Value of Using Different Vegetative Indices to Quantify Agricultural Crop Characteristics at Different Growth Stages under Varying Management Practices

Hatfield, Jerry L.; Prueger, John H.

doi:10.3390/rs2020562

Cited by 259 publications

(159 citation statements)

References 25 publications

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“…The correlation between leaf pigments and leaf N incorporated in chlorophyll molecular structure [17,18] justified the use of vegetation indices for the determination of plant N condition [19][20][21][22][23][24]. Moreover, hyperspectral data have been also successfully used to estimate aboveground biomass accumulation, the second input required for NNI computation, using combinations of visible and near infrared reflectance in the form of simple or normalized ratios [25,26]. Nevertheless, the use of remote sensing to monitor crops in precision farming is still limited although its high potentiality in providing spatially detailed information to support site-specific management.…”

Section: Introductionmentioning

confidence: 99%

Nitrogen Status Assessment for Variable Rate Fertilization in Maize through Hyperspectral Imagery

et al. 2014

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This paper presents a method for mapping the nitrogen (N) status in a maize field using hyperspectral remote sensing imagery. An airborne survey was conducted with an AISA Eagle hyperspectral sensor over an experimental farm where maize (Zea mays L.) was grown with two N fertilization levels (0 and 100 kg N ha The %N a and the W flight were then mapped and used to compute the NNI map over the entire field. The NNI map agreed with the NNI estimated using field data through traditional destructive measurements (R 2 = 0.70) confirming the potential of using remotely sensed indices to assess the crop N condition. Finally, a method to derive a pixel based variable rate N fertilization map was proposed as the difference between the actual N content and the optimal N content. We think that the proposed operational methodology is promising for precision farming since it represents an innovative attempt to derive a variable rate N fertilization map based on the actual crop N status from an aerial hyperspectral image.

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Section: Introductionmentioning

confidence: 99%

Nitrogen Status Assessment for Variable Rate Fertilization in Maize through Hyperspectral Imagery

et al. 2014

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“…However, these studies were mostly based on single-image acquisitions and did not account for the variable nature of the growing season. Because the relationship between spectral measurements and production variables could vary between different phenological stages [13], the use of different vegetation indices during different growing stages would be required [20]. To optimize the scheduling of remote sensing missions and to monitor the production potential throughout the growing season, the temporal profile of the association between spectral information and production variables requires further investigation.…”

Section: Introductionmentioning

confidence: 99%

Temporal Dependency of Yield and Quality Estimation through Spectral Vegetation Indices in Pear Orchards

et al. 2015

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Yield and quality estimations provide vital information to fruit growers, yet require accurate monitoring throughout the growing season. To this end, the temporal dependency of fruit yield and quality estimations through spectral vegetation indices was investigated in irrigated and rainfed pear orchards. Both orchards were monitored throughout three consecutive growing seasons, including spectral measurements (i.e., hyperspectral canopy reflectance measurements) as well as yield determination (i.e., total yield and number of fruits per tree) and quality assessment (i.e., fruit firmness, total soluble solids and fruit color). The results illustrated a clear association between spectral vegetation indices and both fruit yield and fruit quality (|r| > 0.75; p < 0.001). However, the correlations between vegetation indices and production variables varied throughout the growing season, depending on the phenological stage of fruit development. In the irrigated orchard, index values showed a strong association with production variables near time of OPEN ACCESSRemote Sens. 2015, 7 9887 harvest (|r| > 0.6; p < 0.001), while in the rainfed orchard, index values acquired during vegetative growth periods presented stronger correlations with fruit parameters (|r| > 0.6; p < 0.001). The improved planning of remote sensing missions during (rainfed orchards) and after (irrigated orchards) vegetative growth periods could enable growers to more accurately predict production outcomes and improve the production process.

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“…When this occurs, NDVI becomes insensitive to biomass changes, which may have reflexes on productivity (POVH et al, 2008). For this reason, the simple index ratio (RVI, RNIR/RR) and inverse ratio (IRVI, RR/RNIR) have also been studied to predict the biomass in areas with higher intensity of vegetation, since these indexes are less susceptible to saturation (HATFIELD & PRUEGER, 2010;LI et al, 2010;BOLFE et al, 2012).…”

Section: Introductionmentioning

confidence: 99%

Use of active canopy sensors to discriminate wheat response to nitrogen fertilization under no-tillage

2016

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Spectral reflectance sensors may be useful in discriminating biomass and nitrogen status in plants. A field experiment was carried out on a loamy Typic Hapludox under no-tillage in Ponta Grossa, Parana State, Brazil, aiming to evaluate the efficiency of active canopy sensors (GreenSeeker 505 and Crop Circle ACS-470) to discriminate wheat response to nitrogen fertilization by determining the dry mass, nitrogen status, and grain yield. A randomized complete block design was used and four treatments were replicated 12 times. The treatments consisted of nitrogen application, as urea, at the rates of 0, 50, 100, and 150 kg ha -1 . The nitrogen rates were applied in topdressing at tillering of wheat crop. The readings from the sensors were sensitive to changes caused by nitrogen rates application and yet had close and significant correlations with the dry biomass production and nitrogen uptake by wheat plants. GreenSeeker 505 and Crop Circle ACS-470 sensors showed similar efficiency in discriminating biomass production and the nutritional status in the wheat crop related to nitrogen. Wheat grain yield was high and it did not follow the gains in the production of dry mass in the shoot.

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Value of Using Different Vegetative Indices to Quantify Agricultural Crop Characteristics at Different Growth Stages under Varying Management Practices

Cited by 259 publications

References 25 publications

Nitrogen Status Assessment for Variable Rate Fertilization in Maize through Hyperspectral Imagery

Nitrogen Status Assessment for Variable Rate Fertilization in Maize through Hyperspectral Imagery

Temporal Dependency of Yield and Quality Estimation through Spectral Vegetation Indices in Pear Orchards

Use of active canopy sensors to discriminate wheat response to nitrogen fertilization under no-tillage

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