Airborne Hyperspectral Images and Ground-Level Optical Sensors As Assessment Tools for Maize Nitrogen Fertilization

Quemada, Miguel; Gabriel, José Luis; Zarco‐Tejada, Pablo J.

doi:10.3390/rs6042940

Cited by 119 publications

(84 citation statements)

References 44 publications

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“…Although the attention toward precision farming techniques is increasing, studies providing methods to obtain variable rate N fertilization maps based on remote sensing data are still limited. Among remote sensing techniques it is worth mentioning the recent development of UAVs (Unmanned Aerial Vehicles) in agricultural applications allowing the collection of multispectral and hyperspectral imagery at sub-metric spatial resolution [49][50][51][52].…”

Section: Discussionmentioning

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: Discussionmentioning

confidence: 99%

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

et al. 2014

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“…In recent years, the use of chlorophyll meters (CM), which measure leaf chlorophyll content to estimate N nutrition status, has increased among researchers and farmers [10,11]. Hawkings et al [12] found that the adjusted R 2 of the relationship between CM readings and the nitrogen rate difference (ND) for the economic optimum nitrogen rate (EONR) was 0.76 for a maize-maize rotation.…”

Section: Introductionmentioning

confidence: 99%

“…This does not increase grain yield but, instead, wastes fertilizer, increases costs, and can cause nitrate pollution [8]. This problem has led farmers, scientist and politicians to explore how to improve N efficiency, reduce N inputs and prevent water and soil pollution associated with maize production [9][10][11].…”

Section: Introductionmentioning

confidence: 99%

Analysis of Vegetation Indices to Determine Nitrogen Application and Yield Prediction in Maize (Zea mays L.) from a Standard UAV Service

et al. 2016

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Abstract:The growing use of commercial unmanned aerial vehicles (UAV) and the need to adjust N fertilization rates in maize (Zea mays L.) currently constitute a key research issue. In this study, different multispectral vegetation indices (green-band and red-band based indices), SPAD and crop height (derived from a multispectral compact camera mounted on a UAV) were analysed to predict grain yield and determine whether an additional sidedress application of N fertilizer was required just before flowering. Seven different inorganic N rates (0, 100, 150, 200, 250, 300, 400 kg·N·ha −1 ), two different pig slurry manure rates (Ps) (150 or 250 kg·N·ha −1 ) and four different inorganic-organic N combinations (N100Ps150, N100Ps250, N200Ps150, N200Ps250) were applied to maize experimental plots. The spectral index that best explained final grain yield for the N treatments was the Wide Dynamic Range Vegetation Index (WDRVI). It identified a key threshold above/below 250-300 kg·N·ha −1 . WDRVI, NDVI and crop height showed no significant response to extra N application at the economic optimum rate of fertilization (239.8 kg·N·ha −1 ), for which a grain yield of 16.12 Mg·ha −1 was obtained. This demonstrates their potential as yield predictors at V12 stage. Finally, a ranking of different vegetation indices and crop height is proposed to overcome the uncertainty associated with basing decisions on a single index.

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“…Simple regression models usually correlate measured nitrogen content with variables such as chlorophyll, two-band, or three-band spectral indices [16][17][18][19][20][21][22][23]. However, the decoupling of leaf chlorophyll content and LNC might exist in ecosystems where N limitations are not strong [24].…”

Section: Introductionmentioning

confidence: 99%

Limited Effects of Water Absorption on Reducing the Accuracy of Leaf Nitrogen Estimation

Wang

Chen

et al. 2017

Remote Sensing

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Abstract:Nitrogen is an essential nutrient in many terrestrial ecosystems because it affects vegetation's primary production. Due to the variety of nitrogen-containing substances and the differences in their composition across species, statistical approaches are now dominant in remote sensing retrieval of leaf nitrogen content. Many studies remove spectral regions characterized by strong water absorptions before retrieving nitrogen content, because water is believed to mask the absorption features of nitrogen. The objectives of this study are to discuss the necessity of this practice and to explore how water absorption affects leaf nitrogen estimation. Spectral measurements and chemical analyses for Maize, Sawtooth Oak, and Sweetgum leaves were carried out in 2014. The leaf optical properties model PROSPECT5 was used to eliminate the influences of water on the measured reflectance spectra. The inversion accuracy of PROPECT5 for chlorophyll, carotenoid, water, and dry matter of Maize was also discussed. Measured, simulated, and water-removed spectra were used to: (1) find the optimal nitrogen-related spectral index; and (2) regress with the area-based leaf nitrogen concentration (LNC) using the partial least square regression technique (PLSR). Two types of spectral indices were selected in this study: Normalized Difference Spectral Index (NDSI) and Ratio Spectral Index (RSI). Additionally, first-order derivative forms of measured, simulated, and water-removed spectra were devised to search for the optimal spectral indices. Finally, species-specific optimal indices and cross-species optimal indices, as well as their root mean square errors (RMSE) and coefficients of determination (R 2 ), were obtained. The Ending Top Percentile (ETP), an indicator of the performance of cross-species optimal indices, was also calculated. PLSR was combined with leave-one-out cross validation (LOOCV) for each species. The predicted root mean square errors (RMSEP) and predicted R 2 were finally calculated. The results showed that chlorophyll, carotenoid, and water contents could be estimated with R 2 of 0.75, 0.59, and 0.69, respectively, which were acceptable for fresh leaves. The dry matter was retrieved with a relatively lower accuracy because of the fixed absorption coefficients adopted by PROSPECT5. The performances of species-specific optimal indices using water-free spectra were comparable to or worse than the corresponding indices derived with measured or simulated spectra. Compared with measured spectra, ETP did not change much after the effects of water were removed, and the R 2 between cross-species optimal spectral indices and area-based LNC for Sawtooth Oak and Sweetgum decreased while it remained almost the same for Maize, suggesting that the water-removed cross-species optimal indices were inferior to the corresponding optimal indices found without water removal. ETP was larger than 30% for all spectra, demonstrating the non-existence of common optimal NDSI or RSI for the three species. After water removal, the accuracy...

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Airborne Hyperspectral Images and Ground-Level Optical Sensors As Assessment Tools for Maize Nitrogen Fertilization

Cited by 119 publications

References 44 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

Analysis of Vegetation Indices to Determine Nitrogen Application and Yield Prediction in Maize (Zea mays L.) from a Standard UAV Service

Limited Effects of Water Absorption on Reducing the Accuracy of Leaf Nitrogen Estimation

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