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Application of Spectral Remote Sensing for Agronomic Decisions

^a USDA-ARS National Soil Tilth Lab., 2110 University Blvd., Ames, IA 50011
^b CALMIT, School of Natural Resources, Univ. of Nebraska, Lincoln, NE 68583
^c USDA-ARS Soil and Water Conservation Unit, 120 Keim Hall, Univ. of Nebraska, Lincoln, NE 68583
^d USDA-ARS, National Program Staff, 5601 Sunnyside Ave., Beltsville, MD 20705

^* Corresponding author (jerry.hatfield{at}ars.usda.gov).

Remote sensing has provided valuable insights into agronomic management over the past 40 yr. The contributions of individuals to remote sensing methods have lead to understanding of how leaf reflectance and leaf emittance changes in response to leaf thickness, species, canopy shape, leaf age, nutrient status, and water status. Leaf chlorophyll and the preferential absorption at different wavelengths provides the basis for utilizing reflectance with either broad-band radiometers typical of current satellite platforms or hyperspectral sensors that measure reflectance at narrow wavebands. Understanding of leaf reflectance has lead to various vegetative indices for crop canopies to quantify various agronomic parameters, e.g., leaf area, crop cover, biomass, crop type, nutrient status, and yield. Emittance from crop canopies is a measure of leaf temperature and infrared thermometers have fostered crop stress indices currently used to quantify water requirements. These tools are being developed as we learn how to use the information provided in reflectance and emittance measurements with a range of sensors. Remote sensing continues to evolve as a valuable agronomic tool that provides information to scientists, consultants, and producers about the status of their crops. This area is still relatively new compared with other agronomic fields; however, the information content is providing valuable insights into improved management decisions. This article details the current status of our understanding of how reflectance and emittance have been used to quantitatively assess agronomic parameters and some of the challenges facing future generations of scientists seeking to further advance remote sensing for agronomic applications.

Abbreviations: Anth, anthocyanin • ARVI, atmospherically resistant vegetative index • BRDF, bidirectional reflectance distribution function • Car, carotenoids content • Chl, chlorophyll • CWSI, Crop Water Stress Index • DisALEXI, Disaggregation Atmosphere–Land Exchange Inverse • DVI, Difference Vegetative Index • ET, evapotranspiration • GLAI, green leaf area index • GPP, gross primary production • LAD, leaf angle distribution • LAI, leaf area index • NDVI, Normalized Difference Vegetative Index • NDWI, Normalized Difference Water Index • NIR, near infrared • NRI, Normalized Reflectance Index • NN neural network • OSAVI, Optimized soil-adjusted vegetative index • PRI, Photochemical Reflectance Index • PVI, Perpendicular Vegetative Index • RT, radiative transfer models • RT-NN radiative transfer–neural network • SAIL, scattering by arbitrarily inclined leaves • SAVI, soil-adjusted vegetative index • SDD, stress degree day • SIPI, structure-insensitive pigment index • SPAD, Soil–Plant Analyses Development • STD, standard deviation • SWIR, short-wave infrared • TSAVI, Transformed Soil Adjusted Vegetative Index • VF, vegetation fraction • VIs, vegetative indices • WDI, Water Deficit Index

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Received for publication December 30, 2006.