scholarly journals Site-specific nitrogen management in winter wheat supported by low-altitude remote sensing and soil data

2020 ◽  
Author(s):  
F. Argento ◽  
T. Anken ◽  
F. Abt ◽  
E. Vogelsanger ◽  
A. Walter ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Winter Wheat ◽  
Nitrogen Management ◽  
Site Specific ◽  
Low Altitude

Winter Wheat Starter Nitrogen Management: A Preplant Soil Nitrate Test and Site-Specific Nitrogen Loss Potential

2014 ◽  
Vol 78 (3) ◽  
pp. 1021-1034 ◽  
Author(s):  
Patrick Forrestal ◽  
John Meisinger ◽  
Robert Kratochvil
Keyword(s):  
Winter Wheat ◽  
Nitrogen Loss ◽  
Nitrogen Management ◽  
Soil Nitrate ◽  
Site Specific

In-Season Optimization and Site-Specific Nitrogen Management for Soft Red Winter Wheat

2004 ◽  
Vol 96 (1) ◽  
pp. 124 ◽  
Author(s):  
Michael Flowers ◽  
Randall Weisz ◽  
Ronnie Heiniger ◽  
Deanna Osmond ◽  
Carl Crozier
Keyword(s):  
Winter Wheat ◽  
Nitrogen Management ◽  
Site Specific ◽  
Soft Red Winter Wheat ◽  
Red Winter Wheat

Site-Specific Nitrogen Management in Rice Using Remote Sensing and Geostatistics

2017 ◽  
Vol 48 (10) ◽  
pp. 1154-1166 ◽  
Author(s):  
Rahul Tripathi ◽  
A. K. Nayak ◽  
R. Raja ◽  
Mohammad Shahid ◽  
S. Mohanty ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Nitrogen Management ◽  
Site Specific

Analysis of Remote Sensing based Vegetation Indices (VIs) for Unmanned Aerial System (UAS): A Review

2020 ◽  
Vol 3 (2) ◽  
pp. 58-73
Author(s):  
Vijay Bhagat ◽  
Ajaykumar Kada ◽  
Suresh Kumar
Keyword(s):  
Remote Sensing ◽  
Spatial Data ◽  
Satellite Remote Sensing ◽  
Vegetation Indices ◽  
Unmanned Aerial System ◽  
Sustainable Land Management ◽  
Efficient Tool ◽  
Site Specific ◽  
The Earth ◽  
Interesting Area

Unmanned Aerial System (UAS) is an efficient tool to bridge the gap between high expensive satellite remote sensing, manned aerial surveys, and labors time consuming conventional fieldwork techniques of data collection. UAS can provide spatial data at very fine (up to a few mm) and desirable temporal resolution. Several studies have used vegetation indices (VIs) calculated from UAS based on optical- and MSS-datasets to model the parameters of biophysical units of the Earth surface. They have used different techniques of estimations, predictions and classifications. However, these results vary according to used datasets and techniques and appear very site-specific. These existing approaches aren’t optimal and applicable for all cases and need to be tested according to sensor category and different geophysical environmental conditions for global applications. UAS remote sensing is a challenging and interesting area of research for sustainable land management.

Potential of water balance and remote sensing-based evapotranspiration models to predict yields of spring barley and winter wheat in the Czech Republic

2021 ◽  
Vol 256 ◽  
pp. 107064
Author(s):  
František Jurečka ◽  
Milan Fischer ◽  
Petr Hlavinka ◽  
Jan Balek ◽  
Daniela Semerádová ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Czech Republic ◽  
Water Balance ◽  
Winter Wheat ◽  
Spring Barley ◽  
The Czech Republic

scholarly journals Monitoring Hybrid Rice Phenology at Initial Heading Stage Based on Low-Altitude Remote Sensing Data

2020 ◽  
Vol 13 (1) ◽  
pp. 86
Author(s):  
Yi Ma ◽  
Qi Jiang ◽  
Xianting Wu ◽  
Renshan Zhu ◽  
Yan Gong ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Air Temperature ◽  
Hybrid Rice ◽  
Remote Sensing Data ◽  
Rice Cultivars ◽  
Sensing Data ◽  
Gauss Function ◽  
Low Altitude ◽  
Rice Phenology ◽  
Heading Stage

Accurate monitoring of hybrid rice phenology (RP) is crucial for breeding rice cultivars and controlling fertilizing amount. The aim of this study is to monitor the exact date of hybrid rice initial heading stage (IHSDAS) based on low-altitude remote sensing data and analyze the influence factors of RP. In this study, six field experiments were conducted in Ezhou city and Lingshui city from 2016 to 2019, which involved different rice cultivars and nitrogen rates. Three low-altitude remote sensing platforms were used to collect rice canopy reflectance. Firstly, we compared the performance of normalized difference vegetation index (NDVI) and red edge chlorophyll index (CIred edge) for monitoring RP. Secondly, double logistic function (DLF), asymmetric gauss function (AGF), and symmetric gauss function (SGF) were used to fit time-series CIred edge for acquiring phenological curves (PC), the feature: maximum curvature (MC) of PC was extracted to monitor IHSDAS. Finally, we analyzed the influence of rice cultivars, N rates, and air temperature on RP. The results indicated that CIred edge was more appropriate than NDVI for monitoring RP without saturation problem. Compared with DLF and AGF, SGF could fit CIred edge without over fitting problem. MC of SGF_CIred edge from all three platforms showed good performance in monitoring IHSDAS with good robustness, R2 varied between 0.82 and 0.95, RMSE ranged from 2.31 to 3.81. In addition, the results demonstrated that high air temperature might cause a decrease of IHSDAS, and the growth process of rice was delayed when more nitrogen fertilizer was applied before IHSDAS. This study illustrated that low-altitude remote sensing technology could be used for monitoring field-scale hybrid rice IHSDAS accurately.

Directed sampling using remote sensing with a response surface sampling design for site-specific agriculture

2006 ◽  
Vol 53 (2) ◽  
pp. 98-112 ◽  
Author(s):  
Glenn J. Fitzgerald ◽  
Scott M. Lesch ◽  
Edward M. Barnes ◽  
William E. Luckett
Keyword(s):  
Remote Sensing ◽  
Response Surface ◽  
Sampling Design ◽  
Surface Sampling ◽  
Site Specific
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