Applying the NDVI from satellite images in delimiting management zones for annual crops

Damian, Júnior Melo; Pias, Osmar Henrique de Castro; Cherubin, Maurício Roberto; Fonseca, Alencar Zachi da; Fornari, Ezequiel Zibetti; Santi, Antônio Luis

doi:10.1590/1678-992x-2018-0055

Cited by 27 publications

(8 citation statements)

References 39 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Orbital images are commonly used in agriculture to identify spectral variations resulting from soil and crop characteristics at a large-scale, supporting diagnostics for agronomical crop parameters and helping farmers to make better management decisions. For example, over the years, orbital images were used to delimit management zones for annual crops [1], monitor within-field yield variability for many crops such as corn [2] and cotton [3], map vineyard variability [4], plan the wheat harvest [5], develop crop growth model [6], and map grasslands biomass [7,8], among others. Some of the main limitations related to orbital images are the lack of ground truth data (calibration) and the measurement accuracy of the agronomical variables [9].…”

Section: Introductionmentioning

confidence: 99%

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

et al. 2021

View full text Add to dashboard Cite

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.

show abstract

Section: Introductionmentioning

confidence: 99%

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

et al. 2021

View full text Add to dashboard Cite

show abstract

“…Engenharia Agrícola, Jaboticabal, v.40, n.6, p.759-768, nov./dec. 2020 Inaccurate NDVI data result, for example, in errors in estimates of vegetation density, its development and vigor, of plant biomass, phytosanitary status and productive potential of the crop, and delimitation of management zones in precision agriculture (Damian et al, 2020), in addition to inaccurate estimates for input application at variable rates, such as pesticides, irrigation, and topdressing nitrogen fertilization (Castro & Inamasu, 2014;Turra, 2016).…”

Section: Resultsmentioning

confidence: 99%

“…Doumit & Kiselevm (2017) demonstrated the use and potential of VIs for classifying RapidEye images for soybean, wheat, barley, and white oat crops. Damian et al (2020) used NDVI time series to delimit management zones in three areas of the State of Rio Grande do Sul, along with soybean, wheat, corn, and white oat yield maps. However, there are no studies on the spectral behavior and VIs for black oat.…”

Section: Introductionmentioning

confidence: 99%

Evaluation of the Radiometric Accuracy of Images Obtained by a Sequoia Multispectral Camera

et al. 2020

View full text Add to dashboard Cite

Geometric and radiometric corrections of images obtained by remotely piloted aircraft systems (RPAS) are required for the measurement of the spectral reflectance and calculation of vegetation indices. This study aimed to determine which radiometric calibration procedures allow obtaining suborbital images with more accurate reflectance data and spectral indices based on data collected by a Sequoia multispectral camera coupled to a RPAS on black oat cultivations at the flowering stage located in Eldorado do Sul, RS, Brazil. The orthophoto mosaics were processed without and with radiometric corrections by reflectance calibration ground target and/or solar irradiance sensor of the camera, and the radiometric accuracy of these images was evaluated using spectral reflectance ground data collected by the Crop Circle canopy sensor. The results indicated that the use of a reflectance calibration ground target is recommended to increase the radiometric accuracy of photographs even if the Sequoia solar irradiance sensor is employed. The use of the light gray target in the radiometric calibration resulted in orthophotos with the mean square error of reflectance values lower than 0.01 for all the analyzed bands and spectral indices.

show abstract

“…Published feedback is sparse on the quality of GIMMS NDVI 3g.v1 (1981–2015), which uses a new calibration approach and has a conservative measurement of uncertainty of about ±0.005 compared with SeaWiFS images, as reported by the authors (Pinzon & Tucker, 2014). NDVI images have been used in cluster analyses, most often at small scales and with predetermined numbers of clusters (i.e., k‐mean or related approaches; Damian et al, 2020; Romani et al, 2011) but sometimes at large scales (e.g., Mills et al, 2011; White, Hoffman, Hargrove, & Nemani, 2005) or hierarchically (Boone et al, 2000). Summaries of productivity or metrics representing phenology, or their changes through time, could be included in clustering, but I followed the most parsimonious approach, using only the raw GIMMS greenness profiles (Bunker, Tullis, Cothren, Casana, & Aly, 2016).…”

Section: Methodsmentioning

confidence: 99%

Hierarchical global plant biophysical regions as potential analysis units

Boone

2020

Global Change Biology

View full text Add to dashboard Cite

Regional and global vegetation simulations can be problematic when analysis units to which parameters are assigned do not align with plant productivity and phenology. Having a suite of predefined biophysical regions at a variety of scales that correspond to differences in plant productivity and phenology would allow analysts to select a set of analysis units at the scale needed. In other cases, environmental or social responses may be hypothesized to be related to differences in plant dynamics. One may compare the discrimination in such data that biophysical regions at different scales provide to determine which best distinguishes the responses in question, such that like responses fall within the same regions to the degree possible. If those relationships are significant, the responses may then be extrapolated based on the biophysical regions. I defined hierarchical biophysical regions based on plant productivity and phenology by clustering global 0.083 degree resolution normalized difference vegetation indices (NDVI) over a 10 year period. Agglomerative average‐linkage distances based on squared error between clusters were conducted using an iterative sampling approach to merge more than 2 million clusters into fewer and fewer clusters based on NDVI greenness profiles comprised of 240 values over 10 years, until all cells were in a single cluster. Greater and greater differences in greenness profiles were ignored at higher levels of the hierarchy. Using a difference increment of 0.1, 253 non‐duplicative sets of clusters were created, and 107 of those were included in animations that may be used to explore differences in global plant dynamics. Differences in clusters were quantified based on comparing the focal set of cluster results with 10 other cluster sets. Analysts may use the hierarchical clusters to improve the alignment of their parameter sets that inform plant growth and other dynamics with real‐world plant dynamics.

show abstract

Applying the NDVI from satellite images in delimiting management zones for annual crops

Cited by 27 publications

References 39 publications

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

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

Evaluation of the Radiometric Accuracy of Images Obtained by a Sequoia Multispectral Camera

Hierarchical global plant biophysical regions as potential analysis units

Contact Info

Product

Resources

About