Remote Sensing-based Model Research Articles

Modeling Gross Primary Production by Integrating Satellite Data and Coordinated Flux Measurements in Arid and Semi-Arid China

Assessing large-scale patterns of gross pri- mary production (GPP) in arid and semi-arid (ASA) areas is important for both scientific and practical purposes. Remote sensing-based models, which integrate satellite data with input from ground-based meteorological meas- urements and vegetation characteristics, improve spatially extended estimates of vegetation productivity with high accuracy. In this study, the authors simulated GPP in ASA areas by integrating moderate resolution imaging spectral radiometer (MODIS) data with eddy covariance and me- teorological measurements at the flux tower sites using the Vegetation Photosynthesis Model (VPM), which is a re- mote sensing-based model for analyzing the spatial pat- tern of GPP in different land cover types. The field data were collected by coordinating observations at nine sta- tions in 2008. The results indicate that in the region dur- ing the growing season GPP was highest in cropland sites, second highest in woodland sites, and lowest in grassland sites. VPM captured the temporal and spatial characteris- tics of GPP for different land covers in ASA areas. Further, Enhanced Vegetation Index (EVI) had a strong liner rela- tionship with GPP in densely vegetated areas, while the Normalized Difference Vegetation Index (NDVI) had a strong liner relationship with GPP over less dense vegeta- tion. This study demonstrates the potential of satel- lite-driven models for scaling-up GPP, which is a key component for studying the carbon cycle at regional and

Surface energy balance and 24-h evapotranspiration on an agricultural landscape with SRF willow in central New York

Surface energy balance and 24-h evapotranspiration were mapped on an agricultural landscape with SRF willows using a remote sensing-based model to provide information on the water use by different land cover types. The results demonstrate the influence of land cover and vegetation type on surface energy balance and water losses. The evapotranspiration from forests was much higher than for other land cover types, corresponding to the significant cooling of the land surface. The SRF willow plantings were on average 3.6 °C cooler than the surrounding grassy areas; the 24-hour evapotranspiration from willows was 1.6 times higher than the evapotranspiration from surrounding grass, but lower than evapotranspiration from the forests. The possibility of changes in evapotranspiration patterns, local climate, and regional water balance with increasing acreage in SRF crop are discussed.

A comparison of operational remote sensing-based models for estimating crop evapotranspiration

The integration of remotely sensed data into models of evapotranspiration (ET) facilitates the estimation of water consumption across agricultural regions. To estimate regional ET, two basic types of remote sensing approaches have been successfully applied. The first approach computes a surface energy balance using the radiometric surface temperature for estimating the sensible heat flux ( H), and obtaining ET as a residual of the energy balance. This paper compares the performance of three different surface energy balance algorithms: an empirical one-source energy balance model; a one-source model calibrated using inverse modeling of ET extremes (namely ET = 0 and ET at potential) which are assumed to exist within the satellite scene; and a two-source (soil + vegetation) energy balance model. The second approach uses vegetation indices derived from canopy reflectance data to estimate basal crop coefficients that can be used to convert reference ET to actual crop ET. This approach requires local meteorological and soil data to maintain a water balance in the root zone of the crop. Output from these models was compared to sensible and latent heat fluxes measured during the soil moisture–atmosphere coupling experiment (SMACEX) conducted over rain-fed corn and soybean crops in central Iowa. The root mean square differences (RMSD) of the estimation of instantaneous latent and heat fluxes were less than 50 W m −2 for the three energy balance models. The two-source energy balance model gave the lowest RMSD (30 W m −2) and highest r 2 values in comparison with measured fluxes. In addition, three schemes were applied for upscaling instantaneous flux estimates from the energy balance models (at the time of satellite overpass) to daily integrated ET, including conservation of evaporative fraction and fraction of reference ET. For all energy balance models, an adjusted evaporative fraction approach produced the lowest RMSDs in daily ET of 0.4–0.6 mm d −1. The reflectance-based crop coefficient model yielded RMSD values of 0.4 mm d −1, but tended to significantly overestimate ET from corn during a prolonged drydown period. Crop stress can be directly detected using radiometric surface temperature, but ET modeling approaches-based solely on vegetation indices will not be sensitive to stress until there is actual reduction in biomass or changes in canopy geometry.

Spatial Assessment of Water Use in an Environmentally Sensitive Wetland

Wetlands are among the most valuable ecosystems in the world and are crucial in supporting biodiversity. They also provide space for storing surface waters, where intense biological processing occurs that helps improve water quality. Human activities, particularly irrigated agriculture and urban developments instigating water diversions from rivers, have altered the hydrology of most wetlands. The Lower Murrumbidgee wetland, located in the Murrumbidgee River Catchment, is an example and is one of the significant wetlands across the Murray Darling Basin of Australia. Historic estimates show the volumes of water ranged from none in dry years to about 300 to 400 GL (1 GL = 10(9) L) in an average and wet year, respectively. The flows reaching the Lower Murrumbidgee wetland have been drastically reduced by at least 60% because of the upstream diversions introduced during the last century. These reductions have adversely affected the health of natural vegetation and agricultural crops in the Lower Murrumbidgee floodplain. This article presents the results of the quantification of total water consumption of various land uses in the Lower Murrumbidgee floodplain using the remote sensing-based Surface Energy Balance Algorithm for Land (SEBAL) modeling approach. The spatial analysis of actual evapotranspiration (ETa) shows that ETa rates are the highest (13-26%) for the red gum (Eucalyptus camaldulensis) forests both in summer and winter days. However, in terms of total ETa volume, lignum (Muehlenbeckia florulenta) constitutes the most significant part, which is around 14-30% of the total ETa volume for the area. Actual evapotranspiration from winter cereal cropping areas following the summer ponding is the third highest consumer of water after the river red gum and lignum. Actual evapotranspiration from the fallow land is also significant, representing 5-28% of total ETa from the region. In view of the extent of the unaccounted flows in the overall water balance of the system, there is a need to upgrade measuring and reporting infrastructure by strengthening the institutional and management arrangements to better gauge the efficiency of environmental and consumptive water use. The state-of-the-art technology of remote sensing-based SEBAL modeling proved to have potential for measuring actual water use with reliable accuracy and can be used for assessing the environmental and productive use of water from wetlands in other regions of Australia.

Mapping forest alliances and associations using fuzzy systems and nearest neighbor classifiers

The study and management of biological communities depends on systems of classification and mapping for the organization and communication of resource information. Recent advances in remote sensing technology may enable the mapping of forest plant associations using image classification techniques. But few areas outside Europe have alliances and associations described in detail sufficient to support remote sensing-based modeling. Northwestern Montana has one of the few completed plant association classifications in the United States compliant with the recently established National Vegetation Classification system. This project examined the feasibility of mapping forest plant associations using Landsat Enhanced Thematic Mapper Plus data and advanced remote sensing technology and image classification techniques. Suitable reference data were selected from an extensive regional database of plot records. Fifteen percent of the plot samples were reserved for validation of map products, the remainder of plots designated as training data for map modeling. Key differentiae for image classification were identified from a suite of spectral and biophysical variables. Fuzzy rules were formulated for partitioning physiognomic classes in the upper levels of our image classification hierarchy. Nearest neighbor classifiers were developed for classification of lower levels (alliances and associations), where spectral and biophysical contrasts are less distinct. Maps were produced to reflect nine forest alliances and 24 associations across the study area. Error matrices were constructed for each map based on stratified random selections of map validation samples. Accuracy for the alliance map was estimated at 60%. Association classifiers provide between 54 and 86% accuracy within their respective alliances. Alternative techniques are proposed for aggregating classes and enhancing decision tree classifiers to model alliances and associations for interior forest types.

Using a remote sensing field experiment to investigate flux-footprint relations and flux sampling distributions for tower and aircraft-based observations

During a remote sensing field experiment conducted in the Southern Great Plains in 1997 (SGP97), tower and aircraft-based flux observations were collected over one of the main study sites in central Oklahoma. This is an agricultural region and contains primarily grassland/pasture and winter wheat, which was recently harvested leaving a significant number of fields either as wheat stubble or plowed bare soil. Multi-spectral data obtained by aircraft provided high-resolution (30 m) spatially-distributed vegetation cover and surface temperature information over the study area. The spatial variations in these surface states strongly affect the partitioning of surface fluxes between sensible and latent heat. These data, together with coarser resolution (5 km) satellite data, are used in a remote sensing-based energy balance modeling system that disaggregates flux estimates from 5 km to 30-m resolution. The resulting high-resolution flux maps provide a means for evaluating whether tower and aircraft-based flux measurements sample a full range in flux conditions for this landscape. In addition, this remote sensing-based modeling system can be used to investigate the influence of variability in these key surface states on tower and aircraft measurements through flux-footprint modeling. Under the light wind and unstable conditions that existed during the observations, highest correlation between aircraft and modeled estimated heat and water vapor fluxes were obtained using different flux-footprint estimates. More specifically, the source area for heat was estimated to be much closer to the aircraft flight line than for water vapor.

Assessment of a Remote Sensing-Based Model for Predicting Malaria Transmission Risk in Villages of Chiapas, Mexico

A blind test of two remote sensing-based models for predicting adult populations of Anopheles albimanus in villages, an indicator of malaria transmission risk, was conducted in southern Chiapas, Mexico. One model was developed using a discriminant analysis approach, while the other was based on regression analysis. The models were developed in 1992 for an area around Tapachula, Chiapas, using Landsat Thematic Mapper (TM) satellite data and geographic information system functions. Using two remotely sensed landscape elements, the discriminant model was able to successfully distinguish between villages with high and low An. albimanus abundance with an overall accuracy of 90%. To test the predictive capability of the models, multitemporal TM data were used to generate a landscape map of the Huixtla area, northwest of Tapachula, where the models were used to predict risk for 40 villages. The resulting predictions were not disclosed until the end of the test. Independently, An. albimanus abundance data were collected in the 40 randomly selected villages for which the predictions had been made. These data were subsequently used to assess the models' accuracies. The discriminant model accurately predicted 79% of the high-abundance villages and 50% of the low-abundance villages, for an overall accuracy of 70%. The regression model correctly identified seven of the 10 villages with the highest mosquito abundance. This test demonstrated that remote sensing-based models generated for one area can be used successfully in another, comparable area.

Assessment of regional soil moisture conditions by coupling satellite sensor data with a soil-plant system heat and moisture balance model

Abstract Assessment of regional soil moisture conditions is important for agricultural water management, crop yield modelling and meteorological and climatic studies. This paper presents the results of an integrated remote sensing-based modelling study to estimate and map root-zone soil moisture potential, using a soil-plant system-heat and moisture balance model. The model utilizes satellite derived surface temperature, albedo, land cover and soil textural data bases and ground collected agro-meteorological data as inputs.