Results 131 to 140 of about 1,769 (150)

Remote Sensing-Derived Environmental Variables to Estimate Transmission Risk and Predict Malaria Cases in Argentina: A Pre-Certification Study (1986-2005). [PDF]

open access: yesPathogens
Cuéllar AC   +7 more
europepmc   +1 more source
Some of the next articles are maybe not open access.

Related searches:

Estimating Corn Canopy Water Content From Normalized Difference Water Index (NDWI): An Optimized NDWI-Based Scheme and Its Feasibility for Retrieving Corn VWC

IEEE Transactions on Geoscience and Remote Sensing, 2021
Here, four normalized difference water index (NDWI) variants, i.e., NDWI(860,970), NDWI(860,1240), NDWI(860,1640), and NDWI(1240,1640) are generated from the corn-oriented PROSAIL radiative transfer model. It is found that, instead of the linear relationship derived in previous studies, corn canopy water content (CWC) is best approximated as an ...
Linna Chai, Shiqi Yang, Wade Crow
exaly   +2 more sources

The use of the Normalized Difference Water Index (NDWI) in the delineation of open water features

International Journal of Remote Sensing, 1996
The Normalized Difference Water Index (NDWI) is a new method that has been developed to delineate open water features and enhance their presence in remotely-sensed digital imagery. The NDWI makes use of reflected near-infrared radiation and visible green light to enhance the presence of such features while eliminating the presence of soil and ...
exaly   +2 more sources

NDWI—A normalized difference water index for remote sensing of vegetation liquid water from space

Remote Sensing of Environment, 1996
The normalized difference vegetation index (NDVI) has been widely used for remote sensing of vegetation for many years. This index uses radiances or reflectances from a red channel around 0.66 μm and a near-IR channel around 0.86 μm. The red channel is located in the strong chlorophyll absorption region, while the near-IR channel is located in the high
exaly   +2 more sources

Sub‐pixel reflectance unmixing in estimating vegetation water content and dry biomass of corn and soybeans cropland using normalized difference water index (NDWI) from satellites

International Journal of Remote Sensing, 2009
Estimating vegetation cover, water content, and dry biomass from space plays a significant role in a variety of scientific fields including drought monitoring, climate modelling, and agricultural prediction. However, getting accurate and consistent measurements of vegetation is complicated very often by the contamination of the remote sensing signal by
Jingfeng Huang, Michael Cosh
exaly   +2 more sources

Estimation of vegetation water content for corn and soybeans with a normalized difference water index (NDWI) using Landsat Thematic Mapper data

IGARSS 2003. 2003 IEEE International Geoscience and Remote Sensing Symposium. Proceedings (IEEE Cat. No.03CH37477), 2004
A mid-infrared Normalized Difference Water Index (NDWI) is proposed for the estimation of vegetation water content (VWC). As part of a large-scale hydrology experiment (SMEX02) an extensive VWC data set was collected for corn and soybeans over a portion of the growth cycle.
D. Chen   +5 more
openaire   +1 more source

Analysis of Normalized Different Wetness Index (NDWI) Using Landsat Imagery for Confluent Area of Multi Water Resources in Western Jinan

2024 6th International Conference on Communications, Information System and Computer Engineering (CISCE)
Yuyu Liu
exaly   +2 more sources

Karakteristik Indeks Air Menggunakan Normalized Difference Water Index (NDWI) Pada DAS Negeri Rutong Kota Ambon

Jurnal Geografi, Lingkungan dan Kesehatan
The objectives of this study are analyzing the transformation of the water content of the NDWI index and the spatial pattern of its mapping in the Rutong State Watershed of Ambon City and analyzing the influence of environmental attributes on spatial patterns of NDWI index water content. The results of the transformation of the NDWI index water content
Bokiraiya Latuamury   +2 more
openaire   +1 more source

Home - About - Disclaimer - Privacy