Determining nitrogen deficiencies for maize using various remote sensing indices

AbstractDetermining a precise nitrogen fertilizer requirement for maize in a particular field and year has proven to be a challenge due to the complexity of the nitrogen inputs, transformations and outputs in the nitrogen cycle. Remote sensing of maize nitrogen deficiency may be one way to move nitrogen fertilizer applications closer to the specific nitrogen requirement. Six vegetation indices [normalized difference vegetation index (NDVI), green normalized difference vegetation index (GNDVI), red-edge normalized difference vegetation index (RENDVI), triangle greenness index (TGI), normalized area vegetation index (NAVI) and chlorophyll index-green (CIgreen)] were evaluated for their ability to detect nitrogen deficiency and predict grain maize grain yield. Strip trials were established at two locations in Arkansas, USA, with nitrogen rate as the primary treatment. Remote sensing data was collected weekly with an unmanned aerial system (UAS) equipped with a multispectral and thermal sensor. Relationships among index value, nitrogen fertilizer rate and maize growth stage were evaluated. Green NDVI, RENDVI and CIgreen had the strongest relationship with nitrogen fertilizer treatment. Chlorophyll Index-green and GNDVI were the best predictors of maize grain yield early in the growing season when the application of additional nitrogen was still agronomically feasible. However, the logistics of late season nitrogen application must be considered.

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Sustainable Agriculture by Increasing Nitrogen Fertilizer Efficiency Using Low-Resolution Camera Mounted on Unmanned Aerial Vehicles

International Journal of Environmental Research and Public Health ◽

10.3390/ijerph16203893 ◽

2019 ◽

Vol 16 (20) ◽

pp. 3893 ◽

Cited By ~ 1

Author(s):

Kim ◽

Min ◽

Kim ◽

Silva ◽

Hyun ◽

...

Keyword(s):

Chlorophyll Content ◽

Nitrogen Fertilizer ◽

Precision Agriculture ◽

Vegetation Index ◽

Normalized Difference Vegetation Index ◽

Nitrogen Deficiency ◽

Underground Water ◽

Fertilizer Efficiency ◽

Low Resolution ◽

Effective Manner

Nitrogen use efficiency in modern agriculture is very low. It means that a lot of synthetic chemicals are wasted rather than utilized by crops. This can cause more problems where the soil surface is thin and rocky like Jeju Island in the Republic of Korea. This is because overly used nitrogen fertilizer can be washed into the underground water and pollute it. Thus, it would be important to monitor the nitrogen deficiency of crops in the field to provide the right amount of nitrogen in a timely manner so that nitrogen waste can be limited. To achieve this, the normalized difference vegetation index (NDVI) was used to monitor chlorophyll content, which is tightly associated with nitrogen content in the buckwheat field. The NDVI was calculated with the data obtained by a low-resolution camera mounted on an unmanned aerial vehicle. The results showed that the NDVI can estimate the chlorophyll content of buckwheat. These simple but clear results imply that precision agriculture could be achieved even with a low-resolution camera in a cost-effective manner to reduce the pollution of underground water.

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Prediction of crop productivity and evapotranspiration with two photosynthetic parameter regionalization methods

The Journal of Agricultural Science ◽

10.1017/s0021859612000901 ◽

2012 ◽

Vol 152 (1) ◽

pp. 119-133 ◽

Cited By ~ 3

Author(s):

S. HU ◽

X. MO

Keyword(s):

Remote Sensing ◽

Grain Yield ◽

Vegetation Index ◽

Normalized Difference Vegetation Index ◽

Census Data ◽

Wheat Yield ◽

Ecosystem Model ◽

Crop Productivity ◽

Photosynthetic Parameter ◽

Area Index

SUMMARYParameter regionalization is the foundation for the spatial application of an ecosystem model at the canopy level and has been improved greatly by remote sensing (RS). Photosynthetic rate is restricted by the carboxylation rate, which is limited by the activity of the enzyme Rubisco. By including RS normalized difference vegetation index (NDVI) and census data of grain yield at the county level in an ecosystem model (vegetation interface processes (VIP) model), the pattern of photosynthetic parameter Vcmax (maximum catalytic activity of Rubisco) of winter wheat was obtained and then used to simulate the wheat yield and evapotranspiration (ET) in the North China Plain (referred to as the Vcmax method). To evaluate its performance, the simulated yield and ET were compared with those derived by the leaf area index (LAI) method using the retrieved LAI from NDVI to drive the VIP model. The results showed that the Vcmax method performed better than the LAI method in highly productive fields, while the LAI method described the inter-annual variations of yield more favourably in fields with low productivity. Over the study area, average yield (4520 kg/ha) and seasonal ET (360 mm) simulated by the LAI method was slightly lower than those simulated using the Vcmax method (4730 kg/ha for yield and 372 mm for ET). Compared with the census data of yield, the relative root mean square error (RMSE) of grain yield with Vcmax method (0·17) was lower than that of the LAI method (0·20). In conclusion, the physical model with spatial Vcmax pattern from remote sensing is reliable for regional crop productivity prediction.

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Physiological constraints to realizing maize grain yield recovery with silking-stage nitrogen fertilizer applications

Field Crops Research ◽

10.1016/j.fcr.2018.08.025 ◽

2018 ◽

Vol 228 ◽

pp. 102-109 ◽

Cited By ~ 11

Author(s):

Sarah M. Mueller ◽

Tony J. Vyn

Keyword(s):

Grain Yield ◽

Nitrogen Fertilizer ◽

Physiological Constraints ◽

Maize Grain Yield ◽

Maize Grain

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The Use of Remote Sensing to Assess the Effects of Water Stress on Wheat

Experimental Agriculture ◽

10.1017/s0014479700019402 ◽

1991 ◽

Vol 27 (4) ◽

pp. 423-429 ◽

Cited By ~ 20

Author(s):

R. K. Mahey ◽

Rajwant Singh ◽

S. S. Sidhu ◽

R. S. Narang

Keyword(s):

Remote Sensing ◽

Water Stress ◽

Grain Yield ◽

Spectral Data ◽

Growth And Development ◽

Vegetation Index ◽

Normalized Difference Vegetation Index ◽

Spectral Response ◽

Canopy Cover ◽

Area Index

SUMMARYGround-based radiometric measurements in the red and infrared bands were used to monitor the growth and development of wheat under irrigated and stressed conditions throughout the 1987–88 and 1988–89 growth cycles. Spectral data were correlated with plant height, leaf area index, total fresh and total dry biomass, plant water content and grain yield. The radiance ratio (R) and normalized difference vegetation index (NDVI) were highly and linearly correlated with yield, establishing the potential which remote sensing has for predicting grain yield. The correlation for R and NDVI was at a maximum between 75 and 104 days after sowing, corresponding with maximum green crop canopy cover. The differences in spectral response over time between irrigated and unirrigated crops allowed detection of water stress effects on the crop, indicating that a hand-held radiometer can be used to collect spectral data which can supply information on wheat growth and development.Efectos de lafalta de agua en el trigo

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Optimal time for remote sensing to relate to crop grain yield on the Canadian prairies

Canadian Journal of Plant Science ◽

10.4141/p03-070 ◽

2004 ◽

Vol 84 (1) ◽

pp. 97-103 ◽

Cited By ~ 25

Author(s):

Prakash Basnyat, Brian McConkey ◽

Guy P. Lafond ◽

Alan Moulin ◽

Yann Pelcat

Keyword(s):

Remote Sensing ◽

Grain Yield ◽

Spring Wheat ◽

Vegetation Index ◽

Normalized Difference Vegetation Index ◽

Crop Productivity ◽

Field Pea ◽

Optimal Time ◽

Remotely Sensed Data ◽

Canadian Prairies

The optimal time to acquire remote sensing imagery to relate to grain yield has not been thoroughly investigated for the Canadian prairies. Remotely sensed data collected when there is the best relationship with yield should provide useful information on the in-field spatial variability of biophysical factors affecting crop productivity relevant to site-specific management. The correlations of normalized difference vegetation index (NDVI) with grain yield for three dates in 2000 at Indian Head and Swift Current, SK, for field pea, canola, and spring wheat were compared. No single date consistently had the highest NDVI-yield correlation for all crops. The period between Jul. 10 to 30 was optimal to obtain NDVI to relate to grain yield for springseeded crops that typically mature in August. Significant NDVI-yield correlations for this period were confirmed in three additional site-years. In a further site-year, however, NDVI-yield correlation was significant for wheat and pea, but not for canola. Occasional problems relating the NDVI to canola yield were attributed to characteristics of the canola canopy, namely, the highly reflective flowers and the dropping of leaves after flowering. In terms of both magnitude and temporal stability of the NDVI-yield correlation, we ranked the crops as: spring wheat, then pea, and then canola. Key words: Remote sensing; grain yield, field pea, canola, wheat, normalized difference vegetation index

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Soil Water Content and High-Resolution Imagery for Precision Irrigation: Maize Yield

Agronomy ◽

10.3390/agronomy9040174 ◽

2019 ◽

Vol 9 (4) ◽

pp. 174 ◽

Cited By ~ 3

Author(s):

Alfonso de Lara ◽

Louis Longchamps ◽

Raj Khosla

Keyword(s):

Remote Sensing ◽

Grain Yield ◽

Soil Water ◽

Vegetation Indices ◽

Remote Sensing Data ◽

Maize Yield ◽

Maize Grain Yield ◽

Use Efficiency ◽

Maize Grain ◽

Precision Irrigation

Improvement in water use efficiency of crops is a key component in addressing the increasing global water demand. The time and depth of the soil water monitoring are essential when defining the amount of water to be applied to irrigated crops. Precision irrigation (PI) is a relatively new concept in agriculture, and it provides a vast potential for enhancing water use efficiency, while maintaining or increasing grain yield. Neutron probes (NPs) have consistently been used as a robust and accurate method to estimate soil water content (SWC). Remote sensing derived vegetation indices have been successfully used to estimate variability of Leaf Area Index and biomass, which are related to root water uptake. Crop yield has not been evaluated on a basis of SWC, as explained by NPs in time and at different depths. The objectives of this study were (1) to determine the optimal time and depth of SWC and its relationship to maize grain yield (2) to determine if satellite-derived vegetation indices coupled with SWC could further improve the relationship between maize grain yield and SWC. Soil water and remote sensing data were collected throughout the crop season and analyzed. The results from the automated model selection of SWC readings, used to assess maize yield, consistently selected three dates spread around reproductive growth stages for most depths (p value < 0.05). SWC readings at the 90 cm depth had the highest correlation with maize yield, followed closely by the 120 cm. When coupled with remote sensing data, models improved by adding vegetation indices representing the crop health status at V9, right before tasseling. Thus, SWC monitoring at reproductive stages combined with vegetation indices could be a tool for improving maize irrigation management.

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Application of remote sensing in estimating maize grain yield in heterogeneous African agricultural landscapes: a review

International Journal of Remote Sensing ◽

10.1080/01431161.2017.1365390 ◽

2017 ◽

Vol 38 (23) ◽

pp. 6816-6845 ◽

Cited By ~ 16

Author(s):

Walter Chivasa ◽

Onisimo Mutanga ◽

Chandrashekhar Biradar

Keyword(s):

Remote Sensing ◽

Grain Yield ◽

Agricultural Landscapes ◽

Maize Grain Yield ◽

Maize Grain

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Analysis of the moisture content of maize kernels in over-ripe plants

Acta Agronomica Hungarica ◽

10.1556/aagr.54.2006.4.5 ◽

2006 ◽

Vol 54 (4) ◽

pp. 425-430

Author(s):

T. Árendás ◽

L. C. Marton ◽

P. Bónis ◽

Z. Berzsenyi

Keyword(s):

Linear Regression ◽

Moisture Content ◽

Grain Yield ◽

Equilibrium Moisture Content ◽

Weather Conditions ◽

Positive Correlation ◽

Maize Grain Yield ◽

Maize Grain ◽

Maize Kernels

The effect of varying weather conditions on the moisture content of the maize grain yield was investigated in Martonvásár, Hungary from late August to late September, and from the 3rd third of September to the 1st third of Novemberbetween 1999 and 2002. In every year a close positive correlation (P=0.1%) could be observed between the moisture content in late September and the rate of drying down in October. Linear regression was used each year to determine the equilibrium moisture content, to which the moisture content of kernels returned if they contained less than this quantity of water in late September and harvesting was delayed. In the experimental years this value ranged from 15.24-19.01%.

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TEMPERATURA DE SUPERFÍCIE E A RELAÇÃO COM O ÍNDICE DE VEGETAÇÃO POR DIFERENÇA NORMALIZADA (NDVI) NA MICROBACIA DO RIO DA BATATEIRAS, CRATO-CE

Revista da Casa da Geografia de Sobral (RCGS) ◽

10.35701/rcgs.v21n2.548 ◽

2019 ◽

Vol 21 (2) ◽

pp. 1310-1320

Author(s):

Cícera Celiane Januário da Silva ◽

Vinicius Ferreira Luna ◽

Joyce Ferreira Gomes ◽

Juliana Maria Oliveira Silva

Keyword(s):

Remote Sensing ◽

Surface Temperature ◽

Temperature Measurement ◽

Rainy Season ◽

Satellite Images ◽

Vegetation Index ◽

Normalized Difference Vegetation Index ◽

Landsat 8 ◽

Low Density ◽

Difference Map

O objetivo do presente trabalho é fazer uma comparação entre a temperatura de superfície e o Índice de Vegetação por Diferença Normalizada (NDVI) na microbacia do rio da Batateiras/Crato-CE em dois períodos do ano de 2017, um chuvoso (abril) e um seco (setembro) como também analisar o mapa de diferença de temperatura nesses dois referidos períodos. Foram utilizadas imagens de satélite LANDSAT 8 (banda 10) para mensuração de temperatura e a banda 4 e 5 para geração do NDVI. As análises demonstram que no mês de abril a temperatura da superfície variou aproximadamente entre 23.2ºC e 31.06ºC, enquanto no mês correspondente a setembro, os valores variaram de 25°C e 40.5°C, sendo que as maiores temperaturas foram encontradas em locais com baixa densidade de vegetação, de acordo com a carta de NDVI desses dois meses. A maior diferença de temperatura desses dois meses foi de 14.2°C indicando que ocorre um aumento da temperatura proporcionado pelo período que corresponde a um dos mais secos da região, diferentemente de abril que está no período de chuvas e tem uma maior umidade, presença de vegetação e corpos d’água que amenizam a temperatura.Palavras-chave: Sensoriamento Remoto; Vegetação; Microbacia. ABSTRACTThe objective of the present work is to compare the surface temperature and the Normalized Difference Vegetation Index (NDVI) in the Batateiras / Crato-CE river basin in two periods of 2017, one rainy (April) and one (September) and to analyze the temperature difference map in these two periods. LANDSAT 8 (band 10) satellite images were used for temperature measurement and band 4 and 5 for NDVI generation. The analyzes show that in April the surface temperature varied approximately between 23.2ºC and 31.06ºC, while in the month corresponding to September, the values ranged from 25ºC and 40.5ºC, and the highest temperatures were found in locations with low density of vegetation, according to the NDVI letter of these two months. The highest difference in temperature for these two months was 14.2 ° C, indicating that there is an increase in temperature provided by the period that corresponds to one of the driest in the region, unlike April that is in the rainy season and has a higher humidity, presence of vegetation and water bodies that soften the temperature.Key-words: Remote sensing; Vegetation; Microbasin.RESUMENEl objetivo del presente trabajo es hacer una comparación entre la temperatura de la superficie y el Índice de Vegetación de Diferencia Normalizada (NDVI) en la cuenca Batateiras / Crato-CE en dos períodos de 2017, uno lluvioso (abril) y uno (Septiembre), así como analizar el mapa de diferencia de temperatura en estos dos períodos. Las imágenes de satélite LANDSAT 8 (banda 10) se utilizaron para la medición de temperatura y las bandas 4 y 5 para la generación de NDVI. Los análisis muestran que en abril la temperatura de la superficie varió aproximadamente entre 23.2ºC y 31.06ºC, mientras que en el mes correspondiente a septiembre, los valores oscilaron entre 25 ° C y 40.5 ° C, y las temperaturas más altas se encontraron en lugares con baja densidad de vegetación, según el gráfico NDVI de estos dos meses. La mayor diferencia de temperatura de estos dos meses fue de 14.2 ° C, lo que indica que hay un aumento en la temperatura proporcionada por el período que corresponde a uno de los más secos de la región, a diferencia de abril que está en la temporada de lluvias y tiene una mayor humedad, presencia de vegetación y cuerpos de agua que suavizan la temperatura.Palabras clave: Detección remota; vegetación; Cuenca.

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