Mapping of corn phenological stages using NDVI from OLI and MODIS sensors

Venâncio, Luan Peroni; Filgueiras, Roberto; Cunha, Fernando França da; Silva, Francisco Charles dos Santos; Santos, Robson Argolo dos; Mantovani, Everardo Chartuni

doi:10.5433/1679-0359.2020v41n5p1517

Cited by 7 publications

(5 citation statements)

References 42 publications

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“…Figure 2B shows the significant relationships between the different growth stages and NDVI values. The data corroborate those of Wang et al (2016), Alvino et al (2020 and Venancio et al (2020), to which the behavior of the NDVI obtained via satellite images throughout the development cycle of maize was evaluated. These authors found a rapid increase in NDVI values at the beginning of the cycle, thus confirming the results found between the stages V4 and V9.…”

Section: Resultssupporting

confidence: 87%

Vegetation sensors as a tool for plant population identification and corn grain yield estimation

Vian

Bredemeier

Drum

et al. 2021

Pesqui. Agropecu. Trop.

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The estimated corn grain yield is dependent on plant density and should be monitored from the beginning of its development, especially between the phenological stages V3 and V10, since these stages are more responsive to management strategies. This study aimed to evaluate the efficiency of two methods [normalized difference vegetation index (NDVI) and plant occupation index (POI)] to estimate the density of corn plants, in order to identify the plant population in different phenological stages and corn grain yield. Two field experiments were conducted in two crop seasons and treatments consisted of four plant densities (4, 6, 8 and 10 plants m-2). The NDVI measurements of the vegetative canopy were performed in the growth stages V4, V5, V6, V7, V8 and V9 (2014) and V3, V5, V6, V8, V9, V10 and V13 (2015/2016). For the POI, the measurements were performed in the stages V5, V6, V8 and V9, in both crop seasons. The different plant densities were efficient in generating variability in the NDVI and POI values throughout the corn crop development cycle, and both tools were efficient in identifying density variations. It was observed that these tools should be used between the V4 and V9 growth stages.

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Section: Resultssupporting

confidence: 87%

Vegetation sensors as a tool for plant population identification and corn grain yield estimation

Vian

Bredemeier

Drum

et al. 2021

Pesqui. Agropecu. Trop.

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show abstract

“…In addition, the type of input predictors for both models were evaluated to fit the predictive models using high-resolution data. The study demonstrates the potential of using spectral bands as an alternative approach to the common crop yield forecast based on VIs [23,[54][55][56]. The spatial variability of the fields was mapped for the range of yield data values across all fields over the sugarcane growing seasons.…”

Section: Discussionmentioning

confidence: 98%

Sugarcane Yield Mapping Using High-Resolution Imagery Data and Machine Learning Technique

et al. 2021

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Yield maps provide essential information to guide precision agriculture (PA) practices. Yet, on-board yield monitoring for sugarcane can be challenging. At the same time, orbital images have been widely used for indirect crop yield estimation for many crops like wheat, corn, and rice, but not for sugarcane. Due to this, the objective of this study is to explore the potential of multi-temporal imagery data as an alternative for sugarcane yield mapping. The study was based on developing predictive sugarcane yield models integrating time-series orbital imaging and a machine learning technique. A commercial sugarcane site was selected, and Sentinel-2 images were acquired from the beginning of the ratoon sprouting until harvesting of two consecutive cropping seasons. The predictive yield models RF (Random forest) and MLR (Multiple Linear Regression) were developed using orbital images and yield maps generated by a commercial sensor-system on harvesting. Original yield data were filtered and interpolated with the same spatial resolution of the orbital images. The entire dataset was divided into training and testing datasets. Spectral bands, especially the near-infrared at tillering crop stage showed greater contribution to predicting sugarcane yield than the use of derived spectral vegetation indices. The Root Mean Squared Error (RMSE) obtained for the RF regression based on multiple spectral bands was 4.63 Mg ha−1 with an R2 of 0.70 for the testing dataset. Overall, the RF regression had better performance than the MLR to predict sugarcane yield.

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“…Firstly, elevated values of the Normalized Difference Vegetation Index (NDVI) indicated enhanced photosynthetic capacity and the increased uptake of essential soil nutrients, such as nitrogen. Consequently, this was reflected in amplified grain production within corn crops, as demonstrated by previous studies [27][28][29][30]. Furthermore, similar outcomes could be attained by employing unmanned aerial vehicles (UAVs) equipped with high-resolution multispectral passive sensors for remote sensing.…”

Section: Discussionmentioning

confidence: 54%

Effects of Silicon Application on Yield, Spectral Index, and Fall Armyworm (Spodoptera frugiperda) Infestation on Maize (Zea mays) Crop

Galindo-Gutiérrez,

Garcés-Gómez

2023

AgriEngineering

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This paper presents the implementation of statistical and remote sensing techniques to analyze the spectral response, grain yield, and infestation of fall armyworm (Spodoptera frugiperda) in corn (Zea mays) based on the application of edaphic and foliar treatments with silicon, comparing the results with those reported in the literature where it has been demonstrated that the incorporation of this nutrient in different crops improves the activity of the enzyme nitrate reductase and dry matter weight gain. The results show that the foliar application of silicon tends to increase grain production in the crop, while the soil treatment does not improve yield. Similarly, foliar silicon application improves the Normalized Difference Vegetation Index, which improves plant health and could be correlated with higher grain yield of the crop. An inverse correlation was detected between the use of foliar silicon and the Normalized Difference Water Index and a direct relationship in the case of direct field application. As for the analysis of the data to verify the influence of the use of silicon on fall armyworm infestation, no statistically significant evidence was found that would lead to the conclusion that the application of this element, whether in soil or foliar form, could lead to a decrease in crop infestation.

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Mapping of corn phenological stages using NDVI from OLI and MODIS sensors

Abstract: Images from MODIS and OLI sensors were used. Corn phenology was evaluated through NDVI. Data were validated by model identity testing. NDVI can help identify problems in corn crops.

Cited by 7 publications

References 42 publications

Vegetation sensors as a tool for plant population identification and corn grain yield estimation

Vegetation sensors as a tool for plant population identification and corn grain yield estimation

Sugarcane Yield Mapping Using High-Resolution Imagery Data and Machine Learning Technique

Effects of Silicon Application on Yield, Spectral Index, and Fall Armyworm (Spodoptera frugiperda) Infestation on Maize (Zea mays) Crop

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