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Spectral indices from aerial images and their relationship with properties of a corn crop

机译:来自航拍图像的光谱指数及其与玉米作物特性的关系

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Identification of areas with similar restrictions to crop productivity could improve the efficiency to manage agricultural systems, guarantee stable yields, and reduce the effect of droughts in rainfed systems. The ability of any vegetation index to discriminate N and moisture-related changes in leaf reflectance would present an important advantage over the present diagnostic system which involves soil-testing for moisture and available N. The purpose of the study was to calibrate different vegetation indices regarding their capacity to identify water and nitrogen availability for rainfed corn crops in the semiarid Pampas of Argentina. A field experiment with corn with a control without fertilization (N0), and fertilized with 120kgha(-1) of nitrogen (N120) was used. Two sites, Low (L) and High (H), were identified within the field, according to their altimetry, a multi-spectral aerial photography was taken from a manned airplane during flowering stage of the corn crop, and four spectral indices were calculated (NDVI, green NDVI, NGRDI, (NIR/GREEN)-1). At six georeferenced points at each site soil texture, organic matter, available phosphorus, nitrogen and moisture contents as well as corn aerial biomass and grain yield were determined. The two sites differed in most of the evaluated soil properties, crop biomass and grain yield. The spectral information obtained at crop flowering showed clear differences between sites H and L for all four indices, indicating that any of these would be able to detect the differences in soil moisture and fertility among these environments. Both (NIR/GREEN)-1 and green NDVI had the best correlation with crop yield determined in the field, and therefore could be considered most appropriate for estimating corn yields from images taken at flowering. For estimation of N requirements, green NDVI differentiated best between fertilized and non-fertilized crop in the moisture limited environment (H), while (NIR/GREEN)-1 performed better in the site where soil moisture was non-limiting (L).
机译:鉴定作物生产力具有类似限制的地区可以提高管理农业系统的效率,保证稳定的产量,并降低干旱在雨量系统中的效果。任何植被指数辨别N和叶片反射率的水分相关变化的能力将对本诊断系统的重要优势产生了涉及水分土壤测试和可用的N。该研究的目的是校准有关不同植被指数他们在阿根廷半干旱棉花覆盖玉米作物的水和氮可用性的能力。使用玉米与玉米的田间试验(N0),并用120kgha(-1)施用氮气(N120)。根据它们的高度测定,在该领域内识别出两个位点,低(L)和高(H),从玉米作物的开花阶段,从载有载有载物的飞机中取出多光谱航程摄影,并计算四个光谱指标(ndvi,绿色ndvi,ngrdi,(nir / green)-1)。在每个地点土壤质地,有机物,可用磷,氮气和水分含量以及玉米空中生物质和籽粒产量的六个地理位置点。两种位点在大多数评估的土壤性质,作物生物质和籽粒产量不同。在作物开花中获得的光谱信息显示出所有四个指数的位置H和L之间的明显差异,表明其中任何一个都能够检测这些环境中的土壤水分和生育率的差异。 (NIR / Green)-1和绿色NDVI都与该领域中确定的作物产量具有最佳相关性,因此可以认为最适合估计从开花拍摄的图像的玉米产量。为了估计N要求,绿色NDVI在水分有限环境(H)中的受精和非受精作物之间的最佳差异,而在土壤水分是非限制性(L)的场地上更好地进行(NIR / Green)-1。

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