Lee F. Johnson

Mapping vineyard leaf area with multispectral satellite imagery

Vineyard leaf area is a key determinant of grape characteristics and wine quality. As is frequently the case in agriculture, available ground-based leaf area measurements employed by growers are not well suited to larger area mapping. In this study, IKONOS high spatial resolution, multispectral satellite imagery was used to map leaf area throughout two commercial wine grape vineyards (approximately 800 ha) in California’s North Coast growing region. The imagery was collected near harvest during the 2000 growing season, converted to at-sensor radiance, geo-referenced and transformed to normalized difference vegetation index (NDVI) on a per pixel basis. Measurements at 24 ground calibration sites were used to convert NDVI maps to leaf area index (LAI; m 2 leaf area m � 2 ground area); planting density was then used to express leaf area on a per vine basis (LAv). Image-based LAv was significantly correlated with ground-based LAv estimates developed at 23 validation sites (r 2 � /0.72; P B/ 0.001). Despite challenges posed by the discontinuous nature of vineyard canopies and architectural differences imposed by shoot positioning trellis systems, remote sensing appears to offer a basis for mapping vineyard leaf area in low LAI vineyards. Such maps can potentially be used to parameterize plant growth models or provide decision support for irrigation and canopy management. # 2002 Elsevier Science B.V. All rights reserved.

LEAFMOD: A New Within-Leaf Radiative Transfer Model

Abstract We describe the construction and verification of a within-leaf radiative transfer model called LEAFMOD (Leaf Experimental Absorptivity Feasibility MODel). In the model, the one-dimensional radiative transfer equation in a slab of leaf material with homogeneous optical properties is solved. When run in the forward mode, LEAFMOD generates an estimate of leaf reflectance and transmittance given the leaf thickness and optical characteristics of the leaf material (i.e., the absorption and scattering coefficients). In the inverse mode, LEAFMOD computes the total within-leaf absorption and scattering coefficient profiles from measured reflectance, transmittance, and leaf thickness. Inversions with simulated data demonstrate that the model appropriately decouples scattering and absorption within the leaf, producing fresh leaf absorption profiles with peaks at locations corresponding to the major absorption features for water and chlorophyll. Experiments with empirical input data demonstrate that the amplitude of the fresh leaf absorption coefficient profile in the visible wavebands is correlated with pigment concentrations as determined by wet chemical analyses, and that absorption features in the near-infrared wavebands related to various other biochemical constituents can be identified in a dry-leaf absorption profile.

LCM2: A coupled leaf/canopy radiative transfer model

Abstract Two radiative transfer models have been coupled to generate vegetation canopy reflectance as a function of leaf chemistry, leaf morphology (as represented by leaf scattering properties), leaf thickness, soil reflectance, and canopy architecture. A model of radiative transfer within a leaf, called LEAFMOD, treats the radiative transfer equation for a slab of optically uniform leaf material, providing an estimate of leaf hemispherical reflectance and transmittance as well as the radiance exiting the leaf surfaces. The canopy model then simulates radiative transfer within a mixture of leaves, with each having uniform optical properties as determined by LEAFMOD, assuming a bi-Lambertian leaf scattering phase function. The utility of the model, called LCM2 (Leaf/Canopy Model version 2), is demonstrated through predictions of radiometric measurements of canopy reflectance and sensitivity to leaf chlorophyll and moisture content.

Nitrogen influence on fresh-leaf NIR spectra

Abstract Simulations and measurements were used derive information on the form and strength of the nitrogen (N) “signal” in near-infrared (NIR) spectra of fresh leaves. Simulations across multiple species indicated that in total, protein absorption decreased NIR reflectance by up to 1.8% absolute and transmittance by up to 3.7% absolute, all other inputs held equal. Associated changes in spectral slope were generally of ±0.02% nm−1 absolute. Spectral effects were about an order of magnitude more subtle for a smaller, though potentially ecologically significant, change in N concentration of 0.5% absolute over measured. Nitrogen influence on spectral slope was fairly consistent across four empirical datasets as judged by wavelength dependence of N correlation, and there was reasonable agreement of observed and modeled slope perturbations with locations of known protein absorption features. Improved understanding of the form and strength of the N signal under differing conditions will support continued development of laboratory-based spectral measurement and analysis strategies for direct N estimation in individual fresh leaves. A pragmatic approach for canopy-level estimation by remote sensing, however, might additionally consider surrogate measures such as chlorophyll concentration or canopy biophysical properties.

Feasibility of Monitoring Coffee Field Ripeness with Airborne Multispectral Imagery

Multispectral images were collected by an unmanned aerial vehicle over a commercial coffee plantation during the 2002 harvest season. Selected scenes were georegistered to a base map and a mosaic of the study area was created. Image segmentation was performed to identify and mask soil, shadow, and cloud pixels. The remaining pixels, representing sunlit canopy, were assumed to be a mixture of four components: green leaf, underripe fruit, ripe fruit, and overripe fruit. Field and laboratory instruments were used to measure the reflectance spectrum of each component. Based on these spectra, a ripeness index was developed for the airborne imagery that involved computing the per-pixel ratio of digital counts in spectral channels centered at 580 and 660 nm. Results were aggregated on a per-field basis. Mean ripeness index per field was significantly correlated with ground-based counts recorded by the grower, and to eventual harvest date. The results suggest that remote sensing methods may provide an alternative, more spatially comprehensive method for monitoring ripeness status and evaluating harvest readiness of this high-value agricultural commodity.

Satellite Irrigation Management Support With the Terrestrial Observation and Prediction System: A Framework for Integration of Satellite and Surface Observations to Support Improvements in Agricultural Water Resource Management

In California and other regions vulnerable to water shortages, satellite-derived estimates of key hydrologic fluxes can support agricultural producers and water managers in maximizing the benefits of available water supplies. The Satellite Irrigation Management Support (SIMS) project combines NASAs Terrestrial Observation and Prediction System (TOPS), Landsat and MODIS satellite imagery, and surface sensor networks to map indicators of crop irrigation demand and develop information products to support irrigation management and other water use decisions. TOPS-SIMS provides the computing and data processing systems required to support automated, near real-time integration of observations from satellite and surface sensor networks, and generates data and information in formats that are convenient for agricultural producers, water managers, and other end users. Using the TOPS modeling framework to integrate data from multiple sensor networks in near real-time, SIMS currently maps crop fractional cover, basal crop coefficients, and basal crop evapotranspiration. Map products are generated at 30 m resolution on a daily basis over approximately 4 million ha of California farmland. TOPS-SIMS is a fully operational prototype, and a publicly available beta-version of the web interface is being pilot tested by farmers, irrigation consultants, and water managers in California. Data products are distributed via dynamic web services, which support both visual mapping and time-series queries, to allow users to obtain information on spatial and temporal patterns in crop canopy development and water requirements. TOPS-SIMS is an application framework that demonstrates the value of integrating multi-disciplinary Earth observation systems to provide benefits for water resource management.

Neural Network Algorithm for Coffee Ripeness Evaluation Using Airborne Images

A NASA unmanned aerial vehicle (UAV) was deployed over a commercial coffee plantation during the 2002 harvest season. An on-board digital camera system collected a set of high-resolution surface reflectance images in three spectral bands (580, 660, and 790 nm). An intelligent and robust algorithm operated on the multispectral images to estimate absolute percentages of under-ripe (green), ripe (yellow), and over-ripe (brown) coffee cherries displayed on the canopy surface. The procedure was based on a coupled leaf/canopy radiative transfer model (LCM2), modified to include fruiting bodies as photon scattering and absorbing elements. A neural network (NN) set was trained on simulated data, and then used to invert LCM2 for retrieval of fruit and leaf percentages from empirical canopy reflectance data. A projection technique was implemented to systematically mitigate situations where the observed reflectance data fell outside the NN training set domain and the inversion thus initially rendered non-physical solutions (fruit percentages outside of range 0 to 100%). The algorithm was applied to three study fields representing a broad gradient of mature (ripe plus over-ripe) fruit ranging from 28% to 61%. Correlation between predictions and yield data across all ripeness levels was 0.78, with a mean absolute error of 11% (range 1% to 26%). By comparison, a standard ground-based harvest readiness assessment produced a correlation 0.64 with yield, mean absolute error of 13% (range 5% to 23%). The procedure was designed to operate on a reasonably modest set of a priori specifications and, by coupling with remote sensing, potentially represents an efficient method for monitoring ripeness progression or other agricultural phenomena that alter visible and near-infrared crop canopy reflectance.

Solar-powered UAV mission for agricultural decision support

In September 2002, NASAs solar-powered Pathfinder-Plus UAV conducted a proof-of-concept mission over the 1500 ha plantation of the Kauai Coffee Company (KCC), Hawaii. While in U.S. National Airspace, the transponder-equipped UAV was supervised by Honolulu air traffic as a conventionally piloted aircraft. Two digital camera systems were housed in exterior-mounted environmental pods, and were controlled from a ground station established at plantation headquarters. During four hours on-station, the UAV exhibited the ability to navigate pre-planned flightlines, as well as perform spontaneous maneuvers to collect imagery in cloud-free areas. A line-of-sight (local area network) telemetry system using unlicensed radio frequency enabled rapid image download at rates exceeding 5 Mbit sec -1 . All images were thus available for viewing, enhancing, and printing within a few minutes of collection. During the latter part of the mission, the payload was operated over an established wide area network by an operator located on the U.S. mainland at a distance of 4000 km. The mission demonstrated the ability of a solar-powered UAV, equipped with downsized imaging systems, to monitor a localized region for an extended time period and deliver high-resolution imagery on demand. I. INTRODUCTION

Nighttime UAV Vineyard Mission: Challenges of See-and-Avoid in the NAS

A Nighttime UAV Vineyard Mission will demonstrate the use of a UAV-based thermal infrared imaging system for improved direction of frost damage mitigation efforts in agricultural crops. The UAV selected for this April 2005 mission is the APV-3. A flight height of 8,000 ft is planned, enabling thermal mapping coverage of the largest vineyard in California on an hourly basis. To accomplish the Nighttime Mission, it is necessary to demonstrate that the ground-based autopilot has the capability to see-and-avoid potentially conflicting aircraft in the National Airspace System (NAS). This paper provides a review of a daytime UAV test flight conducted in-visual range over the vineyard in August 2003 and describes additional tests being conducted to satisfy FAA see-and-avoid requirements for the planned out-of-visual range nighttime mission.

Seasonal cultivated and fallow cropland mapping using MODIS-based automated cropland classification algorithm

Abstract Increasing drought occurrences and growing populations demand accurate, routine, and consistent cultivated and fallow cropland products to enable water and food security analysis. The overarching goal of this research was to develop and test automated cropland classification algorithm (ACCA) that provide accurate, consistent, and repeatable information on seasonal cultivated as well as seasonal fallow cropland extents and areas based on the Moderate Resolution Imaging Spectroradiometer remote sensing data. Seasonal ACCA development process involves writing series of iterative decision tree codes to separate cultivated and fallow croplands from noncroplands, aiming to accurately mirror reliable reference data sources. A pixel-by-pixel accuracy assessment when compared with the U.S. Department of Agriculture (USDA) cropland data showed, on average, a producer’s accuracy of 93% and a user’s accuracy of 85% across all months. Further, ACCA-derived cropland maps agreed well with the USDA Farm Service Agency crop acreage-reported data for both cultivated and fallow croplands with R -square values over 0.7 and field surveys with an accuracy of ≥ 95 % for cultivated croplands and ≥ 76 % for fallow croplands. Our results demonstrated the ability of ACCA to generate cropland products, such as cultivated and fallow cropland extents and areas, accurately, automatically, and repeatedly throughout the growing season.