M.S. Moran

Estimating crop water deficit using the relation between surface-air temperature and spectral vegetation index

The crop water stress index (CWSI), developed at the USDA-ARS U.S. Water Conservation Laboratory, Phoenix, Arizona, is a commonly used index for detection of plant stress based on the difference between foliage and air temperature. Application of CWSI at local and regional scales has been hampered by the difficulty of measuring foliage temperature of partially vegetated fields. Most hand-held, airborne, and satellite-based infrared sensors measure a composite of both the soil and plant temperatures. The concept proposed here, termed the vegetation index/temperature (VIT) trapezoid, is an attempt to combine spectral vegetation indices with composite surface temperature measurements to allow application of the CWSI theory to partially-vegetated fields without knowledge of foliage temperature. Based on this approach, a new index [water deficit index (WDI)] was introduced for evaluating evapotranspiration rates of both full-cover and partially vegetated sites. By definition, WDI is related to the ratio of actual and potential evapotranspiration; in practice, WDI can be computed using remotely sensed measurements of surface temperature and reflectance (red and near-infrared spectrum) with limited on-site meteorological data (net radiation, vapor pressure deficit, wind speed, and air temperature). Both the VIT trapezoid and WDI concepts were evaluated using 1) a simulation of a two-component (soil and vegetation) energy balance model and 2) existing data from an experiment in an alfalfa field in Phoenix, Arizona. Results from both studies showed that the WDI provided accurate estimates of field evapotranspiration rates and relative field water deficit for both full-cover and partially vegetated sites.

Opportunities and limitations for image-based remote sensing in precision crop management

Abstract This review addresses the potential of image-based remote sensing to provide spatially and temporally distributed information for precision crop management (PCM). PCM is an agricultural management system designed to target crop and soil inputs according to within, field requirements to optimize profitability and protect the environment. Progress in. PCM has been hampered by a lack of timely, distributed information on crop and soil conditions. Based on a review of the information requirements of PCM, eight areas were identified in which image-based remote sensing technology could provide information that is currently lacking or inadequate. Recommendations were made for applications with potential for near-term implementation with available remote sensing technology and instrumentation. We found that both aircraft- and satellite-based re-trote sensing could provide valuable information for PCM applications. Images from aircraft-based sensors have a unique role for monitoring seasonally variable crop/soil conditions and for time specific and time-critical crop management; current satellitebased sensors have limited, but important, applications; and upcoming commercial Earth observation satellites may provide the resolution, timeliness, and high quality required for many PCM operations. The current limitations for image-based remote sensing applications are mainly due to sensor attributes, such as restricted spectral range, coarse spatial resolution, slow turnaround time, and inadequate repeat coverage. According to experts in PCM, the potential market for remote sensing products in PCM is good. Future work should be focused on assimilating remotely sensed infonna- tion into existing decision support systems (DSS), and conducting economic and technical analysis of remote sensing applications with season-long pilot projects.

Validation of Soil Moisture and Ocean Salinity (SMOS) Soil Moisture Over Watershed Networks in the U.S.

Estimation of soil moisture at large scale has been performed using several satellite-based passive microwave sensors and a variety of retrieval methods over the past two decades. The most recent source of soil moisture is the European Space Agency Soil Moisture and Ocean Salinity (SMOS) mission. A thorough validation must be conducted to insure product quality that will, in turn, support the widespread utilization of the data. This is especially important since SMOS utilizes a new sensor technology and is the first passive L-band system in routine operation. In this paper, we contribute to the validation of SMOS using a set of four in situ soil moisture networks located in the U.S. These ground-based observations are combined with retrievals based on another satellite sensor, the Advanced Microwave Scanning Radiometer (AMSR-E). The watershed sites are highly reliable and address scaling with replicate sampling. Results of the validation analysis indicate that the SMOS soil moisture estimates are approaching the level of performance anticipated, based on comparisons with the in situ data and AMSR-E retrievals. The overall root-mean-square error of the SMOS soil moisture estimates is 0.043 m3/m3 for the watershed networks (ascending). There are bias issues at some sites that need to be addressed, as well as some outlier responses. Additional statistical metrics were also considered. Analyses indicated that active or recent rainfall can contribute to interpretation problems when assessing algorithm performance, which is related to the contributing depth of the satellite sensor. Using a precipitation flag can improve the performance. An investigation of the vegetation optical depth (tau) retrievals provided by the SMOS algorithm indicated that, for the watershed sites, these are not a reliable source of information about the vegetation canopy. The SMOS algorithms will continue to be refined as feedback from validation is evaluated, and it is expected that the SMOS estimates will improve.

Determination of sensible heat flux over sparse canopy using thermal infrared data

Surface temperatures, Ts, were estimated for a natural vegetative surface in Owens Valley, California, with infrared thermometric observations collected from an aircraft. The region is quite arid and is composed primarily of bushes (∼30%) and bare soil (∼70%). Application of the bulk transfer equation for the estimation of sensible heat, H, gave unsatisfactory values when compared to Bowen ratio and eddy correlation methods over a particular site. This was attributed to the inability with existing data to properly evaluate the resistance to heat transfer, rah. To obtain appropriate rah-values the added resistance to heat transfer, kB−1, was allowed to vary although there is both theoretical and experimental evidence that kB−1 for vegetative surfaces can be treated as constant. The present data indicate that for partial canopy cover under arid conditions kB−1 may be a function of Ts measured radiometrically. The equation determining kB−1 was simplified and tested over another arid site with good results; however, this had a limited data set (i.e., 6 data points). The dimensionless kB−1 equation is simplified for use over full canopy cover and is shown to give satisfactory estimates of H over a fully-grown wheat crop.

Combining the Penman-Monteith equation with measurements of surface temperature and reflectance to estimate evaporation rates of semiarid grassland

The Penman-Monteith equation is useful for computing evaporation rates of uniform surfaces, such as dense vegetation or bare soil. This equation becomes less useful for evaluation of evaporation rates at the regional scale, where surfaces are generally characterized by a patchy combination of vegetation and soil. This is particularly true in the arid and semi-arid regions of the world. The approach proposed here is an attempt to use remotely-sensed measurements of surface reflectance and temperature to allow application of the Penman-Monteith theory to partiallyvegetated fields without a-priori knowledge of the percent vegetation cover and canopy resistance. Basically, the Penman-Monteith equation was combined with the energy balance equation to estimate the surface temperature CT,) associated with four states: surfaces characterized by full-cover vegetation and bare soil with evaporation rates at potential and zero. Then, linear interpolations between T, values computed for full-cover and bare soil conditions were used to provide information at intermediate states based on measurements of actual surface reflectance and temperature. The approach was first tested using ground-based measurements of surface reflectance and temperature at a rangeland site; the results compared well with on-site measure

A refined empirical line approach for reflectance factor retrieval from Landsat-5 TM and Landsat-7 ETM+

The recent launch of Landsat-7 ETM+ extends the uninterrupted stream of TM and ETM+ images to a potential span of 32 years. This exceptional image set will allow long-term studies of natural resources, but will require an operational method for converting image digital number (dn) to the temporally comparable surface reflectance factor (rsl). A refinement to the empirical line (EL) approach for reflectance factor retrieval (RFR) from the Landsat-5 and -7 TM and ETM+ has been proposed. The refined empirical line (REL) approach requires only one within-scene calibration target, minimal field measurements of that target, and a reasonable estimate of dn for rsl=0 using a radiative transfer model or values provided by this analysis. This study showed that the REL approach worked well for a 10-year Landsat5 TM and Landsat-7 ETM+ image set in Arizona and rsl was retrieved with an estimated accuracy of 0.01. A quantitative approach was proposed to determine the suitability of a within-scene target for the REL approach, and based on historical measurements, a variety of targets met the size and brightness requirements for the REL approach. This operational approach for RFR should encourage long-term investigations of natural resources to answer critical questions regarding resource management and effects of climate changes. D Published by Elsevier Science Inc.

Leaf area index estimates using remotely sensed data and BRDF models in a semiarid region

The amount and spatial and temporal dynamics of vegetation are important information in environmental studies and agricultural practices. There has been a great deal of interest in estimating vegetation parameters and their spatial and temporal extent using remotely sensed imagery. There are primarily two approaches to estimating vegetation parameters such as leaf area index (LAI). The first one is associated with computation of spectral vegetation indices (SVI) from radiometric measurements. This approach uses an empirical or modeled LAI-SVI relation between remotely sensed variables such as SVI and biophysical variables such as LAI. The major limitation of this empirical approach is that there is no single LAI-SVI equation (with a set of coefficients) that can be applied to remote-sensing images of different surface types. The second approach involves using bidirectional reflectance distribution function (BRDF) models. It inverts a BRDF model with radiometric measurements to estimate LAI using an optimization procedure. Although this approach has a theoretical basis and is potentially applicable to varying surface types, its primary limitation is the lengthy computation time and difficulty of obtaining the required input parameters by the model. In this study, we present a strategy that combines BRDF models and conventional LAI-SVI approaches to circumvent these limitations. The proposed strategy was implemented in three sequential steps. In the first step, a BRDF model was inverted with a limited number of data points or pixels to produce a training data set consisting of leaf area index and associated pixel values. In the second step, the training data set passed through a quality control procedure to remove outliers from the inversion procedure. In the final step, the training data set was used either to fit an LAI-SVI equation or to train a neural fuzzy system. The best fit equation or the trained fuzzy system was then applied to large-scale remote-sensing imagery to map spatial LAI distribution. This approach was applied to Landsat TM imagery acquired in the semiarid southeast Arizona and AVHRR imagery over the Hapex-Sahel experimental sites near Niamy, Niger. The results were compared with limited ground-based LAI measurements and suggested that the proposed approach produced reasonable estimates of leaf area index over large areas in semiarid regions. This study was not intended to show accuracy improvement of LAI estimation from remotely sensed data. Rather, it provides an alternative that is simple and requires little knowledge of study target and few ground measurements.

Using Satellite Remote Sensing to Extrapolate Evapotranspiration Estimates in Time and Space over a Semiarid Rangeland Basin

Abstract Remote sensing data from the NOAA-11 AVHRR satellite were collected over the USDA-Agricultural Research Service Walnut Gulch Experimental Watershed in southeastern Arizona during the MONSOON 90 field campaigns. An energy balance model which relies primarily on remotely sensed inputs was used to extrapolate evapotranspiration (ET) estimates from one location containing near-surface meteorological data to other areas in the basin. Satisfactory results were obtained under a wide range of environmental conditions. However, the ET values are essentially instantaneous and therefore do not necessarily provide reliable estimates of daytime or daily ET fluxes required for many hydrological and resource management applications. An operational technique was developed to extrapolate one time of day ET estimates to daytime averages using the evaporative fraction concept and empirical methods for converting midday available energy to daytime average values. Model derived daytime average ET fluxes were in reasonable agreement with local ground-based measurements. The technique also was used to estimate daily ET at the basin scale.

Bidirectional measurements of surface reflectance for view angle corrections of oblique imagery

Abstract An apparatus for acquiring bidirectional reflectance-factor data was constructed and used over four surface types. Data sets were obtained over a headed wheat canopy, bare soil having several different roughness conditions, playa (dry lake bed), and gypsum sand. Results are presented in terms of relative bidirectional reflectance factors (BRFs) as a function of view angle at a number of solar zenith angles, nadir BRFs as a function of solar zenith angles, and, for wheat, vegetation indices as related to view and solar zenith angles. The wheat canopy exhibited the largest BRF changes with view angle. BRFs for the red and the near-infrared (NIR) bands measured over wheat did not have the same relationship with view angle. NIR/Red ratios calculated from nadir BRFs changed by nearly a factor of 2 when the solar zenith angle changed from 20° to 50°. BRF versus view angle relationships were similar for soils having smooth and intermediate rough surfaces but were considerably different for the roughest surface. Nadir BRF versus solar-zenith angle relationships were distinctly different for the three soil roughness levels. Of the various surfaces, BRFs for gypsum sand changed the least with view angle (10% at 30°).

An interdisciplinary field study of the energy and water fluxes in the atmosphere−biosphere system over semiarid rangelands : description and some preliminary results

Abstract Arid and semiarid rangelands comprise a significant portion of the earths land surface. Yet little is known about the effects of temporal and spatial changes in surface soil moisture on the hydrologic cycle, energy balance, and the feedbacks to the atmosphere via thermal forcing over such environments. Understanding this interrelationship is crucial for evaluating the role of the hydrologic cycle in surface-atmosphere interactions. This study focuses on the utility of remote sensing to provide measurements of surface soil moisture, surface albedo, vegetation biomass, and temperature at different spatial and temporal scales. Remote-sensing measurements may provide the only practical means of estimating some of the more important factors controlling land surface processes over large areas. Consequently, the use of remotely sensed information in biophysical and geophysical models greatly enhances their ability to compute fluxes at catchment and regional scales on a routine basis. However, model cal...