Effectiveness of using vegetation index to delineate zones of different soil and crop grain production characteristics

Basnyat, Prakash; McConkey, B.G.; Selles, F.; Meinert, L.B.

doi:10.4141/s04-065

Cited by 13 publications

(7 citation statements)

References 44 publications

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“…The RapidEye satellite system works with five spectral bands (blue, green, red, red edge, near infrared), where the near-infrared (NIR) is, in general, especially sensitive to the vigor of vegetation (Rees 2001;Basnyat et al 2005). The return frequency at nadir is 5.5 days and the spatial resolution is 6.5 m, resampled to 5 m. The images were made Precision Agric available through the RapidEye science Archive (RESA), where 74 radiometric calibrated and georeferenced scenes (Level 1B, Level 3A) could be found for field 100-01 (Table 1).…”

Section: Remote Sensing Datamentioning

confidence: 99%

“…When analyzing time series, satellite remote sensing is often more cost-effective and offers an archive of already acquired data by operating sensors. When it comes to determining MZ on the basis of actual crop growth patterns, satellite imagery applications are valuable tools in precision farming (Basnyat et al 2005). Compared to drone and aircraft-based images, as well as data from crop and soil sensors, most open-source and commercial multispectral remote sensing data has a coarser spatial resolution (centimeters versus meters).…”

Section: Introductionmentioning

confidence: 99%

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Automatic delineation algorithm for site-specific management zones based on satellite remote sensing data

et al. 2017

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In light of the increasing demand for food production, climate change challenges for agriculture, and economic pressure, precision farming is an ever-growing market. The development and distribution of remote sensing applications is also growing. The availability of extensive spatial and temporal data-enhanced by satellite remote sensing and open-source policies-provides an attractive opportunity to collect, analyze and use agricultural data at the farm scale and beyond. The division of individual fields into zones of differing yield potential (management zones (MZ)) is the basis of most offline and mapoverlay precision farming applications. In the process of delineation, manual labor is often required for the acquisition of suitable images and additional information on crop type. The authors therefore developed an automatic segmentation algorithm using multi-spectral satellite data, which is able to map stable crop growing patterns, reflecting areas of relative yield expectations within a field. The algorithm, using RapidEye data, is a quick and probably low-cost opportunity to divide agricultural fields into MZ, especially when yield data is insufficient or non-existent. With the increasing availability of satellite images, this method can address numerous users in agriculture and lower the threshold of implementing precision farming practices by providing a preliminary spatial field assessment.

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Section: Remote Sensing Datamentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Automatic delineation algorithm for site-specific management zones based on satellite remote sensing data

et al. 2017

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“…5,[21][22][23] Historically, such indices were computed from two to three wavelengths in order to maximise the extraction of data from a limited number of wavelengths, typically available from the early satellite images. However, the use of such indices are still frequently reported 5,24,25 and an often cited rationale for this is that vegetation indices give more stable and robust predictions of plant properties (i.e. under varying conditions).…”

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confidence: 99%

Prediction of Wheat Yield and Protein Using Remote Sensors on Plots—Part I: Assessing near Infrared Model Robustness for Year and Site Variations

Øvergaard

Isaksson

Korsæth

2013

Journal of Near Infrared Spectroscopy

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Validation of reflectance-based prediction models for plant properties is often performed on just one or two years of data. Hence, we aimed to perform a more comprehensive study regarding the validation of prediction models for grain yield and protein concentration. A FieldSpec3 portable field spectroradiometer was used to measure canopy reflectance in spring wheat. Spectral reflectance data were collected from three different experimental locations in up to four different years during the period 2007-2010, so that seven unique site years were included, comprising, altogether, 976 individual plots. Several datasets had moderate to severe lodging, which had a markedly negative influence on the prediction results. To correct for this problem, a classification model for the classes "lodging" and "standing crop" was calibrated from the spectral data. The model gave a total classification accuracy of 98.3%. Prediction models for grain yield and grain protein concentration were computed by means of the recent statistical method powered partial least squares (PPLS). Models were calibrated and validated on several combinations of the spectral datasets in order to reveal spatial and temporal effects on the prediction performance. The model performance generally increased with increasing variation in the calibration data, both in time (i.e. more years included) and space (i.e. more sites included). The best model for grain yield explained 94% [root mean square error of prediction (RMSEP) = 156 g m −2 ] of the variance and the predictions of grain protein concentration explained 67% [RMSEP = 1.51 g dry matter (DM) 100 g −1 ] of the variance. The performance of the grain yield PPLS models was compared with that of models based on some widely used vegetation indices [normalised difference vegetation index (NDVI), modified soil adjusted vegetation index (MSAVI), red edge inflection point (REIP) and d-chl-ab]. The explained variance of the models based on vegetation indices did not exceed 55%, indicating that these models were inferior to full spectrum models. This study shows that one or two years of spectral measurement are insufficient for building fully operational models for cereal property predictions.

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“…The method was developed using a RapidEye images from April 2011 until July 2011. The RapidEye satellite system works with five spectral bands (blue, green, red, red edge, near infrared), where the near-infrared (NIR) is, in general, especially sensitive to the vitality of vegetation (Rees 2001;Basnyat et al 2005). The return frequency at nadir is 5.5 days and the spatial resolution is 5 m. The radiometric calibrated and georeferenced scenes (Level 1B, Level 3A) were made available through the RapidEye science Archive (RESA).…”

Section: Satellite Datamentioning

confidence: 99%

Delineation of management zones with spatial data fusion and belief theory

Vallentin

Dobers²,

Itzerott

et al. 2019

Precision Agric

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Precision agriculture, as part of modern agriculture, thrives on an enormously growing amount of information and data for processing and application. The spatial data used for yield forecasting or the delimitation of management zones are very diverse, often of different quality and in different units to each other. For various reasons, approaches to combining geodata are complex, but necessary if all relevant information is to be taken into account. Data fusion with belief structures offers the possibility to link geodata with expert knowledge, to include experiences and beliefs in the process and to maintain the comprehensibility of the framework in contrast to other "black box" models. This study shows the possibility of dividing agricultural land into management zones by combining soil information, relief structures and multi-temporal satellite data using the transferable belief model. It is able to bring in the knowledge and experience of farmers with their fields and can thus offer practical assistance in management measures without taking decisions out of hand. At the same time, the method provides a solution to combine all the valuable spatial data that correlate with crop vitality and yield. For the development of the method, eleven data sets in each possible combination and different model parameters were fused. The most relevant results for the practice and the comprehensibility of the model are presented in this study. The aim of the method is a zoned field map with three classes: "low yield", "medium yield" and "high yield". It is shown that not all data are equally relevant for the modelling of yield classes and that the phenology of the plant is of particular importance for the selection of satellite images. The results were validated with yield data and show promising potential for use in precision agriculture.

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Effectiveness of using vegetation index to delineate zones of different soil and crop grain production characteristics

Cited by 13 publications

References 44 publications

Automatic delineation algorithm for site-specific management zones based on satellite remote sensing data

Automatic delineation algorithm for site-specific management zones based on satellite remote sensing data

Prediction of Wheat Yield and Protein Using Remote Sensors on Plots—Part I: Assessing near Infrared Model Robustness for Year and Site Variations

Delineation of management zones with spatial data fusion and belief theory

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