scholarly journals COMPARING BROAD-BAND AND RED EDGE-BASED SPECTRAL VEGETATION INDICES TO ESTIMATE NITROGEN CONCENTRATION OF CROPS USING CASI DATA

2016 ◽  
Vol XLI-B7 ◽  
pp. 137-143 ◽  
Author(s):  
Yanjie Wang ◽  
Qinhong Liao ◽  
Guijun Yang ◽  
Haikuan Feng ◽  
Xiaodong Yang ◽  
...  
Keyword(s):  
Nitrogen Concentration ◽  
Vegetation Indices ◽  
Broad Band ◽  
Image Data ◽  
N Fertilization ◽  
Physiological Status ◽  
Red Edge ◽  
Spectral Vegetation Indices ◽  
Crop Types ◽  
Edge Based

In recent decades, many spectral vegetation indices (SVIs) have been proposed to estimate the leaf nitrogen concentration (LNC) of crops. However, most of these indices were based on the field hyperspectral reflectance. To test whether they can be used in aerial remote platform effectively, in this work a comparison of the sensitivity between several broad-band and red edge-based SVIs to LNC is investigated over different crop types. By using data from experimental LNC values over 4 different crop types and image data acquired using the Compact Airborne Spectrographic Imager (CASI) sensor, the extensive dataset allowed us to evaluate broad-band and red edge-based SVIs. The result indicated that NDVI performed the best among the selected SVIs while red edge-based SVIs didn’t show the potential for estimating the LNC based on the CASI data due to the spectral resolution. In order to search for the optimal SVIs, the band combination algorithm has been used in this work. The best linear correlation against the experimental LNC dataset was obtained by combining the 626.20nm and 569.00nm wavebands. These wavelengths correspond to the maximal chlorophyll absorption and reflection position region, respectively, and are known to be sensitive to the physiological status of the plant. Then this linear relationship was applied to the CASI image for generating an LNC map, which can guide farmers in the accurate application of their N fertilization strategies.

scholarly journals COMPARING BROAD-BAND AND RED EDGE-BASED SPECTRAL VEGETATION INDICES TO ESTIMATE NITROGEN CONCENTRATION OF CROPS USING CASI DATA

2016 ◽  
Vol XLI-B7 ◽  
pp. 137-143
Author(s):  
Yanjie Wang ◽  
Qinhong Liao ◽  
Guijun Yang ◽  
Haikuan Feng ◽  
Xiaodong Yang ◽  
...  
Keyword(s):  
Nitrogen Concentration ◽  
Vegetation Indices ◽  
Broad Band ◽  
Image Data ◽  
N Fertilization ◽  
Physiological Status ◽  
Red Edge ◽  
Spectral Vegetation Indices ◽  
Crop Types ◽  
Edge Based

In recent decades, many spectral vegetation indices (SVIs) have been proposed to estimate the leaf nitrogen concentration (LNC) of crops. However, most of these indices were based on the field hyperspectral reflectance. To test whether they can be used in aerial remote platform effectively, in this work a comparison of the sensitivity between several broad-band and red edge-based SVIs to LNC is investigated over different crop types. By using data from experimental LNC values over 4 different crop types and image data acquired using the Compact Airborne Spectrographic Imager (CASI) sensor, the extensive dataset allowed us to evaluate broad-band and red edge-based SVIs. The result indicated that NDVI performed the best among the selected SVIs while red edge-based SVIs didn’t show the potential for estimating the LNC based on the CASI data due to the spectral resolution. In order to search for the optimal SVIs, the band combination algorithm has been used in this work. The best linear correlation against the experimental LNC dataset was obtained by combining the 626.20nm and 569.00nm wavebands. These wavelengths correspond to the maximal chlorophyll absorption and reflection position region, respectively, and are known to be sensitive to the physiological status of the plant. Then this linear relationship was applied to the CASI image for generating an LNC map, which can guide farmers in the accurate application of their N fertilization strategies.

Improving estimation of summer maize nitrogen status with red edge-based spectral vegetation indices

2014 ◽  
Vol 157 ◽  
pp. 111-123 ◽  
Author(s):  
Fei Li ◽  
Yuxin Miao ◽  
Guohui Feng ◽  
Fei Yuan ◽  
Shanchao Yue ◽  
...  
Keyword(s):  
Vegetation Indices ◽  
Summer Maize ◽  
Nitrogen Status ◽  
Red Edge ◽  
Spectral Vegetation Indices ◽  
Edge Based

scholarly journals Monitoring and predicting crop growth and analysing agricultural ecosystems by remote sensing

1996 ◽  
Vol 5 (3) ◽  
pp. 367-376 ◽  
Author(s):  
Tsuyoshi Akiyama ◽  
Y. Inoue ◽  
M. Shibayama ◽  
Y. Awaya ◽  
N. Tanaka
Keyword(s):  
Remote Sensing ◽  
Near Infrared ◽  
Vegetation Indices ◽  
Nutrient Deficiency ◽  
The Arctic ◽  
Ground Vegetation ◽  
Spectral Vegetation Indices ◽  
Project Monitoring ◽  
Crop Types ◽  
Crop Discrimination

LANDSAT/TM data, which are characterized by high spectral/spatial resolutions, are able to contribute to practical agricultural management. In the first part of the paper, the authors review some recent applications of satellite remote sensing in agriculture. Techniques for crop discrimination and mapping have made such rapid progress that we can classify crop types with more than 80% accuracy. The estimation of crop biomass using satellite data, including leaf area, dry and fresh weights, and the prediction of grain yield, has been attempted using various spectral vegetation indices. Plant stresses caused by nutrient deficiency and water deficit have also been analysed successfully. Such information may be useful for farm management. In the latter half of the paper, we introduce the Arctic Science Project, which was carried out under the Science and Technology Agency of Japan collaborating with Finnish scientists. In this project, monitoring of the boreal forest was carried out using LANDSAT data. Changes in the phenology of subarctic ground vegetation, based on spectral properties, were measured by a boom-mounted, four-band spectroradiometer. The turning point dates of the seasonal near-infrared (NIR) and red (R) reflectance factors might indicate the end of growth and the beginning of autumnal tints, respectively.

Monitoring Mediterranean grass phenology from digital terrestrial camera and Sentinel-2 vegetation indices in an oak-grass savanna ecosystem

2020 ◽  
Author(s):  
Maria P. González-Dugo ◽  
Pedro J. Gómez-Giraldez ◽  
María J. Pérez-Palazón ◽  
María J. Polo
Keyword(s):  
Vegetation Indices ◽  
Broad Band ◽  
Primary Source ◽  
Hydrological Regime ◽  
Agroforestry System ◽  
Red Edge ◽  
Annual Grasslands ◽  
The Difference ◽  
Phenological Dates ◽  
Sentinel 2

<p>Annual grasslands are an essential component of Mediterranean oak savannas, the most extensive agroforestry system in Europe, as the primary source of fodder for livestock and wildlife. Monitoring its phenology is key to adequately assess the impacts of global warming on different time scales and identify pre-critical states in the framework of early warning decision making systems. The natural variability of the climatic-hydrological regime in these areas and the usually complex spatial patterns of the vegetation, with sparse distribution and multiple layers, encourage the exploitation of available data from remote sensing sources. This work presents an assessment of vegetation indexes (VI) from Sentinel-2 validated against field data from terrestrial photography in an oak-grass system in southern Spain as a multi-approach method to monitor phenology in grass pastures. The analysis also has provided an insight into the links of the phenology dynamics with hydrological variables under these conditions.</p><p>From December 2017 to May 2019 a quantitative value of grassland greenness was computed using the Green Chromatic Coordinate (GCC) index. The phenological parameters of the start of the season (SOS), the peak of the season (POS) and end of the season (EOS) were extracted using the 50% amplitude method and confirmed using field photography. These values were compared with those provided by eight VI's derived from Sentinel-2 (NDVI, GNDVI, SAVI, EVI, EVI2, MTCI, IRECI and S2REP) and the difference in days between the key phenological dates were estimated. The results showed that for annual grasslands NDVI was the index providing estimations closest to those of ground GCC, with differences below 10 days for all phenological dates and the best correlation with GCC values (r = 0.83, p <0.001). None of the VIs using bands in the red-edge region have improved the NDVI results. Two of them, MTCI and S2REP, followed a different trend that the rest of explored indices, presenting a high temporal variability. The high diversity of species, typical of Mediterranean grasslands, might explain the high variability observed in these values. However, the third index using red-edge bands, IRECI, presented a high correlation with GCC. In this case, the index was designed to focus on the chlorophyll content of the canopy instead of the leaf scale addressed by S2REP. The influence of the vegetation ground coverage and foliage density is then higher and more similar to the broad-band indices. GNDVI also provided good general results. Soil moisture (SM) time-series were also used to estimate phenology and have presented a good agreement with GCC in SOS and EOS estimations, with SM reaching threshold values a few days before greenness ones, as measured by GCC. However, SM was not a good indicator of the POS, presenting significant biases with respect to GCC estimations.</p>

Monitoring crop N status by using red edge-based indices

2017 ◽  
Vol 8 (2) ◽  
pp. 338-342 ◽  
Author(s):  
J. González-Piqueras ◽  
H. Lopez-Corcoles ◽  
S. Sánchez ◽  
J. Villodre ◽  
V. Bodas ◽  
...  
Keyword(s):  
Vegetation Indices ◽  
Growth Cycle ◽  
N Content ◽  
Spectral Bands ◽  
Red Edge ◽  
Use Efficiency ◽  
Edge Based ◽  
Linear Correlations ◽  
N Status ◽  
Sentinel 2

Intensive agriculture has the objective to increase nutrients use efficiency. Nitrogen (N) is a key nutrient for crops and the estimations of crop N status allow adjusting the fertilization levels to crop requirements, while reducing the environmental costs and optimizing the benefits for farmers. In this work the N status of wheat in a commercial plot has been monitored, varying the N supply taking into account the variability of the soil. The N content in the cover has been monitored simultaneously by sampling at field level and by using vegetation indices based on reflectance in the red-edge band. The results of the field campaign along a crop growth cycle show that the REP, MTCI, AIVI and CCCI calculated from narrow spectral bands show good linear correlations (R2>0.93) with respect to N content (g·m−2). These indices are stable when passing to broad bands as the case of Sentinel 2 with R2>0.9.

Using portable RapidSCAN active canopy sensor for rice nitrogen status diagnosis

2017 ◽  
Vol 8 (2) ◽  
pp. 349-352 ◽  
Author(s):  
J. Lu ◽  
Y. Miao ◽  
W. Shi ◽  
J. Li ◽  
J. Wan ◽  
...  
Keyword(s):  
Northeast China ◽  
Vegetation Index ◽  
Normalized Difference Vegetation Index ◽  
Stem Elongation ◽  
Vegetation Indices ◽  
Sanjiang Plain ◽  
N Nutrition ◽  
Red Edge ◽  
Edge Based ◽  
On Farm

The objective of this study was to determine how much improvement red edge-based vegetation indices (VIs) obtained with the RapidSCAN sensor would achieve for estimating rice nitrogen (N) nutrition index (NNI) at stem elongation stage (SE) as compared with commonly used normalized difference vegetation index (NDVI) and ratio vegetation index (RVI) in Northeast China. Sixteen plot experiments and seven on-farm experiments were conducted from 2014 to 2016 in Sanjiang Plain, Northeast China. The results indicated that the performance of red edge-based VIs for estimation of rice NNI was better than NDVI and RVI. N sufficiency index calculated with RapidSCAN VIs (NSI_VIs) (R2=0.43–0.59) were more stable and more strongly related to NNI than the corresponding VIs (R2=0.12–0.38).

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