Application of Spectral Remote Sensing for Agronomic Decisions

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

Determination of Corn Nutrient Status under N&K Stressed Condition Using Hyperspectral Analysis

Part 1: GIS, GPS, RS and Precision FarmingInternational audienceVariable fertilization for crops, like corn, depends on monitoring nutrition condition. Thus, hyperspectral reflectance was used to predict chlorophyll and total nitrogen content under different N,K treatments during corn growth. The area of the experimental field was divided into 3 strips with different nitrogen treatment (N1:0 kg/ha–low, N2:314 kg/ha–normal, and N3:653 kg/ha–high). In each strip, there were 3 repetitions of 3 potassium treatments( K1:0 kg/ha–low, K2:214 kg/ha–normal, and K3:500 kg/ha–high). The results show that growth stages happened in advance under low nitrogen treatment and reflectance intensity, total N, and chlorophyll content are all largest under N2K1 in shooting stage. GNDVI(R2=0.88, RMSE=0.08) performs well for chlorophyll prediction under N3K2. In addition, MLR has the potential of determination of chlorophyll(R2=0.94, RMSE=0.02) and total N(R2=0.97, RMSE=0.09) in trumpet stage, as well as PLSR for chlorophyll(R2=0.99, SEC=0.01;SEP=0.09) and total N (R2=0.96, SEC=0.11;SEP=0.47) in trumpet stage