Estimating Cotton Nitrogen Nutrition Status Using Leaf Greenness and Ground Cover Information

Muharam, Farrah Melissa; Maas, Stephen J.; Bronson, K. F.; Delahunty, Tina

doi:10.3390/rs70607007

Cited by 20 publications

(14 citation statements)

References 41 publications

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“…Spectral measurements at different spatial scales have determined particular wavelengths to be the most sensitive to the crop N status [11,20]. This may lead a particular vegetation index to perform differently depending on the spatial scale at which the spectral measurement is made when tracking N deficiencies in a crop.…”

Section: Unmanned Aerial Systems For Monitoring Crop Performancementioning

confidence: 99%

Assessment of In-Season Cotton Nitrogen Status and Lint Yield Prediction from Unmanned Aerial System Imagery

et al. 2017

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Abstract:The present work assessed the usefulness of a set of spectral indices obtained from an unmanned aerial system (UAS) for tracking spatial and temporal variability of nitrogen (N) status as well as for predicting lint yield in a commercial cotton (Gossypium hirsutum L.) farm. Organic, inorganic and a combination of both types of fertilizers were used to provide a range of eight N rates from 0 to 340 kg N ha −1 . Multi-spectral images (reflectance in the blue, green, red, red edge and near infrared bands) were acquired on seven days throughout the season, from 62 to 169 days after sowing (DAS), and data were used to compute structure-and chlorophyll-sensitive vegetation indices (VIs). Above-ground plant biomass was sampled at first flower, first cracked boll and maturity and total plant N concentration (N%) and N uptake determined. Lint yield was determined at harvest and the relationships with the VIs explored. Results showed that differences in plant N% and N uptake between treatments increased as the season progressed. Early in the season, when fertilizer applications can still have an effect on lint yield, the simplified canopy chlorophyll content index (SCCCI) was the index that best explained the variation in N uptake and plant N% between treatments. Around first cracked boll and maturity, the linear regression obtained for the relationships between the VIs and both plant N% and N uptake was statistically significant, with the highest r 2 values obtained at maturity. The normalized difference red edge (NDRE) index, and SCCCI were generally the indices that best distinguished the treatments according to the N uptake and total plant N%. Treatments with the highest N rates (from 307 to 340 kg N ha −1 ) had lower normalized difference vegetation index (NDVI) than treatments with 0 and 130 kg N ha −1 at the first measurement day (62 DAS), suggesting that factors other than fertilization N rate affected plant growth at this early stage of the crop. This fact affected the earliest date at which the structure-sensitive indices NDVI and the visible atmospherically resistant index (VARI) enabled yield prediction (97 DAS). A statistically significant linear regression was obtained for the relationships between SCCCI and NDRE with lint yield at 83 DAS. Overall, this study shows the practicality of using an UAS to monitor the spatial and temporal variability of cotton N status in commercial farms. It also illustrates the challenges of using multi-spectral information for fertilization recommendation in cotton at early stages of the crop.

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Section: Unmanned Aerial Systems For Monitoring Crop Performancementioning

confidence: 99%

Assessment of In-Season Cotton Nitrogen Status and Lint Yield Prediction from Unmanned Aerial System Imagery

et al. 2017

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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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“…Extensive studies have been conducted to estimate N content through chlorophyll-based spectral indices [9][10][11][12] , using techniques such as selecting sensitive wavelengths related to N, or by acquiring spectral reflectance data from multiple sensors with a variety of spatial resolutions 13 . Leaf N has been found to relate to leaf or canopy reflectance more closely in the green wavelengths than in the red-edge or NIR regions 14 .…”

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confidence: 99%

Estimation of leaf nutrition status in degraded vegetation based on field survey and hyperspectral data

Peng

Zhang

et al. 2020

Sci Rep

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Timely monitoring of global plant biogeochemical processes demands fast and highly accurate estimation of plant nutrition status, which is often estimated based on hyperspectral data. However, few such studies have been conducted on degraded vegetation. In this study, complete combinations of either original reflectance or first-order derivative spectra have been developed to quantify leaf nitrogen (N), phosphorus (P), and potassium (K) contents of tree, shrub, and grass species using hyperspectral datasets from light, moderate, and severely degraded vegetation sites in Helin County, China. Leaf N, P, and K contents were correlated to identify suitable combinations. The most effective combinations were those of reflectance difference (Dij), normalized differences (ND), first-order derivative (FD), and first-order derivative difference (FD(D)). Linear regression analysis was used to further optimize sensitive band-based combinations, which were compared with 43 frequently used empirical spectral indices. The proposed hyperspectral indices were shown to effectively quantify leaf N, P, and K content (R2 > 0.5, p < 0.05), confirming that hyperspectral data can be potentially used for fine scale monitoring of degraded vegetation.employed in biomes such as grasslands and savannas 1,18-20 , crops 21 , and trees 22 . A study of maize leaf P content found that 540, 720, 740, and 850 nm are the most sensitive bands for detection of P in both the vegetation production stage and the flowering stage 21 .The spectral region which most closely relates to leaf P content has been found to overlay the spectral region which demonstrates water absorbing traits (1000-2500 nm) 19 . Bands which are indicative of leaf P also lie in the region of 580-710 nm, although this varies among different case studies. The confusion between water absorption and sensitivity to sampling sites makes it difficult to identify the most suitable bands for leaf P estimation. In this study, we aimed to select several sensitive bands from the 500 available and develop a complete combination of reflectance and its first-order derivative (FD) from tree, shrub, and grass species in various degraded vegetation sites, with the objective of developing more general hyperspectral indices for the estimation of leaf P content.Finally, potassium (K) is also a key plant requirement, present mostly as K + ions in vacuoles. K provides regulatory control over processes such as transpiration, starch synthesis, sucrose translocation, respiration, and lipid synthesis 23 . Plants deficient in K exhibit limited growth, metabolism, and stress defense 24 , leading to lower overall biomass and coverage and changes to leaf color. If a K deficiency occurs at the vegetation level, this can accelerate the degradation process. Soils in many broad-acre semiarid areas have become deficient in K, resulting in a decrease of K in the canopy and stem 25,26 .Remote sensing of leaf N, P, or K contents is a challenging task due to the lack of direct absorption features that can be observed in t...

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Estimating Cotton Nitrogen Nutrition Status Using Leaf Greenness and Ground Cover Information

Cited by 20 publications

References 41 publications

Assessment of In-Season Cotton Nitrogen Status and Lint Yield Prediction from Unmanned Aerial System Imagery

Assessment of In-Season Cotton Nitrogen Status and Lint Yield Prediction from Unmanned Aerial System Imagery

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

Estimation of leaf nutrition status in degraded vegetation based on field survey and hyperspectral data

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