Nishan Bhattarai

A simple Landsat–MODIS fusion approach for monitoring seasonal evapotranspiration at 30 m spatial resolution

Persistent cloud-cover in the humid southeastern USA and the low temporal resolution of Landsat sensors limit the derivation of seasonal evapotranspiration (ET) maps at moderate spatial resolution. This article introduces a Landsat Moderate Resolution Imaging Spectroradiometer (Landsat–MODIS) ET fusion model that uses simple linear regression to integrate Landsat-derived reference ET fraction (ETrF) from mapping ET at high resolution with internalized calibration (METRIC) model and the vegetation temperature condition index (VTCI) derived from MODIS images. For a study site in Florida, model-estimated ET and ET estimated using energy budget eddy covariance at a US Geological Survey (USGS) station in Ferris Farm, Florida, were found to be in a good agreement with a root mean squared error of 0.44 mm day–1, coefficient of determination (R2) of 0.80, Nash–Sutcliffe efficiency of 0.79 for daily ET (ETd), and 2% relative error for cumulative seasonal ET during the growing season of 2001. At another study site in Alabama, the model underestimated 2008 annual water balance ET for the Fish River Watershed by 39 mm or 4%. Comparisons of model-estimated ET with that obtained using a non-fusion Landsat-only approach at both sites indicated that the fusion of Landsat and MODIS ET values reduces potential errors in ET estimation that would otherwise arise due to insufficient availability of cloud-free Landsat images for METRIC processing. Validation results and application of the model in deriving seasonal/annual ET for different land-cover classes in the Fish River Watershed suggested that the fusion model has the potential to be used in continuously monitoring ET for field- to watershed-level agricultural and hydrological applications in the southeastern USA.

“Are Brazil's Deforesters Avoiding Detection?”

Rates of deforestation reported by Brazil’s official deforestation monitoring system have declined dramatically in the Brazilian Amazon. Much of Brazil’s success in its fight against deforestation has been credited to a series of policy changes put into place between 2004 and 2008. In this research, we posit that one of these policies, the decision to use the country’s official system for monitoring forest loss in the Amazon as a policing tool, has incentivized landowners to deforest in ways and places that evade Brazil’s official monitoring and enforcement system. As a consequence, we a) show or b) provide several pieces of suggestive evidence that recent successes in protecting monitored forests in the Brazilian Amazon may be doing less to protect the region’s forests than previously assumed.

Validation of evaporation estimates from a modified surface energy balance algorithm for land (SEBAL) model in the south-eastern United States

A modified surface energy balance algorithm for land (SEBAL) model, which has been widely used in the western United States since its development in 1998, was validated in the humid south-eastern United States using daily and monthly evapotranspiration (ET) estimates. Sixteen Landsat 5 Thematic Mapper (TM) images from April 2000 to September 2006 were processed, and the results were compared with energy-budget eddy covariance (EBEC) ET estimates from four US Geological Survey (USGS) stations. The model performed well in terms of Nash–Sutcliffe efficiency (NSE) coefficients (daily = 0.82, monthly = 0.77) and coefficients of determination (R 2, daily = 0.83, monthly = 0.77). Root mean square errors (RMSEs, daily = 0.48 mm/day, monthly = 16 mm/month), mean absolute errors (MAEs, daily = 0.32 ± 0.36 mm/day, monthly = 12 ± 10 mm/month), mean relative errors (MREs, daily = 7 ± 8%, monthly = 11 ± 12%) and mean bias errors (MBEs, daily = 0.05 mm/day, monthly = −2 mm/month) were comparable to the results from similar studies in the western United States. Results from the study support the applicability of the modified SEBAL model in the rapidly growing south-eastern United States as a tool for estimating consumptive water use via remotely sensed methods.