Evaluating the sensitivity of vegetation and water indices to monitor drought For Three Mediterranean Crops

2020 ◽  
Author(s):  
Tugba Yildirim ◽  
Yuting Zhou ◽  
K. Colton Flynn ◽  
Prasanna H. Gowda ◽  
Shengfang Ma ◽  
...  
Keyword(s):  
Water Indices

scholarly journals Evaluation of Water Indices for Surface Water Extraction in a Landsat 8 Scene of Nepal

Sensors ◽  
2018 ◽  
Vol 18 (8) ◽  
pp. 2580 ◽  
Author(s):  
Tri Acharya ◽  
Anoj Subedi ◽  
Dong Lee
Keyword(s):  
Surface Water ◽  
Vegetation Index ◽  
Normalized Difference Vegetation Index ◽  
Pure Water ◽  
Confusion Matrix ◽  
Threshold Value ◽  
Environmental Noise ◽  
Water Extraction ◽  
Landsat 8 ◽  
Water Indices

Accurate and frequent updates of surface water have been made possible by remote sensing technology. Index methods are mostly used for surface water estimation which separates the water from the background based on a threshold value. Generally, the threshold is a fixed value, but can be challenging in the case of environmental noise, such as shadow, forest, built-up areas, snow, and clouds. One such challenging scene can be found in Nepal where no such evaluation has been done. Taking that in consideration, this study evaluates the performance of the most widely used water indices: Normalized Difference Vegetation Index (NDVI), Normalized Difference Water Index (NDWI), Modified NDWI (MNDWI), and Automated Water Extraction Index (AWEI) in a Landsat 8 scene of Nepal. The scene, ranging from 60 m to 8848 m, contains various types of water bodies found in Nepal with different forms of environmental noise. The evaluation was conducted based on measures from a confusion matrix derived using validation points. Comparing visually and quantitatively, not a single method was able to extract surface water in the entire scene with better accuracy. Upon selecting optimum thresholds, the overall accuracy (OA) and kappa coefficient (kappa) was improved, but not satisfactory. NDVI and NDWI showed better results for only pure water pixels, whereas MNDWI and AWEI were unable to reject snow cover and shadows. Combining NDVI with NDWI and AWEI with shadow improved the accuracy but inherited the NDWI and AWEI characteristics. Segmenting the test scene with elevations above and below 665 m, and using NDVI and NDWI for detecting water, resulted in an OA of 0.9638 and kappa of 0.8979. The accuracy can be further improved with a smaller interval of categorical characteristics in one or multiple scenes.

Use of water indices in the determination of thermokarst lakes according to remote sensing data

2021 ◽  
pp. 67-74
Author(s):  
Artem Pshenichnikov
Keyword(s):  
Visual Analysis ◽  
Remote Sensing Data ◽  
Water Bodies ◽  
Identical Result ◽  
Spectral Indices ◽  
Quadratic Error ◽  
Thermokarst Lakes ◽  
Ndvi Index ◽  
Water Indices

The results of application of six spectral indices (AWEI, MNDWI, NDVI, NDWI, TCW, WRI) for the isolation of thermokarst lakes in tundra landscapes of northern Yakutia are presented. To assess the accuracy of decryption of lakes, an average quadratic error (MSE) was calculated. The minimum MSE value is 0.11 km2 and corresponds to the NDWI index. An almost identical result (0.12 km2) is found in the WRI index, slightly worse (0.15 km2) one — in the NDVI index. An MNDWI index has the highest mean square error (7.02 km2). Visual analysis also showed better decryption of water bodies using the NDWI, WRI and NDVI indices, which allows the use of these indices for automatical isolatation water bodies.

Exploring Sentinel-1 and Sentinel-2 diversity for Flood inundation mapping using deep learning

2021 ◽  
Author(s):  
Goutam Konapala ◽  
Sujay Kumar
Keyword(s):  
Flood Mapping ◽  
Attractive Alternative ◽  
Flood Inundation ◽  
Positive Role ◽  
Inundation Maps ◽  
Flood Inundation Mapping ◽  
Sar Imagery ◽  
Operational Errors ◽  
Water Indices ◽  
Sentinel 2

<p>Identification of flood water extent from satellite images has historically relied on either synthetic aperture radar (SAR) or multi-spectral (MS) imagery. But MS sensors may not penetrate cloud cover, whereas SAR is plagued by operational errors such as noise-like speckle challenging their viability to global flood mapping applications. An attractive alternative is to effectively combine MS data and SAR, i.e., two aspects that can be considered complementary with respect to flood mapping tasks. Therefore, in this study, we explore the diverse bands of Sentinel 2 (S2) derived water indices and Sentinel 1 (S1) derived SAR imagery along with their combinations to access their capability in generating accurate flood inundation maps. For this purpose, a fully connected deep convolutional neural network known as U-Net is applied to combinations of S1 and S2 bands to 446 (training: 313, validating: 44, testing: 89) hand labeled flood inundation extents derived from Sen1Floods11 dataset spanning across 11 flood events. The trained U-net was able to achieve a median F1 score of 0.74 when using DEM and S1 bands as input in comparison to 0.63 when using only S1 bands highlighting the active positive role of DEM in mapping floods. Among the, S2 bands, HSV (Hue, Saturation, Value) transformation of Sentinel 2 data has achieved a median F1 score of 0.94 outperforming the commonly used water spectral indices owing to HSV’s transformation’s superior contrast distinguishing abilities. Also, when combined with Sentinel 1 SAR imagery too, HSV achieves a median F1 score 0.95 outperforming all the well-established water indices in detecting floods in majority of test images.</p>

scholarly journals Climatic water indices for viticulture

2018 ◽  
Vol 53 (6) ◽  
pp. 765-768
Author(s):  
Marco Antônio Fonseca Conceição ◽  
Jorge Tonietto ◽  
Reginaldo Teodoro de Souza
Keyword(s):  
Climate Data ◽  
Water Index ◽  
Historical Series ◽  
Weather Stations ◽  
Water Indices

Abstract: The objective of this work was to evaluate the performance of vineyard water indices in different grape-growing regions. The climate data used come from the historical series of weather stations located in 18 countries. The evaluated indices were the following: dryness, Zuluaga, humidity, aridity, moisture, and the grapevine water index. The grapevine water index and the indices of drought, moisture, and aridity exhibit similar performances, which makes them suitable to be used equivalently in climatological studies of grapevine regions.

scholarly journals Open Surface Water Mapping Algorithms: A Comparison of Water-Related Spectral Indices and Sensors

Water ◽  
2017 ◽  
Vol 9 (4) ◽  
pp. 256 ◽  
Author(s):  
Yan Zhou ◽  
Jinwei Dong ◽  
Xiangming Xiao ◽  
Tong Xiao ◽  
Zhiqi Yang ◽  
...  
Keyword(s):  
Surface Water ◽  
Water Body ◽  
Landsat 8 ◽  
Open Surface ◽  
Body Mapping ◽  
Water Index ◽  
Landsat 7 ◽  
Surface Water Bodies ◽  
Water Indices ◽  
Sentinel 2

Open surface water bodies play an important role in agricultural and industrial production, and are susceptible to climate change and human activities. Remote sensing data has been increasingly used to map open surface water bodies at local, regional, and global scales. In addition to image statistics-based supervised and unsupervised classifiers, spectral index- and threshold-based approaches have also been widely used. Many water indices have been proposed to identify surface water bodies; however, the differences in performances of these water indices as well as different sensors on water body mapping are not well documented. In this study, we reviewed and compared existing open surface water body mapping approaches based on six widely-used water indices, including the tasseled cap wetness index (TCW), normalized difference water index (NDWI), modified normalized difference water index (mNDWI), sum of near infrared and two shortwave infrared bands (Sum457), automated water extraction index (AWEI), land surface water index (LSWI), as well as three medium resolution sensors (Landsat 7 ETM+, Landsat 8 OLI, and Sentinel-2 MSI). A case region in the Poyang Lake Basin, China, was selected to examine the accuracies of the open surface water body maps from the 27 combinations of different algorithms and sensors. The results showed that generally all the algorithms had reasonably high accuracies with Kappa Coefficients ranging from 0.77 to 0.92. The NDWI-based algorithms performed slightly better than the algorithms based on other water indices in the study area, which could be related to the pure water body dominance in the region, while the sensitivities of water indices could differ for various water body conditions. The resultant maps from Landsat 8 and Sentinel-2 data had higher overall accuracies than those from Landsat 7. Specifically, all three sensors had similar producer accuracies while Landsat 7 based results had a lower user accuracy. This study demonstrates the improved performance in Landsat 8 and Sentinel-2 for open surface water body mapping efforts.

scholarly journals A Comparative Study of Water Indices and Image Classification Algorithms for Mapping Inland Surface Water Bodies Using Landsat Imagery

2020 ◽  
Vol 12 (10) ◽  
pp. 1611
Author(s):  
Feifei Pan ◽  
Xiaohuan Xi ◽  
Cheng Wang
Keyword(s):  
Image Classification ◽  
Landsat Imagery ◽  
Google Earth ◽  
Water Bodies ◽  
Classification Algorithms ◽  
Landsat 8 ◽  
Blue Band ◽  
Ground Truth Data ◽  
Water Index ◽  
Water Indices

A comparative study of water indices and image classification algorithms for mapping inland water bodies using Landsat imagery was carried out through obtaining 24 high-resolution (≤5 m) and cloud-free images archived in Google Earth with the same (or ±1 day) acquisition dates as the Landsat-8 OLI images over 24 selected lakes across the globe, and developing a method to generate the alternate ground truth data from the Google Earth images for properly evaluating the Landsat image classification results. In addition to the commonly used green band-based water indices, Landsat-8 OLI’s ultra-blue, blue, and red band-based water indices were also tested in this research. Two unsupervised (the zero-water index threshold H0 method and Otsu’s automatic threshold selection method) and one supervised (the k-nearest neighbor (KNN) method) image classification algorithms were employed for conducting the image classification. Through comparing a total of 2880 Landsat image classification results with the alternate ground truth data, this study showed that (1) it is not necessary to use some supervised image classification methods for extracting water bodies from Landsat imagery given the high computational cost associated with the supervised image classification algorithms; (2) the unsupervised classification algorithms such as the H0 and Otsu methods could achieve comparable accuracy as the KNN method, although the H0 method produced more large error outliers than the Otsu method, thus the Otsu method is better than the H0 method; and (3) the ultra-blue band-based AWEInsuB is the best water index for the H0 method, and the ultra-blue band-based MNDWI2uB is the best water index for both the Otsu and KNN methods.

scholarly journals Testing the Relevance of Daily MODIS Data to Monitor Mediterranean Shrubland Canopy Water Content with Temporal Cross-Correlation Analyses

2013 ◽  
Vol 4 (1) ◽  
pp. 1-19 ◽  
Author(s):  
Carole Delenne ◽  
Jean-Stéphane Bailly ◽  
Michel Deshayes
Keyword(s):  
Water Content ◽  
Forest Fire ◽  
Fire Hazard ◽  
Statistical Tests ◽  
Delay Effect ◽  
Field Surveys ◽  
Modis Data ◽  
Mediterranean Shrubland ◽  
Canopy Water ◽  
Water Indices

Drought alert systems for forest fire prevention often rely on vegetation water content (VWC) monitoring which is a key parameter in forest fire hazard. In southern France, VWC is now monitored through regular field surveys. Thanks to the theoretical sensitivity of shortwave infrared reflectance to VWC, MODIS satellite data are potentially able to monitor VWC depending on plant species VWC magnitude. In this paper, a specific statistical approach based on temporal cross-correlations is developed in order to test the correlation between two MODIS water indices and VWC measurements coming from field surveys. This test assesses the ability of daily MODIS data to monitor Mediterranean shrubland canopy water content and detect any delay effect between MODIS and field survey temporal series. Statistical tests are carried out for 29 sites containing 18 dominant shrubland Mediterranean species. 67% and 54% of significant correlation were found using respectively the NDII and NDWI indices from MODIS data. Correlation were found low with a dominant negative delay effect, i.e., with a MODIS signal that reacts a few days after the field VWC. Test results show that, even if deeper pre-processing of MODIS data may be required, site soil, site vegetation cover, and heterogeneity at MODIS pixel scale, as well as species VWC sensitivity make correlation between field VWC and MODIS water indices non univoque and highly variable. Many obstacles are still to overcome, for an accurate monitoring of Mediterranean shrubland canopy water content using MODIS daily data.

A basic study on comprehensive water indices using glass fiber filters and a spectrophotometer

1996 ◽  
Vol 34 (7-8) ◽  
pp. 163-168
Author(s):  
Keigo Nakamura ◽  
Yukihiro Shimatani
Keyword(s):  
Water Quality ◽  
Glass Fiber ◽  
Quality Index ◽  
Point Of View ◽  
Absorbance Spectrum ◽  
Water Color ◽  
Quality Change ◽  
Basic Study ◽  
Water Quality Change ◽  
Water Indices

Chemical and biotic indices of water quality have some problems. They need a lot of time to measure and technology, therefore we considered evaluating water color objectively and quantitatively. This method is as follows; after filtering sampling water on the glass fiber filter, absorbance spectrum of this filter is measured by the spectrophotometer. This method does not need, technology and cost. We surveyed the relation between absorbance spectrum and conventional water indices. As a result, this method is very effective to evaluate water quality change from the water color point of view abd it can also evaluate turbidity and Chlorophyll-a very easily. We expect this method to lead to a new water quality index.

Sign in / Sign up

Export Citation Format

Share Document