Soybean crop coverage estimation from NDVI images with different spatial resolution to evaluate yield variability in a plot

Casa, Antonio de la; Ovando, Gustavo; Bressanini, Luciano; Martínez, Jorge Martínez; Díaz, Guillermo J.; Miranda, C.

doi:10.1016/j.isprsjprs.2018.10.018

Cited by 53 publications

(32 citation statements)

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“…For example, wheat ear density estimates using a digital red-green-blue (RGB) camera were similar to estimates made using traditional methodologies [5]. Cameras integrating RGB and near infrared (NIR) sensors have been used to generate vegetation indices (VIs) such as Normalized Difference Vegetation Index (NDVI) [6,7,8] and Normalized Difference Red Edge (NDRE) to assess crop status [9,10,11]. The combination of RGB bands to compute Green-Red Vegetation Index (GRVI) can also be used for biomass estimates [12,13], yield monitoring [14], and canopy volume estimates [15].…”

Section: Introductionmentioning

confidence: 99%

Phenotyping of Plant Biomass and Performance Traits Using Remote Sensing Techniques in Pea (Pisum sativum, L.)

Vargas

Zhang

Smitchger

et al. 2019

Sensors

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Field pea cultivars are constantly improved through breeding programs to enhance biotic and abiotic stress tolerance and increase seed yield potential. In pea breeding, the Above Ground Biomass (AGBM) is assessed due to its influence on seed yield, canopy closure, and weed suppression. It is also the primary yield component for peas used as a cover crop and/or grazing. Measuring AGBM is destructive and labor-intensive process. Sensor-based phenotyping of such traits can greatly enhance crop breeding efficiency. In this research, high resolution RGB and multispectral images acquired with unmanned aerial systems were used to assess phenotypes in spring and winter pea breeding plots. The Green Red Vegetation Index (GRVI), Normalized Difference Vegetation Index (NDVI), Normalized Difference Red Edge Index (NDRE), plot volume, canopy height, and canopy coverage were extracted from RGB and multispectral information at five imaging times (between 365 to 1948 accumulated degree days/ADD after 1 May) in four winter field pea experiments and at three imaging times (between 1231 to 1648 ADD) in one spring field pea experiment. The image features were compared to ground-truth data including AGBM, lodging, leaf type, days to 50% flowering, days to physiological maturity, number of the first reproductive node, and seed yield. In two of the winter pea experiments, a strong correlation between image features and seed yield was observed at 1268 ADD (flowering). An increase in correlation between image features with the phenological traits such as days to 50% flowering and days to physiological maturity was observed at about 1725 ADD in these winter pea experiments. In the spring pea experiment, the plot volume estimated from images was highly correlated with ground truth canopy height (r = 0.83) at 1231 ADD. In two other winter pea experiments and the spring pea experiment, the GRVI and NDVI features were significantly correlated with AGBM at flowering. When selected image features were used to develop a least absolute shrinkage and selection operator model for AGBM estimation, the correlation coefficient between the actual and predicted AGBM was 0.60 and 0.84 in the winter and spring pea experiments, respectively. A SPOT-6 satellite image (1.5 m resolution) was also evaluated for its applicability to assess biomass and seed yield. The image features extracted from satellite imagery showed significant correlation with seed yield in two winter field pea experiments, however, the trend was not consistent. In summary, the study supports the potential of using unmanned aerial system-based imaging techniques to estimate biomass and crop performance in pea breeding programs.

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Section: Introductionmentioning

confidence: 99%

Phenotyping of Plant Biomass and Performance Traits Using Remote Sensing Techniques in Pea (Pisum sativum, L.)

Vargas

Zhang

Smitchger

et al. 2019

Sensors

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“…Because of its long-term series span and large area coverage, remote sensing data has become a common and vitally important data source in the field of vegetation dynamics monitoring (Casa et al, 2018). The normalized difference vegetation index (NDVI) is widely used as a parameter of vegetation dynamics and refers to the quantitative values of vegetation conditions.…”

Section: Introductionmentioning

confidence: 99%

Vegetation dynamic analysis based on multisource remote sensing data in the east margin of the Qinghai-Tibet Plateau, China

Wang

Peng

Xiangdong

et al. 2019

PeerJ

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This study focuses on the vegetation dynamic caused by global environmental change in the eastern margin of the Qinghai-Tibet Plateau (EMQTP). The Qinghai–Tibet Plateau (QTP) is one of the most sensitive areas responding to global environmental change, particularly global climate change, and has been recognized as a hotspot for coupled studies on changes in global terrestrial ecosystems and global climates. An important component of terrestrial ecosystems, vegetation dynamic has become a key issue in global environmental change, and numerous case studies have been conducted on vegetation dynamic trends using multi-source data and multi-scale methods across different study periods. The EMQTP is regarded as a transitional area located between the QTP and the Sichuan basin, and has special geographical and climatic conditions. Although this area is ecologically fragile and sensitive to climate change, few studies about vegetation dynamics have been carried out in this area. Thus, in this study, we used long-term series datasets of GIMMS 3g NDVI and VGT/PROBA-V NDVI to analyze the vegetation dynamics and phenological changes from 1982 to 2018. Validation was performed based on Landsat NDVI and Vegetation Index & Phenology (VIP) data. The results reveal that the year 1998 was a vital turning point in the start of growing season (SGS) in vegetation ecosystems. Before this turning point, the SGS had an average slope of 9.2 days/decade, and after, the average slope was 3.9 days/decade. The length of growing season (LGS) was slightly prolonged between 1982 to 2015. Additionally, the largest national alpine wetland grassland experienced significant vegetation degradation; in autumn, the degraded area accounted for 63.4%. Vegetation degradation had also appeared in the arid valleys of the Yalong River and the Jinsha River. Through validation analysis, we found that the main causes of vegetation degradation are the natural degradation of wetland grassland and human activities, specifically agricultural development and residential area expansion.

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“…Using photogrammetric processes, the soil coverage and canopy structure among others can be determined. This method can be implemented using a UAV with two digital cameras [19,20]. In some studies, the weight of the plant biomass is estimated from the coverage percentage but it is very sensitive to the structure of the plant.…”

Section: Introductionmentioning

confidence: 99%

Development and Validation of a LiDAR Scanner for 3D Evaluation of Soil Vegetal Coverage

et al. 2020

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Water and wind erosion are serious problems due to the loss of soil productivity. The coverage of soil, by means of cover crops or crops residues, is an effective tool to prevent wind and water erosion. The soil coverage could curb wind on the surface, avoid water runoff and reduce direct soil evaporation. Residue spatial distribution is the main factor to successful soil protection. The current work presents details of a prototype, design and validation as a measuring instrument to sense the height of vegetal crop residues based on a short-ranged laser distance sensor (LiDAR) and a computer numerical control (CNC) mechanism. The results obtained in this work showed a high level of confidence to estimate the height and composition of soil vegetal coverage.

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Soybean crop coverage estimation from NDVI images with different spatial resolution to evaluate yield variability in a plot

Cited by 53 publications

References 50 publications

Phenotyping of Plant Biomass and Performance Traits Using Remote Sensing Techniques in Pea (Pisum sativum, L.)

Phenotyping of Plant Biomass and Performance Traits Using Remote Sensing Techniques in Pea (Pisum sativum, L.)

Vegetation dynamic analysis based on multisource remote sensing data in the east margin of the Qinghai-Tibet Plateau, China

Development and Validation of a LiDAR Scanner for 3D Evaluation of Soil Vegetal Coverage

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