Nitrogen Status Estimation of Winter Wheat by Using an IKONOS Satellite Image in the North China Plain

Lu, Jia; Yu, Zihui; Li, Fēi; Gnyp, Martin L.; Koppe, Wolfgang; Bareth, Georg; Miao, Yuxin; Chen, Xinping; Zhang, Fusuo

doi:10.1007/978-3-642-27278-3_19

Cited by 9 publications

(10 citation statements)

References 29 publications

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“…NDVI has been correlated with crop parameters, such as wet biomass, leaf area index, plant height and grain yield [10]. Based on the NDVI, more indices have been generated that present equal or better performances in the estimation of crop-related parameters and are based on the same or different bands of the electromagnetic spectrum, such as the Normalized Difference Red Edge Index (NDRE) [11], the Advanced Normalised Vegetation Index (ANVI) [12], the Green Normalised Difference Vegetation Index (GNDVI) [13], the Green Red Vegetation Index (GRVI) [14], the Damage Sensitive Spectral Index (DSSI) [15], the Optimized Soil-Adjusted Vegetation Index (OSAVI) [16], the Ratio Vegetation Index (RVI) [17], the Simple Ratio (SR) [18] and the Enhanced Vegetation Index (EVI) [19]. Despite the usefulness of the SVIs in assessing the crop growth and yield parameters, the platform that is used to acquire the different bands is very important and can be divided into satellite, aerial and proximal platforms, based on the distance to the assessed crop [20,21].…”

Section: Introductionmentioning

confidence: 99%

Satellite and Proximal Sensing to Estimate the Yield and Quality of Table Grapes

et al. 2018

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Table grapes are a crop with high nutritional value that need to be monitored often to achieve high yield and quality. Non-destructive methods, such as satellite and proximal sensing, are widely used to estimate crop yield and quality characteristics, and spectral vegetation indices (SVIs) are commonly used to present site specific information. The aim of this study was the assessment of SVIs derived from satellite and proximal sensing at different growth stages of table grapes from veraison to harvest. The study took place in a commercial table grape vineyard (Vitis vinifera cv. Thompson Seedless) during three successive cultivation years (2015-2017). The Normalized Difference Vegetation Index (NDVI) and Green Normalized Difference Vegetation Index (GNDVI) were calculated by employing satellite imagery (Landsat 8) and proximal sensing (Crop Circle ACS 470) to assess the yield and quality characteristics of table grapes. The SVIs exhibited different degrees of correlations with different measurement dates and sensing methods. Satellite-based GNDVI at harvest presented higher correlations with crop quality characteristics (r = 0.522 for berry diameter, r = 0.537 for pH, r = 0.629 for berry deformation) compared with NDVI. Proximal-based GNDVI at the middle of veraison presented higher correlations compared with NDVI (r = −0.682 for berry diameter, r = −0.565 for berry deformation). Proximal sensing proved to be more accurate in terms of table grape yield and quality characteristics compared to satellite sensing.

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Section: Introductionmentioning

confidence: 99%

Satellite and Proximal Sensing to Estimate the Yield and Quality of Table Grapes

et al. 2018

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“…SAT and UAV NDVI have been widely tested to assess wheat N status and estimate crop final yield, with encouraging results. Strong correlations were found between SAT NDVI and specific agronomic traits such as above ground biomass (Perry et al 2013), SPAD, stem sap nitrate, biomass and N uptake (Jia et al 2012) as well as between UAV NDVI and wheat leaf area index (Lelong et al ., 2008). However, few studies compared lab analysis on plant samples and in-field and remote sensing methods to assess wheat N status in early growth stages (Cao et al ., 2015; Eitel et al ., 2011; Perry et al ., 2013).…”

Section: Introductionmentioning

confidence: 99%

Reliability of Ndvi Derived by High Resolution Satellite and Uav Compared to in-Field Methods for the Evaluation of Early Crop N Status and Grain Yield in Wheat

et al. 2017

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SUMMARYThis study was aimed at comparing in-field parameters and remote sensing NDVI (normalized difference vegetation index) by both satellite (SAT) and unmanned aerial vehicle (UAV) for the assessment of early nitrogen (N) status and prediction of yield in winter wheat (Triticum aestivum L.). Six increasing N rates, i.e., 0, 40, 80, 120, 160, 200 kg N ha−1 were applied, half at tillering and half at shooting. Thus, when the crop N status was monitored between the two N applications, consecutive N treatments differentiated from each other by just 20 kg N ha−1. The following in-field and remote sensed parameters were compared as indicators of crop vegetative and N status: plant N% (w:w) concentration; crop N uptake (Nupt); ratio between transmitted and incident photosynthetically active radiation (PARt/PARi); leaf SPAD values, an indirect index for chlorophyll content; SAT and UAV derived NDVI. As reliable indicators of wheat N availability, in-field parameters were ranked as follows: PARt/PARi ≅ Nupt > SPAD ≅ N%. The PARt/PARi, Nupt and SPAD resulted quite strongly correlated to each other. At all crop stages, the NDVI was strongly correlated with PARt/PARi and Nupt. It is of relevance that NDVI correlated quite strongly to in-field parameters and grain yield at shooting, i.e., before the second N application, when the N rate can still be adjusted. The SAT and UAV NDVIs were strongly correlated to each other, which means they can be used alternatively depending on the context.

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“…B e r k e m b a n g n y a t e k n o l o g i penginderaan jauh (satelit dengan berbagai resolusi) dan sistem informasi geografis membuat manajemen lahan secara spasial dapat dikelola dengan mudah dan cepat. Estimasi kandungan hara nitrogen dengan menggunakan citra penginderaan jauh multispektral pada tanaman semusim telah dilakukan beberapa peneliti diantaranya pada tanaman jagung menggunakan citra Quickbird (Bausch & Khosla, 2010), pada tanaman gandum menggunakan citra Ikonos (Jia et al, 2011) dan pada tanaman gandum menggunakan citra RapidEye (Basso et al, 2016;Magney et al, 2016). Hasil penelitian tersebut menunjukkan 2 bahwa nilai koefisien determinasi (R ) lebih dari 0,70.…”

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“…F a k t o r l a i n y a n g m u n g k i n menyebabkan rendahnya korelasi antara kandungan hara nitrogen perkebunan karet dengan band tunggal dan indeks vegetasi dari citra GeoEye-1, Sentinel-2A dan Landsat 8 OLI karena variasi kandungan nitrogen antar sampel tidak terlalu tinggi dan tidak terdapat blok tanaman yang tanpa pemupukan nitrogen yang digunakan sebagai blok kontrol. Penelitian sebelumnya untuk estimasi kandungan hara nitrogen pada tanaman jagung dan gandum dilakukan dengan cara membuat variasi dosis pemupukan nitrogen yang tinggi antar blok tanaman dan membuat blok kontrol tanpa pemupukan nitrogen (Bausch & Khosla, 2010;Jia et al, 2011;Hunt et al, 2013). (Magney et al, 2016).…”

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Pengaruh Resolusi Spasial Citra Terhadap Hasil Pemetaan Kandungan Hara Nitrogen Perkebunan Karet

Saputra¹,

Kamal

Wicaksono

2018

JPK

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Nitrogen is one of the nutrients needed in large quantities by plants. Plant with nitrogen deficiency will lead to stunted growth a n d d e c r e a s e d p l a n t p r o d u c t i v i t y . Implementation of the precision farming system on fertilizer activity in rubber plantation was done by determining fertilizer dose that is made based on soil and plants nutrient content. For large areas, it is costly analysis of plant nutrients. Therefore it is needed a technology that can estimate the condition of plant nutrients quickly and low cost. Remote sensing technology is an alternative that can be used for large areas and with a fast time and it is relatively low cost. The study was aimed to obtain information effect of spatial resolution images on the results of nitrogen content mapping on rubber plantation. Multi-resolution images used include GeoEye-1 (2 m), and Landsat 8 OLI (30 m). The method used was to build semi-empirical relationship between single band and image vegetation index with rubber plant nitrogen nutrient content. The results showed that map of nitrogen content estimate of rubber plant using Sentinel-2A (SE 0,369) image had higher accuracy than using GeoEye-1 (SE 0,519) and Landsat 8 OLI images (SE 0,462).

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Nitrogen Status Estimation of Winter Wheat by Using an IKONOS Satellite Image in the North China Plain

Cited by 9 publications

References 29 publications

Satellite and Proximal Sensing to Estimate the Yield and Quality of Table Grapes

Satellite and Proximal Sensing to Estimate the Yield and Quality of Table Grapes

Reliability of Ndvi Derived by High Resolution Satellite and Uav Compared to in-Field Methods for the Evaluation of Early Crop N Status and Grain Yield in Wheat

Pengaruh Resolusi Spasial Citra Terhadap Hasil Pemetaan Kandungan Hara Nitrogen Perkebunan Karet

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