Estimating biomass of winter wheat using narrowband vegetation indices for precision agriculture

An important part of wheat harvest planning is to have good understanding of the field where harvesting operation is to be conducted. Terrain characteristics influence biomass product. This information has been challenging to view of the area to be worked. Precision agriculture is about collecting timely geospatial information on soil-plant relations and prescribing an applying site-specific treatments to increase agricultural production and protect the environment. Precision farming should be applied in order to achieve sustainable agriculture. The development and implementation of site-specific farming has been made possible by combining geographic information systems (GIS) and hyperspectral remote sensing. In this research we introduce one existing problem that could be solved based on application of hyperspectral remote sensing. Digital images were taken by an Aisa EAGLE II hyperspectral sensor, which produced images with 253 contiguous bands (400-1000 nm), a spectral sampling of 2.5nm bandwidth, and a ground pixel size of 1m. In our work narrowband vegetation indices (VI) were calculated from high resolution aerial hyperspectral images for estimating the biomass of winter wheat in an agricultural area. Narrow band’s NDVI were computed for each combination of NIR and Red bands. Regression model was computed between NDVI’s and field samples, where 625nm and 720nm bands produced the strongest relationship with biomass values (n=9, R2=0.762, p<0.05). -------------------------------------------------------------------- A szantofoldi gabonatermesztes tervezesenek fontos resze a gazdalkodasi terulet megismerese. A teruleti jellemzők befolyasoljak a biomassza produktumot. Ez az informacio kihivast jelent a gazdalkodas teruleti tervezeseben. A precizios novenytermesztes osszegyűjti az aktualis terinformatikai informaciokat a talaj-noveny kapcsolatrendszereről es meghatarozza az alkalmazhato helyspecifikus kezeleseket, amelyek novelik a mezőgazdasagi termeles merteket es a kornyezetet is vedi. A precizios gazdalkodas alkalmazhato a fenntarthato mezőgazdasagi fejlődes elerese erdekeben. A helyspecifikus gazdalkodasi modszer kidolgozasa es vegrehajtasa lehetőve tette a geoinformacios rendszerek (GIS) es a hiperspektralis taverzekeles otvozeset. Jelen kutatasban szeretnenk bemutatni egy olyan gyakorlati peldat, amely megoldasara taverzekelesből nyert informaciok segitsegevel kezelhetők. A felvetelek AISA EAGLE II tipusu hiperspektralis szenzorral keszultek lathato es kozeli infravoros tartomanyban (400-1000 nm), 2,5 nm-es spektralis mintavetelezessel es 1 meter terepi felbontassal, az igy elkeszult felvetelek 253 db spektralis csatornat tartalmaztak. Munkank soran nagy felbontasu legi hiperspektralis felvetelekből szamitott keskenysavu vegetacios indexek (VI) segitsegevel kovetkeztettunk az őszi buza biomassza mennyisegere egy mezőgazdasagi teruleten. A keskenysavu NDVI szamitasahoz a voros-el szamitast es az osszes csatornakombinaciot teszteltuk a voros es a kozeli infravoros tartomanyokban a nedves biomassza-hozam becslesere. A legszorosabb regressziot a nedves biomassza es a mintaterulet hiperspektralis felvetel pixelei kozott a 625nm voros es a 720nm-es kozeli infravoros csatornakbol szamitott keskeny savu NDVI alkalmazasaval kaptuk.