Comparison of Satellite Imagery and Ground‐Based Active Optical Sensors as Yield Predictors in Sugar Beet, Spring Wheat, Corn, and Sunflower

Bu, Honggang; Sharma, Lakesh K.; Denton, Anne; Franzen, David W.

doi:10.2134/agronj2016.03.0150

Cited by 41 publications

(32 citation statements)

References 42 publications

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“…We suggest that while developing P recommendations, it may be essential to differentiate between soil types and regions. For example, in North Dakota, United States for N in corn, sunflower, and wheat [53][54][55][56][57][58][59][60][61] recommendations were developed considering soil variability, the price of the commodity, cultivation system, yield potential, and soil moisture. It was found that there is a need to examine banded application according to the crop root system development because banded application P stays where it is applied.…”

Section: Discussionmentioning

confidence: 99%

A Case Study of Potential Reasons of Increased Soil Phosphorus Levels in the Northeast United States

2017

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Recent phosphorus (P) pollution in the United States, mainly in Maine, has raised some severe concerns over the use of P fertilizer application rates in agriculture. Phosphorus is the second most limiting nutrient after nitrogen and has damaging impacts on crop yield if found to be deficient. Therefore, farmers tend to apply more P than is required to satisfy any P loss after its application at planting. Several important questions were raised in this study to improve P efficiency and reduce its pollution. The objective of this study was to find potential reasons for P pollution in water bodies despite a decrease in potato acreage. Historically, the potato was found to be responsible for P water contamination due to its high P sensitivity and low P removal (25-30 kg ha −1 ) from the soil. Despite University of Maine recommended rate of 56 kg ha −1 P, if soil tests reveal that P is below 50 kg ha −1 , growers tend to apply P fertilizer at the rate of 182 kg ha −1 to compensate for any loss. The second key reason for excessive P application is its tendency to get fixed by aluminum (Al) in the soil. Soil sampling data from UMaine Soil Testing Laboratory confirmed that in Maine reactive Al levels have remained high over the last ten years and are increasing further. Likewise, P application to non-responsive sites, soil variability, pH change, and soil testing methods were found to be other possible reasons that might have led to increases in soil P levels resulting in P erosion to water streams.

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Section: Discussionmentioning

confidence: 99%

A Case Study of Potential Reasons of Increased Soil Phosphorus Levels in the Northeast United States

2017

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“…Variations in slope within a landscape can have a substantial impact on grain yield variability [64]. Soil depth and drainage also have a significant impact on corn grain and potato yield [65]. In commercial crop production, higher N fertility levels have been observed in foot slopes and depressions due to the flow of water and soil deposition of clay and organic matter to these landscape positions.…”

Section: Spatial Variationmentioning

confidence: 99%

“…This indicates that estimation of yield and potential for N response in relative terms is possible. Several studies were conducted to improve the relationship between crop yield and sensor readings [65,106,107,[131][132][133], however, their practical application is still in question due to their inconsistent results.…”

Section: Use Of Sensors and Ndvimentioning

confidence: 99%

“…However, LAI has not been used to a level that could provide growers a stable and reliable sensor based in-season N application equation, and the apparent reason is measuring LAI is labor intensive. A recent study by Sharma et al [65] on potatoes provided some promising results using estimated LAI from the sensor itself, which was found useful in improving the sensor and potato yield relationship and also solved the issue of red wavelength saturation with increased biomass. This is an important finding regarding solving the red wavelength saturation and improving the sensor and yield relationship using the same sensor from where NDVI readings were collected.…”

Section: Use Of Sensors and Ndvimentioning

confidence: 99%

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A Review of Methods to Improve Nitrogen Use Efficiency in Agriculture

2017

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Management of nitrogen (N) is a challenging task and several methods individually and in combination are in use to manage its efficiency. However, nitrogen use efficiency (NUE) has not been improved to a level, only 33%, as predicted by the researchers while developing nitrogen management tools and methods. The primary objective of this review article is to evaluate methods and tools available to manage nitrogen. Several methods, soil testing, plant tissue testing, spectral response, fertilizer placement and timing and vegetative indexes (leaf area index, and NDVI) through drones, handheld sensors, and satellite imagery were reviewed on the subject of user-friendly and effectiveness towards NUE. No single method was found sufficient to counter the nitrogen loss. Some methods were found time consuming and unsynchronized with N uptake behavior of particular crop, for example, plant tissue testing. Use of precision agriculture tools, such as GreenSeeker, Holland Crop Circle, drone, and satellite imagery, were found better compared to conventional methods such as soil testing, but these tools can only be used when the crop is up. Therefore, N management is possible only through inseason N application methods. When 70% of the applied nitrogen is used by the crops within 25-30 days after planting, for example, corn and potatoes, it is required to apply major N rates through inseason approach and some N at planting using soil test reports. In conclusion, this article strongly advocates using two or more methods in combination when managing N.

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“…2018, 10, 68 8 of 20 this study include the NDVI and an NDVI combined with the NIR reflectance of vegetation (NIRv). The NDVI is a key parameter that is used to improve the accuracy of yield prediction for sugar beets, spring wheat, corn, and sunflower based on the NDVI relationships with optical signals under different nitrogen (N) and sulfur (S) contents [68][69][70][71][72][73][74][75][76]. Notably, the NDVI has been widely used for LAI extraction from high spatial resolution imagery [21,22,24,25,29,77].…”

Section: Lai Inversion Proceduresmentioning

confidence: 99%

Comparative Analysis of Chinese HJ-1 CCD, GF-1 WFV and ZY-3 MUX Sensor Data for Leaf Area Index Estimations for Maize

Zhao

Liu

et al. 2018

Remote Sensing

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Abstract:In recent years, China has developed and launched several satellites with high spatial resolutions, such as the resources satellite No. 3 (ZY-3) with a multi-spectral camera (MUX) and 5.8 m spatial resolution, the satellite GaoFen No. 1 (GF-1) with a wide field of view (WFV) camera and 16 m spatial resolution, and the environment satellite (HJ-1A/B) with a charge-coupled device (CCD) sensor and 30 m spatial resolution. First, to analyze the potential application of ZY-3 MUX, GF-1 WFV, and HJ-1 CCD to extract the leaf area index (LAI) at the regional scale, this study estimated LAI from the relationships between physical model-based spectral vegetation indices (SVIs) and LAI values that were generated from look-up tables (LUTs), simulated from the combination of the PROSPECT-5B leaf model and the scattering by arbitrarily inclined leaves with the hot-spot effect (SAILH) canopy reflectance model. Second, to assess the surface reflectance quality of these sensors after data preprocessing, the well-processed surface reflectance products of the Landsat-8 operational land imager (OLI) sensor with a convincing data quality were used to compare the performances of ZY-3 MUX, GF-1 WFV, and HJ-1 CCD sensors both in theory and reality. Apart from several reflectance fluctuations, the reflectance trends were coincident, and the reflectance values of the red and near-infrared (NIR) bands were comparable among these sensors. Finally, to analyze the accuracy of the LAI estimated from ZY-3 MUX, GF-1 WFV, and HJ-1 CCD, the LAI estimations from these sensors were validated based on LAI field measurements in Huailai, Hebei Province, China. The results showed that the performance of the LAI that was inversed from ZY-3 MUX was better than that from GF-1 WFV, and HJ-1 CCD, both of which tended to be systematically underestimated. In addition, the value ranges and accuracies of the LAI inversions both decreased with decreasing spatial resolution.

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Comparison of Satellite Imagery and Ground‐Based Active Optical Sensors as Yield Predictors in Sugar Beet, Spring Wheat, Corn, and Sunflower

Cited by 41 publications

References 42 publications

A Case Study of Potential Reasons of Increased Soil Phosphorus Levels in the Northeast United States

A Case Study of Potential Reasons of Increased Soil Phosphorus Levels in the Northeast United States

A Review of Methods to Improve Nitrogen Use Efficiency in Agriculture

Comparative Analysis of Chinese HJ-1 CCD, GF-1 WFV and ZY-3 MUX Sensor Data for Leaf Area Index Estimations for Maize

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