-parallel method. Results showed that the parallel program has a remarkable speedup (reaching 240 times) compared with the non-parallel program with a similar accuracy. This study indicated that the parallel implementation based on GPU was successful in improving the efficiency of the assimilation between RS data and the WOFOST model and was conducive to obtaining regional crop growth conditions efficiently and accurately.</p>

scholarly journals Quantifying Uncertainty and Bridging the Scaling Gap in the Retrieval of Leaf Area Index by Coupling Sentinel-2 and UAV Observations

2020 ◽  
Vol 12 (11) ◽  
pp. 1843 ◽  
Author(s):  
Andrew Revill ◽  
Anna Florence ◽  
Alasdair MacArthur ◽  
Stephen Hoad ◽  
Robert Rees ◽  
...  
Keyword(s):  
Spatial Heterogeneity ◽  
Leaf Area Index ◽  
Leaf Area ◽  
Growth Stages ◽  
Two Stage ◽  
Area Index ◽  
Field Surveys ◽  
Red Edge ◽  
Calibration Approach ◽  
Sentinel 2

Leaf area index (LAI) estimates can inform decision-making in crop management. The European Space Agency’s Sentinel-2 satellite, with observations in the red-edge spectral region, can monitor crops globally at sub-field spatial resolutions (10–20 m). However, satellite LAI estimates require calibration with ground measurements. Calibration is challenged by spatial heterogeneity and scale mismatches between field and satellite measurements. Unmanned Aerial Vehicles (UAVs), generating high-resolution (cm-scale) LAI estimates, provide intermediary observations that we use here to characterise uncertainty and reduce spatial scaling discrepancies between Sentinel-2 observations and field surveys. We use a novel UAV multispectral sensor that matches Sentinel-2 spectral bands, flown in conjunction with LAI ground measurements. UAV and field surveys were conducted on multiple dates—coinciding with different wheat growth stages—that corresponded to Sentinel-2 overpasses. We compared chlorophyll red-edge index (CIred-edge) maps, derived from the Sentinel-2 and UAV platforms. We used Gaussian processes regression machine learning to calibrate a UAV model for LAI, based on ground data. Using the UAV LAI, we evaluated a two-stage calibration approach for generating robust LAI estimates from Sentinel-2. The agreement between Sentinel-2 and UAV CIred-edge values increased with growth stage—R2 ranged from 0.32 (stem elongation) to 0.75 (milk development). The CIred-edge variance between the two platforms was more comparable later in the growing season due to a more homogeneous and closed wheat canopy. The single-stage Sentinel-2 LAI calibration (i.e., direct calibration from ground measurements) performed poorly (mean R2 = 0.29, mean NRMSE = 17%) when compared to the two-stage calibration using the UAV data (mean R2 = 0.88, mean NRMSE = 8%). The two-stage approach reduced both errors and biases by >50%. By upscaling ground measurements and providing more representative model training samples, UAV observations provide an effective and viable means of enhancing Sentinel-2 wheat LAI retrievals. We anticipate that our UAV calibration approach to resolving spatial heterogeneity would enhance the retrieval accuracy of LAI and additional biophysical variables for other arable crop types and a broader range of vegetation cover types.

Red-Edge Band Vegetation Indices for Leaf Area Index Estimation From Sentinel-2/MSI Imagery

2020 ◽  
Vol 58 (2) ◽  
pp. 826-840 ◽  
Author(s):  
Yuanheng Sun ◽  
Qiming Qin ◽  
Huazhong Ren ◽  
Tianyuan Zhang ◽  
Shanshan Chen
Keyword(s):  
Leaf Area Index ◽  
Leaf Area ◽  
Vegetation Indices ◽  
Area Index ◽  
Red Edge ◽  
Index Estimation ◽  
Edge Band ◽  
Sentinel 2

scholarly journals VIS-NIR, Red-Edge and NIR-Shoulder Based Normalized Vegetation Indices Response to Co-Varying Leaf and Canopy Structural Traits in Heterogeneous Grasslands

2020 ◽  
Vol 12 (14) ◽  
pp. 2254 ◽  
Author(s):  
Hafiz Ali Imran ◽  
Damiano Gianelle ◽  
Duccio Rocchini ◽  
Michele Dalponte ◽  
M. Pilar Martín ◽  
...  
Keyword(s):  
Near Infrared ◽  
Spatial Scales ◽  
Vegetation Indices ◽  
Radiative Transfer Model ◽  
Transfer Model ◽  
Sharp Change ◽  
Full Potential ◽  
Area Index ◽  
Red Edge ◽  
Temporal And Spatial

Red-edge (RE) spectral vegetation indices (SVIs)—combining bands on the sharp change region between near infrared (NIR) and visible (VIS) bands—alongside with SVIs solely based on NIR-shoulder bands (wavelengths 750–900 nm) have been shown to perform well in estimating leaf area index (LAI) from proximal and remote sensors. In this work, we used RE and NIR-shoulder SVIs to assess the full potential of bands provided by Sentinel-2 (S-2) and Sentinel-3 (S-3) sensors at both temporal and spatial scales for grassland LAI estimations. Ground temporal and spatial observations of hyperspectral reflectance and LAI were carried out at two grassland sites (Monte Bondone, Italy, and Neustift, Austria). A strong correlation (R2 > 0.8) was observed between grassland LAI and both RE and NIR-shoulder SVIs on a temporal basis, but not on a spatial basis. Using the PROSAIL Radiative Transfer Model (RTM), we demonstrated that grassland structural heterogeneity strongly affects the ability to retrieve LAI, with high uncertainties due to structural and biochemical PTs co-variation. The RENDVI783.740 SVI was the least affected by traits co-variation, and more studies are needed to confirm its potential for heterogeneous grasslands LAI monitoring using S-2, S-3, or Gaofen-5 (GF-5) and PRISMA bands.

scholarly journals The Response of Maize Lines to Foliar Fertilizing

Agriculture ◽  
2020 ◽  
Vol 10 (9) ◽  
pp. 365
Author(s):  
Milan Brankov ◽  
Milena Simić ◽  
Željko Dolijanović ◽  
Miloš Rajković ◽  
Violeta Mandić ◽  
...  
Keyword(s):  
Amino Acids ◽  
Grain Yield ◽  
Leaf Area ◽  
Harvest Index ◽  
Growth Parameters ◽  
Growth Stages ◽  
Fresh Matter ◽  
Area Index ◽  
Positive Effects ◽  
The Impact

The objective of this study was to evaluate the impact of two foliar fertilizers applied on five maize (Zea mays L.) lines. Fertilizers were applied at different growth stages of maize, during three consecutive years (2010–2012) at the experimental field of the Maize Research Institute “Zemun Polje”, Serbia. Maize growth parameters such as fresh matter, height, leaf area and grain yield were recorded. Foliar fertilizer with amino acids (FAA) was more advantageous to maize plants compared to fertilizer containing phosphorus (FP) as a main component. Applied FAA has shown positive effects by increasing fresh matter, leaf area index, and plant height in all three years. In 2012, due to unfavorable meteorological conditions, grain yield and harvest index were very low, compared to the previous two years, although, positive effects on morphological traits were observed 21 days after treatments (DAT), as well as in the anthesis stage. The best results of 30% of grain yield and harvest index increase were recorded in line L1 in 2010 and 2011. The same line had an increase of more than 40% of fresh matter and leaf area on average for all three years. The positive effects that have been noticed in this research could recommend foliar fertilizing with fertilizer containing N in a form of an amino acids complex.

scholarly journals Effects of Gibberellic Acid (Ga3) on Growth and Yield Parameters of French Bean (Phaseolus Vulgaris L.)

2017 ◽  
Vol 43 (1) ◽  
pp. 49-60
Author(s):  
Ferdowsi Noor ◽  
Feroza Hossain ◽  
Umme Ara
Keyword(s):  
Growth Rate ◽  
Leaf Area ◽  
Dry Matter ◽  
Growth Parameters ◽  
French Bean ◽  
Research Field ◽  
Growth And Yield ◽  
Growth Stages ◽  
Area Index ◽  
Net Assimilation

A field study was conducted during the Rabi season of 2009-2010 in the research field of Bangladesh Council of Scientific and Industrial Research (BCSIR), Dhaka. Six levels of GA3, viz. 0, 30, 50, 70, 90 and 110 ppm were sprayed at 18 days after sowing (DAS). GA3 treatments significantly increased plant height than the control plants. GA3 with 30 to 90 ppm significantly increased number of branches and leaves, leaf area, leaf area index (LAI), leaf dry matter and total dry matter at different growth stages. GA3 at 30 to 70 ppm gradually increased crop growth rate (CGR), net assimilation rate (NAR) and relative growth rate (RGR) and declined advanced growth stages. Number of dry pods /plant, number of seeds /pod, 1000 seed weight, fresh fodder, fresh pod, dry seed yield and harvest index also significantly increased. Positive significant correlations were found among growth parameters and as well as yield contributing characters. Asiat. Soc. Bangladesh, Sci. 43(1): 49-60, June 2017

scholarly journals Influence of Tree Age and Variety on Allometric Characteristics and Water Use of Mangifera indica L. Growing in Plantation

2011 ◽  
Vol 2011 ◽  
pp. 1-8 ◽  
Author(s):  
Philip G. Oguntunde ◽  
Johnson T. Fasinmirin ◽  
Nick van de Giesen
Keyword(s):  
West Africa ◽  
Water Use ◽  
Sap Flow ◽  
Growth Parameters ◽  
Mangifera Indica ◽  
Xylem Sap ◽  
Tree Age ◽  
Area Index ◽  
Highly Correlated ◽  
Old Trees

Data on water relations and growth characteristics of mango trees needed for productive plantation management are currently lacking in West Africa. Relationships between allometric properties and water use in mango trees were examined. In addition, the effects on allometric characteristics and xylem sap flow were investigated in a mixed varieties plantation. Tree age explained more than 92% of the variation in stem diameter, over 96% of the variation in height, over 92% of the variation in crown diameter, and more than 97% of the variation in leaf area index of the 60 mango trees sampled. Water use increased from 1.01 kg d-1 to 156.7 kg d-1 from the 2- to the 33-year-old trees for a typical bright day. Sap flow was highly correlated with age under different sky conditions. A power function relating daily sap flow to age yielded an r2 of 0.98 for bright days and 0.87 when combined with rainy day data. The water use and growth parameters of the three cultivars were generally not significantly different. This paper has implications for mango productivity and for orchard water management in potentially dry areas of West Africa.

Sign in / Sign up

Export Citation Format

Share Document