An Evaluation of MODIS 250-m Data for Green LAI Estimation in Crops

Green leaf area index (LAI) is an important variable for climate modeling, estimates of primary production, agricultural yield forecasting, and many other diverse applications. Remotely sensed data provide considerable potential for estimating LAI at local, regional, and global scales. The goal of this study was to retrieve green LAI from MODIS 250-m vegetation index (VI) data for irrigated and rainfed maize and soybeans. The performance of both MODIS-derived NDVI and Wide Dynamic Range Vegetation Index (WDRVI) were evaluated across three growing seasons (2002 through 2004) over a wide range of LAI and also compared to the performance of NDVI and WDRVI derived from reflectance data collected at close-range across the same field locations. The NDVI vs. LAI relationship showed asymptotic behavior with a sharp decrease in the sensitivity of the NDVI to LAI exceeding 2 m2/m2 for both crops. WDRVI vs. LAI relation was linear across the entire range of LAI variation with determination coefficients above 0.93. Importantly, the coefficients of the close-range WDRVI vs. LAI equation and the MODIS-retrieved WDRVI vs. LAI equation were very close. The WDRVI was found to be capable of accurately estimating LAI across a much greater LAI range than the NDVI and can be used for assessing even slight variations in LAI, which are indicative of the early stages of plant stress. These results demonstrate the new possibilities for analyzing the spatio-temporal variation of the LAI of crops using multi-temporal MODIS 250-m imagery

Remote estimation of leaf area index and green leaf biomass in maize canopies

Leaf area index (LAI) is an important variable for climate modeling, estimates of primary production, agricultural yield forecasting, and many other diverse studies. Remote sensing provides a considerable potential for estimating LAI at local to regional and global scales. Several spectral vegetation indices have been proposed, but their capacity to estimate LAI is highly reduced at moderate-to- high LAI. In this paper, we propose a technique to estimate LAI and green leaf biomass remotely using reflectances in two spectral channels either in the green around 550 nm, or at the red edge near 700 nm, and in the NIR (beyond 750 nm). The technique was tested in agricultural fields under a maize canopy, and proved suitable for accurate estimation of LAI ranging from 0 to more than 6

Remote estimation of leaf area index and green leaf biomass in maize canopies

Leaf area index (LAI) is an important variable for climate modeling, estimates of primary production, agricultural yield forecasting, and many other diverse studies. Remote sensing provides a considerable potential for estimating LAI at local to regional and global scales. Several spectral vegetation indices have been proposed, but their capacity to estimate LAI is highly reduced at moderate-to- high LAI. In this paper, we propose a technique to estimate LAI and green leaf biomass remotely using reflectances in two spectral channels either in the green around 550 nm, or at the red edge near 700 nm, and in the NIR (beyond 750 nm). The technique was tested in agricultural fields under a maize canopy, and proved suitable for accurate estimation of LAI ranging from 0 to more than 6

Elements of an Integrated Phenotyping System for Monitoring Crop Status at Canopy Level

Great care is needed to obtain spectral data appropriate for phenotyping in a scientifically rigorous manner. This paper discusses the procedures and considerations necessary and also suggests important pre-processing and analytical steps leading to real-time, non-destructive assessment of crop biophysical characteristics. The system has three major components: (1) data-collection platforms (with a focus on backpack and tractor-mounted units) including specific instruments and their configurations; (2) data-collection and display software; and (3) standard products depicting crop-biophysical characteristics derived using a suite of models to transform the spectral data into accurate, reliable biophysical characteristics of crops, such as fraction of green vegetation, absorbed photosynthetically active radiation, leaf area index, biomass, chlorophyll content and gross primary production. This system streamlines systematic data acquisition, facilitates research, and provides useful products for agriculture

Remote Estimation of Net Ecosystem CO2 Exchange in Crops: Principles, Technique Calibration and Validation

Accurate and synoptic estimation of spatially distributed CO2 fluxes is of great importance for regional and global studies of carbon balance. A technique solely based on remotely sensed data was developed and tested for estimating net ecosystem CO2 exchange (NEE) in maize and soybean. The model is based on the reflectance in two spectral channels: the near-infrared and either the green or red-edge around 700 nm. The technique provides accurate estimations of mid-day NEE in both crops under either rainfed or irrigated conditions, explaining more than 85% of NEE variation in maize and more than 81% in soybean, and shows great potential for remotely tracking crop NEE

Three-Band Model for Noninvasive Estimation of Chlorophyll Carotenoids and Anthocyanin Contents in Higher Plant Leaves

Leaf pigment content and composition provide important information about plant physiological status. Reflectance measurements offer a rapid, nondestructive technique to estimate pigment content. This paper describes a recently developed three-band conceptual model capable of remotely estimating total of chlorophylls, carotenoids and anthocyanins contents in leaves from many tree and crop species. We tuned the spectral regions used in the model in accord with pigment of interest and the optical characteristics of the leaves studied, and showed that the developed technique allowed accurate estimation of total chlorophylls, carotenoids and anthocyanins, explaining more than 91%, 70% and 93% of pigment variation, respectively. This new technique shows a great potential for noninvasive tracking of the physiological status of vegetation and the impact of environmental changes

Collecting Spectral Data over Cropland Vegetation Using Machine-Positioning versus Hand-Positioning of the Sensor

The paper describes an all-terrain motorized platform for deploying sensors and compares data collected by means of that system with those collected by means of hand-held sensors. The results not only highlight the greater variability in spectra that can be expected when deploying field radiometers by hand but, more importantly, they quantify the difference. Researchers should be aware of the potential for diminishing the validity of findings based on reflectance spectra acquired by means of hand-held sensors

Remote Estimation of Vegetation Fraction in Corn Canopies

The aim of the paper was to test two new techniques that make use of channels in the visible range of the spectrum only to estimate vegetation fraction in corn canopies. High spectral resolution radiometers were employed to measure spectral reflectance, and the information content of spectra was investigated. Radiances in spectral channels of MODIS and MERIS were used to calculate Visible Atmospherically Resistant Indices, VARIgreen=(Rgreen- Rred)/(Rgreen+Rred-Rblue) and VARI700=(R700-1.7*Rred+0.7*Rblue)/(R700+2.3*Rred-1.3*Rblue). The indices allowed for estimation of vegetation fraction with less than 10% error. One other technique was based on the well-documented approach for fully closed canopies involving the high degree of covariance for paired reflectances at 550 nm vs. 700 nm and at 500 nm vs. 670 nm. The coordinate location within the resulting spectral space was used as a measure of vegetation fraction

Elements of an Integrated Phenotyping System for Monitoring Crop Status at Canopy Level

Great care is needed to obtain spectral data appropriate for phenotyping in a scientifically rigorous manner. This paper discusses the procedures and considerations necessary and also suggests important pre-processing and analytical steps leading to real-time, non-destructive assessment of crop biophysical characteristics. The system has three major components: (1) data-collection platforms (with a focus on backpack and tractor-mounted units) including specific instruments and their configurations; (2) data-collection and display software; and (3) standard products depicting crop-biophysical characteristics derived using a suite of models to transform the spectral data into accurate, reliable biophysical characteristics of crops, such as fraction of green vegetation, absorbed photosynthetically active radiation, leaf area index, biomass, chlorophyll content and gross primary production. This system streamlines systematic data acquisition, facilitates research, and provides useful products for agriculture

Monitoring Maize (\u3ci\u3eZea mays\u3c/i\u3e L.) Phenology with Remote Sensing

Monitoring crop phenology is required for understanding intra- and interannual variations of agroecosystems, as well as for improving yield prediction models. The objective of this paper is to remotely evaluate the phenological development of maize (Zea mays L.) in terms of both biomass accumulation and reproductive organ appearance. Maize phenology was monitored by means of the recently developed visible atmospherically resistant indices, derived from spectral reflectance data. Visible atmospherically resistant indices provided significant information for crop phenology monitoring as they allowed us to detect: (i) changes due to biomass accumulation, (ii) changes induced by the appearance and development of reproductive organs, and (iii) the onset of senescence, earlier than widely used vegetation indices. Visible atmospherically resistant indices allowed the identification of the timing of phenological transitions that are related to the maize physiological development. They also allowed identification of the onset of the grain-fill period, which is important since maximum yield potential of maize plants depends on optimal environmental conditions during this period