Compositing the Minimum NDVI for Daily Water Surface Mapping

Abstract

Capturing high frequency water surface dynamics via optical remote sensing is important for understanding hydro-ecological processes over seasonally flooded wetlands. However, it is a difficult task due to the presence of clouds on satellite images. This study proposed the MODerate-resolution Imaging Spectroradiometer (MODIS) Normalized Difference Vegetation Index (NDVI) Minimum Value Composite (MinVC) algorithm to generate daily water surface data at a 250-m resolution. The algorithm selected pixelwise minimum values from the combined daily Terra and Aqua MODIS NDVI data within a 15-day moving window. Consisting mainly of cloud and water surface information, the MinVC NDVI data were segmented for water surfaces over the Poyang Lake, China (2000–2017) by using an edge detection model. The water surface mapping result was strongly correlated with the Landsat based result (R2 = 0.914, root mean square error, RMSE = 223.7 km2), the cloud free MODIS image based result (R2 = 0.824, RMSE = 356.7 km2), the recent Landsat-MODIS image fusion based result (R2 = 0.765, RMSE = 403 km2), and the hydrodynamic modeling result (R2 = 0.799). Compared to the equivalent eight-day MOD13 NDVI based on the Constraint View-Angle Maximum Value Composite (CV-MVC) algorithm, the daily MinVC NDVI highlighted water bodies by generating spatially homogenous water surface information. Consequently, the algorithm provided spatially and temporally continuous data for calculating water submersion times and trends in water surface area, which contribute to a better understanding of hydro-ecological processes over seasonally flooded wetlands. Within the framework of sensor intercalibration, the algorithm can be extended to incorporate multiple sensor data for improved water surface mapping.

Remote Soil Moisture Measurement from Drone-Borne Reflectance Spectroscopy: Applications to Hydroperiod Measurement in Desert Playas

“…Orthomosaics were generated with <1 cm spatial resolution and were orthorectified using the concurrently produced digital elevation model. Two multispectral soil moisture parameters were also examined at the site: normalized difference vegetation index: NDVI, which has been shown to inversely correlate with bare land soil moisture by [14], and normalized difference water index (NDWI-here, the open water index described by [34] is used to target standing water in the AHS plume-not the NDWI plant moisture index of [35]. NDWI in the [34] formulation is the ratio between green and SWIR channels: NDWI = ρ green /ρ SWIR (6) where ρ green and ρ SWIR are reflectance in Landsat 8 bands 3 and 6, as processed and delivered as surface reflectance by [36] via USGS Earth Explorer.…”

“…Remote sensing of surface soil moisture typically uses microwave, radar [7], or thermal measurements to infer the water content of the soil, for example, using the changing heat capacity of wetted ground [8], however, the complex interplay between soil moisture, albedo of barren ground, and changing density, compaction, and heat capacity can reduce correlations between thermal remote sensing measurements and soil water content [9]. Synthetic aperture radar can be used to determine soil moisture [10], as can reflectance imaging in the shortwave near-infrared (SWIR) [11][12][13][14], although in some studies, SWIR reflectance indices are found to correlate more closely with matrix-bound-water under tension, rather than with total pore water content [6].…”

“…In barren soils free of vascular vegetation, optical reflectance measurements in the near-infrared can be used to directly observe the 1.4 µm absorption from water that fills surface pore space [13,15], or broadband multispectral indices can be used to infer soil moisture content, for example, through detections of minimum NDVI [14]. Others have combined plant stress measurements (NDVI) with soil surface temperature measurements (a function of soil heat capacity), to derive soil moisture in agricultural, grassland, and forested settings [16][17][18].…”

Regionalized Linear Models for River Depth Retrieval Using 3-Band Multispectral Imagery and Green LIDAR Data

“…To establish the georeferenced water surface elevation along the river sections, we used local satellite imagery in combination with the red LIDAR raster to set up polygons with the same water surface elevation. To obtain water surface maps, we used the Normalized Difference Vegetation Index (NDVI) method on the satellite imagery, using a combination of the red and near-infrared (NIR) bands [37]. In the resulting raster, negative values indicated water surface, and positive values indicated dry land.…”

An Optical and SAR Based Fusion Approach for Mapping Surface Water Dynamics over Mainland China

“…Surface water mapping is an established application in remote sensing, yet by reviewing the extensive literature on the subject, the approaches vary according to the objectives and scale of the study, sensor used, and environmental settings. In the optical domain, a common approach for mapping surface water is done using image thresholding on spectral bands or water indices such as the Normalized Difference Water Index (NDWI), Normalized Difference Vegetation Index (NDVI), Modified Normalized Difference Water Index (MNDWI) or Automated Water Extraction Index (AWEI) [27][28][29][30][31][32][33][34]. Theoretically, the thresholds for separating water from non-water are universal, but praxis thresholds tend to vary with the regional settings and atmospheric conditions.…”