Pasquale Nino , Guido D'Urso , Silvia Vanino , Claudia Di Bene , Roberta Farina , Salvatore Falanga Bolognesi , Carlo De Michele , Rosario Napoli
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The NNI is the ratio between the actual crop nitrogen content (N<sub>a</sub>) and the optimal level (N<sub>c</sub>) required for ideal growth conditions.</p><p>On ground level, Leaf Area Index (LAI), Canopy reflectance, leaf chlorophyll content (LCC) and N concentration in leaves were measured. At landscape level, LAI and Canopy Chlorophyll Indices (CIs) were derived from Sentinel 2 (S2) multispectral images captured on the same days as the ground measurements: Chlorophyll Indexes were used for estimating the canopy chlorophyll content, CCC. N<sub>a</sub> in leaves and canopy were calculated from LCC and CCC respectively.</p><p>In the study area, N<sub>c</sub> is N<sub>c</sub> = 4.65LAI<sup>−0.35</sup>, R<sup>2</sup> = 0.92. Among the tested Chlorophyll Indices (CIs) regression models, the linear regression was the more accurate to predict Na content, even though most of the tested Chlorophyll Indices (CIs) showed an R<sup>2</sup> > 0.8,a. The best-performing spectral index in both calibration and validation steps resulted from the IRECI, with R<sup>2</sup> = 0.90 and RMSE = 0.31. The developed NNI well-captured the seasonal N dynamic for durum wheat, under different N management and meteorological conditions. The NNI calculated from S2 data for crop N status assessment, showed to be an accurate estimation of the Nitrogen Nutrition Index and can be used for the fertilization plans without costly on ground measurements.</p></div>","PeriodicalId":53227,"journal":{"name":"Remote Sensing Applications-Society and Environment","volume":"36 ","pages":"Article 101323"},"PeriodicalIF":3.8000,"publicationDate":"2024-08-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Nitrogen status of durum wheat derived from Sentinel-2 satellite data in central Italy\",\"authors\":\"Pasquale Nino , Guido D'Urso , Silvia Vanino , Claudia Di Bene , Roberta Farina , Salvatore Falanga Bolognesi , Carlo De Michele , Rosario Napoli\",\"doi\":\"10.1016/j.rsase.2024.101323\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<div><p>In agriculture, nitrogen (N) is a key element in plant nutrition that affects, both positively and negatively, the productive and qualitative results of the crop. Accurate quantification of nitrogen levels is crucial for devising effective plant nutrition strategies. The objective of this study was to validate a novel method to estimate the N content at different phenological stages of durum wheat (<em>Triticum durum</em> Desf. cv. Iride) under different N management strategies (chemical synthetic fertilizer - SYN and organic fertilizer - ORG) in Italy, using the Nitrogen Nutrition Index (NNI)as a diagnostic tool for improving nitrogen fertilization timing and doses. The NNI is the ratio between the actual crop nitrogen content (N<sub>a</sub>) and the optimal level (N<sub>c</sub>) required for ideal growth conditions.</p><p>On ground level, Leaf Area Index (LAI), Canopy reflectance, leaf chlorophyll content (LCC) and N concentration in leaves were measured. 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引用次数: 0
摘要
在农业中，氮（N）是植物营养的关键元素，对作物的产量和质量都有正反两方面的影响。氮含量的精确定量对于制定有效的植物营养策略至关重要。本研究的目的是验证一种新方法，以估算意大利硬粒小麦（Triticum durum Desf. cv. Iride）在不同氮肥管理策略（化学合成肥料 - SYN 和有机肥料 - ORG）下不同物候期的氮含量，并将氮营养指数（NNI）作为改进氮肥施肥时间和剂量的诊断工具。氮营养指数是作物实际含氮量（Na）与理想生长条件所需的最佳含氮量（Nc）之间的比率。在地面上，测量了叶面积指数（LAI）、冠层反射率、叶片叶绿素含量（LCC）和叶片中的氮浓度。在景观层面，叶面积指数和树冠叶绿素指数（CIs）是根据哨兵 2 号（S2）多光谱图像得出的：叶绿素指数用于估算冠层叶绿素含量（CCC）。在研究区域，叶片和冠层中的 Na 分别由 LCC 和 CCC 计算得出。在测试的叶绿素指数（CIs）回归模型中，尽管大多数测试的叶绿素指数（CIs）都显示出 R2 > 0.8,a，但线性回归预测 Na 含量更为准确。IRECI 是校准和验证步骤中表现最好的光谱指数，R2 = 0.90，RMSE = 0.31。所开发的 NNI 很好地捕捉了不同氮管理和气象条件下硬质小麦的季节性氮动态。根据 S2 数据计算出的用于作物氮状况评估的 NNI 表明是对氮营养指数的准确估算，可用于施肥计划，而无需昂贵的实地测量。
Nitrogen status of durum wheat derived from Sentinel-2 satellite data in central Italy
In agriculture, nitrogen (N) is a key element in plant nutrition that affects, both positively and negatively, the productive and qualitative results of the crop. Accurate quantification of nitrogen levels is crucial for devising effective plant nutrition strategies. The objective of this study was to validate a novel method to estimate the N content at different phenological stages of durum wheat (Triticum durum Desf. cv. Iride) under different N management strategies (chemical synthetic fertilizer - SYN and organic fertilizer - ORG) in Italy, using the Nitrogen Nutrition Index (NNI)as a diagnostic tool for improving nitrogen fertilization timing and doses. The NNI is the ratio between the actual crop nitrogen content (Na) and the optimal level (Nc) required for ideal growth conditions.
On ground level, Leaf Area Index (LAI), Canopy reflectance, leaf chlorophyll content (LCC) and N concentration in leaves were measured. At landscape level, LAI and Canopy Chlorophyll Indices (CIs) were derived from Sentinel 2 (S2) multispectral images captured on the same days as the ground measurements: Chlorophyll Indexes were used for estimating the canopy chlorophyll content, CCC. Na in leaves and canopy were calculated from LCC and CCC respectively.
In the study area, Nc is Nc = 4.65LAI−0.35, R2 = 0.92. Among the tested Chlorophyll Indices (CIs) regression models, the linear regression was the more accurate to predict Na content, even though most of the tested Chlorophyll Indices (CIs) showed an R2 > 0.8,a. The best-performing spectral index in both calibration and validation steps resulted from the IRECI, with R2 = 0.90 and RMSE = 0.31. The developed NNI well-captured the seasonal N dynamic for durum wheat, under different N management and meteorological conditions. The NNI calculated from S2 data for crop N status assessment, showed to be an accurate estimation of the Nitrogen Nutrition Index and can be used for the fertilization plans without costly on ground measurements.
期刊介绍：
The journal ''Remote Sensing Applications: Society and Environment'' (RSASE) focuses on remote sensing studies that address specific topics with an emphasis on environmental and societal issues - regional / local studies with global significance. Subjects are encouraged to have an interdisciplinary approach and include, but are not limited by: " -Global and climate change studies addressing the impact of increasing concentrations of greenhouse gases, CO2 emission, carbon balance and carbon mitigation, energy system on social and environmental systems -Ecological and environmental issues including biodiversity, ecosystem dynamics, land degradation, atmospheric and water pollution, urban footprint, ecosystem management and natural hazards (e.g. earthquakes, typhoons, floods, landslides) -Natural resource studies including land-use in general, biomass estimation, forests, agricultural land, plantation, soils, coral reefs, wetland and water resources -Agriculture, food production systems and food security outcomes -Socio-economic issues including urban systems, urban growth, public health, epidemics, land-use transition and land use conflicts -Oceanography and coastal zone studies, including sea level rise projections, coastlines changes and the ocean-land interface -Regional challenges for remote sensing application techniques, monitoring and analysis, such as cloud screening and atmospheric correction for tropical regions -Interdisciplinary studies combining remote sensing, household survey data, field measurements and models to address environmental, societal and sustainability issues -Quantitative and qualitative analysis that documents the impact of using remote sensing studies in social, political, environmental or economic systems
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