Philippe Vigneault

Remote Estimation of Crop Chlorophyll Content Using Spectral Indices Derived From Hyperspectral Data

This paper examines the use of simulated and measured canopy reflectance for chlorophyll estimation over crop canopies. Field spectral measurements were collected over corn and wheat canopies in different intensive field campaigns organized during the growing seasons of 2004 and 2005. They were used to test and evaluate several combined indices for chlorophyll determination using hyperspectral imagery (Compact Airborne Spectrographic Imager). Several index combinations were investigated using both PROSPECT-SAILH canopy simulated spectra and field-measured reflectances. The relationships between leaf chlorophyll content and combined optical indices have shown similar trends for both PROSPECT-SAILH simulated data and ground-measured data sets, which indicates that both spectral measurements and radiative transfer models hold comparable potential for the quantitative retrieval of crop foliar pigments. The data set used has shown that crop type had a clear influence on the establishment of predictive equations as well as on their validation. In addition to generating different predictive equations, corn and wheat data yielded contrasting agreement between estimated and measured chlorophyll contents even for the same predictive algorithm. Among the set of indices tested in this paper, index combinations like modified chlorophyll absorption ratio index/optimized soil-adjusted vegetation index (OSAVI), triangular chlorophyll index/OSAVI, moderate resolution imaging spectrometer terrestrial chlorophyll index/improved soil-adjusted vegetation index (MSAVI), and red-edge model/MSAVI seem to be relatively consistent and more stable as estimators of crop chlorophyll content.

A comparison of crop data measured by two commercial sensors for variable-rate nitrogen application

Nitrogen (N) fertilizer rates applied spatially according to crop requirements can improve the efficiency of N use. The study compares the performance of two commercial sensors, the Yara N-Sensor/FieldScan (Yara International ASA, Germany) and the GreenSeeker (NTech Industries Inc., Ukiah, California, USA), for assessing the status of N in spring wheat (Triticum aestivum L.) and corn (Zea mays L.). Four experiments were conducted at different locations in Quebec and Ontario, Canada. The normalized difference vegetation index (NDVI) was determined with the two sensors at specific growth stages. The NDVI values derived from Yara N-Sensor/FieldScan correlated with those from GreenSeeker, but only at the early growth stages, where the NDVI values varied from 0.2 to 0.6. Both sensors were capable of describing the N condition of the crop or variation in the stand, but each sensor had its own sensitivity characteristics. It follows that the algorithms developed with one sensor for variable-rate N application cannot be transferred directly to another sensor. The Yara N-Sensor/FieldScan views the crop at an oblique angle over the rows and detects more biomass per unit of soil surface compared to the GreenSeeker with its nadir (top-down) view of the crop. The Yara N-Sensor/FieldScan should be used before growth stage V5 for corn during the season if NDVI is used to derive crop N requirements. GreenSeeker performed well where NDVI values were >0.5. However, unlike GreenSeeker, the Yara N-Sensor/FieldScan can also record spectral information from wavebands other than red and near infrared, and more vegetation indices can be derived that might relate better to N status than NDVI.

Fuzzy logic approach for spatially variable nitrogen fertilization of corn based on soil, crop and precipitation information

A fuzzy Inference System (FIS) was developed to generate recommendations for spatially variable applications of nitrogen (N) fertilizer using soil, plant and precipitation information. Experiments were conducted over three seasons (2005-2007) to assess the effects of soil electrical conductivity (ECa), nitrogen sufficiency index (NSI), and precipitations received in the vicinity of N fertilizers application, on response to N measured at mid-season growth. Another experiment was conducted in 2010 to understand the effect of water supply (WS) on response to N, using a spatially variable irrigation set-up. Better responses to N were observed in the case of high ECa, low NSI and high WS. In the opposite cases (low ECa, high NSI or low WS), nitrogen fertilizer rates can be reduced. Using fuzzy logic, expert knowledge was formalized as a set of rules involving ECa, NSI and cumulative precipitations to estimate economically optimal N rates (EONR).

Estimation of Plant Chlorophyll using Hyperspectral Observations and Radiative Transfer Models: Spectral Indices Sensitivity and Crop-Type Effects

This study aims at using forward model simulations and ground-measurements (biophysical and spectral) to estimate chlorophyll concentration from hyperspectral data and imagery. Hence, intensive field campaigns were organized during the growing seasons of 2000, 2004, and 2005 in order to collect ground spectra and corresponding leaf chlorophyll content values, and crop growth status, as well as CASI (Compact Airborne Spectrographic Imager) hyperspectral images. Acquisition dates were planned to coincide with different phenological development stages, to monitor temporal changes in crop biophysical attributes. Field spectral measurements collected were used to test and evaluate several combined indices for chlorophyll determination using hyperspectral imagery. Several index combinations were investigated using both PROSPECT-SAILH canopy simulated spectra and field measured reflectances. The relationships between leaf chlorophyll content and combined optical indices showed similar trends for both PROSPECT-SAILH simulated data and ground measured datasets. The dataset used showed that crop type had a clear influence on the establishment of predictive equations as well as on their validation. In addition to generating different predictive equations, corn and wheat data yielded contrasting agreement between estimated and measured chlorophyll contents even for the same predictive algorithm. Among the set of indices tested in this study, index combinations MCARI/OSAVI, TCI/OSAVI, MTCI/MSAVI, and R-M/MSAVI were found relatively consistent and more stable as estimators of crop chlorophyll content.

Linear spectral unmixing for crop and soil information extraction from a single worldview-2 image

Spatial information related to vegetation status and soil properties is needed in precision farming, especially early in the growing season. At these stages, vegetation has already emerged while soil is also visible in multispectral EO images. In this paper, linear spectral unmixing is applied to an 8 bands WorldView-2 image to extract information on both vegetation and soil acquired at in-season nitrogen sidedress stage in 2010 and 2011 for four corn fields located in the Montérégie region of Quebec, Canada. EO derived soil properties were strongly correlated to ECa. Correlation between dark soil abundance and ECa reached R=0.9 and correlation between bright soil abundance and ECa was about R=-0.7. Vegetation abundance for combined data of several fields was better correlated to measured biomass than NDVI and SAVI. The possibility to get valuable soil and plant information from a single multispectral image offers an interesting cost reduction opportunity for precision farming applications.