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Characterizing Water and Nitrogen Stress in Corn Using Remote Sensing
Author(s) -
Clay D. E.,
Kim KiIn,
Chang J.,
Clay S. A.,
Dalsted K.
Publication year - 2006
Publication title -
agronomy journal
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.752
H-Index - 131
eISSN - 1435-0645
pISSN - 0002-1962
DOI - 10.2134/agronj2005.0204
Subject(s) - normalized difference vegetation index , red edge , reflectivity , vegetation (pathology) , nitrogen , crop , environmental science , vegetation index , zoology , remote sensing , agronomy , mathematics , leaf area index , chemistry , geology , biology , physics , optics , medicine , organic chemistry , pathology
Interactions between water and N may impact remote‐sensing‐based N recommendations. The objectives of this study were to determine the influence of water and N stress on reflectance from a corn ( Zea mays L.) crop, and to evaluate the impacts of implementing a remote‐sensing‐based model on N recommendations. A replicated N and water treatment factorial experiment was conducted in 2002, 2003, and 2004. Yield losses due to water (YLWS) and N (YLNS) stress were determined using the 13 C discrimination (Δ) approach. Reflectance data (400–1800 nm) collected at three growth stages (V8–V9, V11–VT, and R1–R2) were used to calculate six different remote sensing indices (normalized difference vegetation index [NDVI], green normalized vegetation index, normalized difference water index [NDWI], N reflectance index, and chorophyll green and red edge indices). At the V8–V9 growth stage, increasing the N rate from 0 to 112 kg N ha −1 decreased reflectance in the blue (485 nm), green (586 nm), and red (661 nm) bands. Nitrogen had an opposite effect in the near‐infrared (NIR, 840 nm) band. At the V11–VT growth stage, reflectance in the blue, green, and red bands were lower in fertilized than unfertilized treatments. At the R1–R2 growth stage, YLWS was highly correlated ( r = 0.58, P = 0.01) with red reflectance and NDVI ( r = −0.61, P = 0.01), while YLNS was correlated with all of the indices except NDVI. A remote sensing model based on YLNS was more accurate at predicting N requirements than models based on yield or yield plus YLWS. These results were attributed to N and water having an additive effect on yield, and similar optimum N rates (100–120 kg N ha −1 ) for both moisture regimes.