Nitrogen Status Assessment for Variable Rate Fertilization in Maize through Hyperspectral Imagery

Remote Sensing

Valentina Picchi¹  Stefano Amaducci²  Mirco Boschetti³  Roberto Colombo⁴  Cinzia Panigada⁴  Chiara Cilia⁴  Micol Rossini⁴  Lorenzo Busetto⁴  Michele Meroni⁴ 

[1] Consiglio Per la Ricerca e la Sperimentazione in Agricoltura (CRA), Research Unit of Food Technology, Via Venezian 26, 20133 Milano, Italy;Istituto di Agronomia e Coltivazioni Erbacee, Università Cattolica del Sacro Cuore, Via Emilia Parmense 84, 29100 Piacenza, Italy;Remote Sensing Department-IREA-National Research Council (CNR), Via Bassini 15, 20133 Milano, Italy;Remote Sensing of Environmental Dynamics Laboratory, Department of Earth and Environmental Science (DISAT), University of Milano-Bicocca, Piazza della Scienza 1, 20126 Milano, Italy;

关键词: Nitrogen Nutrition Index;    nitrogen concentration;    airborne;    hyperspectral;    precision farming;    vegetation indices;    variable rate fertilization;    Zea mays L.;   

DOI  :  10.3390/rs6076549

来源: DOAJ

【 摘 要 】

This paper presents a method for mapping the nitrogen (N) status in a maize field using hyperspectral remote sensing imagery. An airborne survey was conducted with an AISA Eagle hyperspectral sensor over an experimental farm where maize (Zea mays L.) was grown with two N fertilization levels (0 and 100 kg N ha−1) in four replicates. Leafand canopy field data were collected during the flight. The nitrogen (N) status has been estimated in this work based on the Nitrogen Nutrition Index (NNI), defined as the ratio between the leaf actual N concentration (%Na) of the crop and the minimum N content required for the maximum biomass production (critical N concentration (%Nc)) calculated through the dry mass at the time of the flight (Wflight). The inputs required to calculatethe NNI (i.e., %Na and Wflight) have been estimated through regression analyses betweenfield data and remotely sensed vegetation indices. MCARI/MTVI2 (Modified Chlorophyll Absorption Ratio Index/Modified Triangular Vegetation Index 2) showed the best performances in estimating the %Na (R2 = 0.59) and MTVI2 in estimating the Wflight (R2 = 0.80). The %Na and the Wflight were then mapped and used to compute the NNI map over the entire field. The NNI map agreed with the NNI estimated using field data through traditional destructive measurements (R2 = 0.70) confirming the potential of using remotely sensed indices to assess the crop N condition. Finally, a method to derive a pixel based variable rate N fertilization map was proposed as the difference between the actual N content and the optimal N content. We think that the proposed operational methodology is promising for precision farming since it represents an innovative attempt to derive a variable rate N fertilization map based on the actual crop N status from an aerial hyperspectral image.

【 授权许可】

Unknown