An NDVI-Based Statistical ET Downscaling Method

Abstract

This study proposes a new method for downscaling ETWatch 1-km actual evapotranspiration (ET) products to a spatial resolution of 30 m using Landsat8 normalized difference vegetation index (NDVI) data. The NDVI is employed as an indicator of land-surface vegetation, which displays periodic spatial patterns on the land surface. A 30-m-resolution ten-day ET dataset is then calculated primarily using the NDVI and the historical ratio of coarse NDVI and ET that considers different land cover types. Good agreement and correlations were obtained between the downscaled data and observations from three flux sites in two study areas. The mean bias (MB) per ten-day period ranges from 4.21 mm in Guantao to 1.55 mm in Huazhaizi, and the coefficient of determination (R 2 ) varies from 0.87 to 0.95. The downscaling results show good consistency with the original ETWatch 1-km data over both temporal and spatial scales for different land cover types, with R 2 values ranging from 0.82 to 0.98. In addition, the downscaled results capture the progression of vegetation growth well. This study demonstrates the applicability of the new "de-pixelation" downscaling method in the management of water resources.

Satellite-Based Water Consumption Dynamics Monitoring in an Extremely Arid Area

“…After this rectification process, we achieved less than one pixel of root mean square error (RMSE). Similar performances after downscaling have been observed from these two data sources based on Tan's research [35].…”

“…where λE (W/m 2 ) is the latent heat flux, s is the slope of the curve relating saturated water vapour pressure to temperature, A (W/m 2 ) is the available energy calculated using net radiation and soil heat flux, Rn is the net radiation flux, ρ (kg•m −3 ) is the air density, C p (J•kg −1 K −1 ) is the specific heat of air, (e sat − e a ) is the vapour pressure deficit (VPD), γ (Pa•K −1 ) is the psychrometric constant, R a is the aerodynamic resistance, and R sur is the surface resistance. The resulting coarse ET map was then disaggregated to a 30-m scale using the empirical downscaling method recommended by Tan [35]. The downscaling step was conducted under the assumption that the 250-m pixels accurately represent the water consumption from the corresponding land surface.…”

“…Downscaling factor p consists of the NDVI because water consumption in arid areas is dominated by vegetation. The historical ratio of NDVI/ETa was used to decrease the potential deviation because of heterogeneity of the land surface [35].…”

Seasonal Crop Water Balance Using Harmonized Landsat-8 and Sentinel-2 Time Series Data

“…One of the most widely used methods to determine water demand is the estimation of evapotranspiration (ET). Several studies have been done to evaluate the effect of water applied in crop yield [12][13][14][15][16] to optimize the water management in agriculture. Nonetheless, this process is difficult to correctly quantify when dealing with large areas, as there is great spatiotemporal variability due to the complex interactions between the soil, vegetation, and climate [17].…”

“…Only remote sensing imagery that provides spectral information in the thermal band may be used as input for these models. Unfortunately, most current satellite sensors do not provide this information, but they do include a set of spectral bands that allows the radiometric behavior of vegetation to be determined by focusing on the spectral contrast presented by plant cover in the red and near infrared bands [12]. Most V I are based on this principle; one which allows multitemporal data series to be constructed, thus providing essential information on water consumption patterns in various crop types and helping keep information on different agricultural covers up to date, as well as for monitoring of the biophysical properties of plants, such as plant cover, vigor, and growth dynamics [35].…”