scholarly journals Temperature Vegetation Dryness Index-Based Soil Moisture Retrieval Algorithm Developed for Geo-KOMPSAT-2A

2021 ◽  
Vol 13 (15) ◽  
pp. 2990
Author(s):  
Sumin Ryu ◽  
Young-Joo Kwon ◽  
Goo Kim ◽  
Sungwook Hong
Keyword(s):  
Soil Moisture ◽  
East Asia ◽  
Land Surface ◽  
Vegetation Index ◽  
Normalized Difference Vegetation Index ◽  
Negative Slope ◽  
Retrieval Algorithm ◽  
Infrared Sensors ◽  
Dryness Index ◽  
Land Data Assimilation System

The Korea Meteorological Administration (KMA) has developed many product algorithms including that for soil moisture (SM) retrieval for the geostationary satellite Geo-Kompsat-2A (GK-2A) launched in December 2018. This was developed through a five-year research project owing to the significance of SM information for hydrological and meteorological applications. However, GK-2A’s visible and infrared sensors lack direct SM sensitivity. Therefore, in this study, we developed an SM algorithm based on the conversion relationships between SM and the temperature vegetation dryness index (TVDI) estimated for various land types in the full disk area using two of GK-2A’s level 2 products, land surface temperature (LST) and normalized difference vegetation index (NDVI), and the Global Land Data Assimilation System (GLDAS) SM data for calibration. Methodologically, various coefficients were obtained between TVDI and SM and used to estimate the GK-2A-based SM. The GK-2A SM algorithm was validated with GLDAS SM data during different periods. Our GK-2A SM product showed seasonal and spatial agreement with GLDAS SM data, indicating a dry-wet pattern variation. Quantitatively, the GK-2A SM showed annual validation results with a correlation coefficient (CC) > 0.75, bias < 0.1%, and root mean square error (RMSE) < 4.2–4.7%. The monthly averaged CC values were higher than 0.7 in East Asia and 0.5 in Australia, whereas RMSE and unbiased RMSE values were < 0.5% in East Asia and Australia. Discrepancies between GLDAS and GK-2A TVDI-based SMs often occurred in dry Australian regions during dry seasons due to the high LST sensitivity of GK-2A TVDI. We determined that relationships between TVDI and SM had positive or negative slopes depending on land cover types, which differs from the traditional negative slope observed between TVDI and SM. The KMA is currently operating this GK-2A SM algorithm.

Characterizing waste disposal sites by using multi-spectral satellite imagery

2021 ◽  
Author(s):  
Gaetana Ganci ◽  
Annalisa Cappello ◽  
Giuseppe Bilotta ◽  
Giuseppe Pollicino ◽  
Luigi Lodato
Keyword(s):  
Soil Moisture ◽  
Surface Temperature ◽  
Waste Disposal ◽  
Land Surface ◽  
Vegetation Index ◽  
Normalized Difference Vegetation Index ◽  
The State ◽  
Dump Site ◽  
Temperature State ◽  
Surrounding Areas

&lt;p&gt;The application of remote sensing for monitoring, detecting and analysing the spatial and extents and temporal changes of waste dumping sites and landfills could become a cost-effective and powerful solution. Multi-spectral satellite images, especially in the thermal infrared, can be exploited to characterize the state of activity of a landfill.&amp;#160; Indeed, waste disposal sites, during the period of activity, can show differences in surface temperature (LST, Land Surface Temperature), state of vegetation (estimated through NDVI, Normalized Difference Vegetation Index) or soil moisture (estimated through NDWI, Normalized Difference Water Index) compared to neighboring areas. Landfills with organic waste typically show higher temperatures than surrounding areas due to exothermic decomposition activities. In fact, the biogas, in the absence or in case of inefficiency of the conveying plants, rises through the layers of organic matter and earth (landfill body) until it reaches the surface at a temperature of over 40 &amp;#176; C. Moreover, in some cases, leachate contamination of the aquifers can be identified by analyzing the soil moisture, through the estimate of the NDWI, and the state of suffering of the vegetation surrounding the site, through the estimate of the NDVI. This latter can also be an indicator of soil contamination due to the presence of toxic and potentially dangerous waste when buried or present nearby. To take into account these facts, we combine the LST, NDVI and NDWI indices of the dump site and surrounding areas in order to characterize waste disposal sites. Preliminary results show how this approach can bring out the area and level of activity of known landfill sites. This could prove particularly useful for the definition of intervention priorities in landfill remediation works.&lt;/p&gt;

scholarly journals Improving GOES Advanced Baseline Imager (ABI) aerosol optical depth (AOD) retrievals using an empirical bias correction algorithm

2020 ◽  
Vol 13 (11) ◽  
pp. 5955-5975
Author(s):  
Hai Zhang ◽  
Shobha Kondragunta ◽  
Istvan Laszlo ◽  
Mi Zhou
Keyword(s):  
Aerosol Optical Depth ◽  
Optical Depth ◽  
Bias Correction ◽  
Land Surface ◽  
Vegetation Index ◽  
Normalized Difference Vegetation Index ◽  
Infrared Imaging ◽  
Retrieval Algorithm ◽  
Correction Algorithm ◽  
Time Step

Abstract. The Advanced Baseline Imager (ABI) on board the Geostationary Operational Environmental Satellite-R (GOES-R) series enables retrieval of aerosol optical depth (AOD) from geostationary satellites using a multiband algorithm similar to those of polar-orbiting satellites' sensors, such as the Moderate Resolution Imaging Spectroradiometer (MODIS) and Visible Infrared Imaging Radiometer Suite (VIIRS). However, this work demonstrates that the current version of GOES-16 (GOES-East) ABI AOD has diurnally varying biases due to limitations in the land surface reflectance relationships between the 0.47 µm band and the 2.2 µm band and between the 0.64 µm band and 2.2 µm band used in the ABI AOD retrieval algorithm, which vary with the Sun–satellite geometry and NDVI (normalized difference vegetation index). To reduce these biases, an empirical bias correction algorithm has been developed based on the lowest observed ABI AOD of an adjacent 30 d period and the background AOD at each time step and at each pixel. The bias correction algorithm improves the performance of ABI AOD compared to AErosol RObotic NETwork (AERONET) AOD, especially for the high and medium (top 2) quality ABI AOD. AOD data for the period 6 August to 31 December 2018 are used to evaluate the bias correction algorithm. After bias correction, the correlation between the top 2 quality ABI AOD and AERONET AOD improves from 0.87 to 0.91, the mean bias improves from 0.04 to 0.00, and root-mean-square error (RMSE) improves from 0.09 to 0.05. These results for the bias-corrected top 2 qualities ABI AOD are comparable to those of the corrected high-quality ABI AOD. By using the top 2 qualities of ABI AOD in conjunction with the bias correction algorithm, the areal coverage of ABI AOD is increased by about 100 % without loss of data accuracy.

scholarly journals A new method for estimating of evapotranspiration and surface soil moisture from optical and thermal infrared measurements: the simplified triangle

2020 ◽  
Author(s):  
Toby N. Carlson ◽  
George Petropoulos
Keyword(s):  
Soil Moisture ◽  
Land Surface ◽  
Vegetation Index ◽  
Normalized Difference Vegetation Index ◽  
Heat Fluxes ◽  
Surface Model ◽  
Landsat 8 ◽  
Simple Method ◽  
Practical Applications ◽  
Infrared Measurements

Earth Observation (EO) provides a promising approach towards deriving accurate spatiotemporal estimates of key parameters characterizing land surface interactions, such as latent (LE) and sensible (H) heat fluxes as well as soil moisture content. This paper proposes a very simple method to implement, yet reliable to calculate evapotranspiration fraction (EF) and surface moisture availability (Mo) from remotely sensed imagery of Normalized Difference Vegetation Index (NDVI) and surface radiometric temperature (Tir). The method is unique in that it derives all of its information solely from these two images. As such, it does not depend on knowing ancillary surface or atmospheric parameters, nor does it require the use of a land surface model. The procedure for computing spatiotemporal estimates of these important land surface parameters is outlined herein stepwise for practical application by the user. Moreover, as the newly developedscheme is not tied to any particular sensor, it can also beimplemented with technologically advanced EO sensors launched recently or planned to be launched such as Landsat 8 and Sentinel 3. The latter offers a number of key advantages in terms of future implementation of the method and wider use for research and practical applications alike.

scholarly journals Assessment of Soil Moisture Anomaly Sensitivity to Detect Drought Spatio-Temporal Variability in Romania

Sensors ◽  
2021 ◽  
Vol 21 (24) ◽  
pp. 8371
Author(s):  
Irina Ontel ◽  
Anisoara Irimescu ◽  
George Boldeanu ◽  
Denis Mihailescu ◽  
Claudiu-Valeriu Angearu ◽  
...  
Keyword(s):  
Soil Moisture ◽  
Land Surface ◽  
Vegetation Index ◽  
Normalized Difference Vegetation Index ◽  
Root Zone ◽  
Standardized Precipitation Index ◽  
In Situ Measurements ◽  
Near Surface ◽  
Soil Moisture Anomaly

This paper will assess the sensitivity of soil moisture anomaly (SMA) obtained from the Soil water index (SWI) product Metop ASCAT, to identify drought in Romania. The SWI data were converted from relative values (%) to absolute values (m3 m−3) using the soil porosity method. The conversion results (SM) were validated using soil moisture in situ measurements from ISMN at 5 cm depths (2015–2020). The SMA was computed based on a 10 day SWI product, between 2007 and 2020. The analysis was performed for the depths of 5 cm (near surface), 40 cm (sub surface), and 100 cm (root zone). The standardized precipitation index (SPI), land surface temperature anomaly (LST anomaly), and normalized difference vegetation index anomaly (NDVI anomaly) were computed in order to compare the extent and intensity of drought events. The best correlations between SM and in situ measurements are for the stations located in the Getic Plateau (Bacles (r = 0.797) and Slatina (r = 0.672)), in the Western Plain (Oradea (r = 0.693)), and in the Moldavian Plateau (Iasi (r = 0.608)). The RMSE were between 0.05 and 0.184. Furthermore, the correlations between the SMA and SPI, the LST anomaly, and the NDVI anomaly were significantly registered in the second half of the warm season (July–September). Due to the predominantly agricultural use of the land, the results can be useful for the management of water resources and irrigation in regions frequently affected by drought.

scholarly journals DROUGHT MONITORING IN SHANDONG PROVINCE BASED ON MODIFIED TEMPERATURE-VEGETATION-SOIL MOISTURE DRYNESS INDEX (TVMDI)

2020 ◽  
Vol XLII-3/W10 ◽  
pp. 911-916
Author(s):  
H. Wan ◽  
Z. D. Wang ◽  
P. Guo ◽  
B. Wang ◽  
X. C. Li ◽  
...  
Keyword(s):  
Soil Moisture ◽  
Correlation Coefficient ◽  
Land Surface ◽  
Vegetation Index ◽  
Shandong Province ◽  
Severe Drought ◽  
Average Correlation ◽  
Shandong Peninsula ◽  
Wide Range ◽  
Dryness Index

Abstract. Drought is one of the frequent natural disasters in Shandong province, which is characterized by high frequency and wide range. In response to frequent droughts that are not monitored in time, monitoring the changes of drought is of great significance to agricultural production and social development. This study used the Temperature-Vegetation-soil Moisture Dryness Index (TVMDI) model, combined with the optical MODIS land surface temperature, vegetation index, surface albedo data and microwave FY-3B soil moisture data, to monitor the drought of Shandong province in 2016. The precipitation and temperature data of weather station were used to validate the monitoring results. The results show that, in 2016, the drought in Shandong province mainly occurred in winter and spring, and the drought in summer was alleviated. From the perspective of space, the northern Shandong and the Shandong peninsula areas are relatively humid with less drought time, while the local areas in the central and southern Shandong province suffer from severe drought with longer drought time. From the perspective of correlation with meteorological factors, the average correlation coefficient between TVMDI and precipitation can reach 0.45, and the average correlation coefficient between TVMDI and temperature can reach 0.63.

Estimating Regional Soil Moisture with Synergistic Use of AMSR2 and MODIS Images

2021 ◽  
Vol 87 (9) ◽  
pp. 649-660
Author(s):  
Majid Rahimzadegan ◽  
Arash Davari ◽  
Ali Sayadi
Keyword(s):  
Soil Moisture ◽  
Land Surface ◽  
Vegetation Index ◽  
Normalized Difference Vegetation Index ◽  
Regional Scale ◽  
Ground Truth ◽  
Absolute Error ◽  
Simple Method ◽  
Ground Truth Data ◽  
Pi Index

Soil moisture content (SMC), product of Advanced Microwave Scanning Radiometer 2 (AMSR2), is not at an adequate level of accuracy on a regional scale. The aim of this study is to introduce a simple method to estimate SMC while synergistically using AMSR2 and Moderate Resolution Imaging Spectroradiometer (MODIS) measurements with a higher accuracy on a regional scale. Two MODIS products, including daily reflectance (MYD021) and nighttime land surface temperature (LST) products were used. In 2015, 1442 in situ SMC measurements from six stations in Iran were used as ground-truth data. Twenty models were evaluated using combinations of polarization index (PI), index of soil wetness (ISW), normalized difference vegetation index (NDVI), and LST. The model revealed the best results using a quadratic combination of PI and ISW, a linear form of LST, and a constant value. The overall correlation coefficient, root-mean-square error, and mean absolute error were 0.59, 4.62%, and 3.01%, respectively.

scholarly journals Single-Pass Soil Moisture Retrieval Using GNSS-R at L1 and L5 Bands: Results from Airborne Experiment

2021 ◽  
Vol 13 (4) ◽  
pp. 797
Author(s):  
Joan Francesc Munoz-Martin ◽  
Raul Onrubia ◽  
Daniel Pascual ◽  
Hyuk Park ◽  
Miriam Pablos ◽  
...  
Keyword(s):  
Soil Moisture ◽  
Antenna Arrays ◽  
Land Surface ◽  
Vegetation Index ◽  
Normalized Difference Vegetation Index ◽  
Monitoring Network ◽  
Dual Band ◽  
Rain Event ◽  
Single Pass ◽  
The Impact

Global Navigation Satellite System—Reflectometry (GNSS-R) has already proven its potential for retrieving a number of geophysical parameters, including soil moisture. However, single-pass GNSS-R soil moisture retrieval is still a challenge. This study presents a comparison of two different data sets acquired with the Microwave Interferometer Reflectometer (MIR), an airborne-based dual-band (L1/E1 and L5/E5a), multiconstellation (GPS and Galileo) GNSS-R instrument with two 19-element antenna arrays with four electronically steered beams each. The instrument was flown twice over the OzNet soil moisture monitoring network in southern New South Wales (Australia): the first flight was performed after a long period without rain, and the second one just after a rain event. In this work, the impact of surface roughness and vegetation attenuation in the reflectivity of the GNSS-R signal is assessed at both L1 and L5 bands. The work analyzes the reflectivity at different integration times, and finally, an artificial neural network is used to retrieve soil moisture from the reflectivity values. The algorithm is trained and compared to a 20-m resolution downscaled soil moisture estimate derived from SMOS soil moisture, Sentinel-2 normalized difference vegetation index (NDVI) data, and ECMWF Land Surface Temperature.

scholarly journals Integrating Optical and Microwave Satellite Observations for High Resolution Soil Moisture Estimate and Applications in CONUS Drought Analyses

2019 ◽  
Vol 7 (1) ◽  
Author(s):  
Donglian Sun ◽  
Yu Li ◽  
Xiwu Zhan ◽  
Chaowei Yang ◽  
Ruixin Yang
Keyword(s):  
Soil Moisture ◽  
Spatial Resolution ◽  
Land Surface ◽  
Vegetation Index ◽  
Normalized Difference Vegetation Index ◽  
High Spatial Resolution ◽  
Weather Conditions ◽  
Daily Basis ◽  
Satellite Observations ◽  
Drought Analysis

<strong>In this study, optical and microwave satellite observations are integrated to estimate soil moisture at high spatial resolution and applied for drought analysis in the continental United States.  To estimate soil moisture, a new refined model is proposed to estimate soil moisture (SM) with auxiliary data like soil texture, topography, surface types, accumulated precipitation, in addition to Normalized Difference Vegetation Index and Land Surface Temperature (LST) used in the traditional universal triangle method. It is found the new proposed SM model using accumulated precipitation demonstrated close agreements with the </strong><strong>U.S. Drought Monitor (USDM) spatial patterns.  Currently, the USDM is providing a weekly map.  Recently, “flash” drought concept appears. To obtain drought map on daily basis, LST is derived from microwave observations and downscaled to the same resolution as the thermal infrared LST product and used to fill the gaps due to clouds in optical LST data. With the integrated daily LST available under nearly all weather conditions, daily soil moisture can be estimated at relatively high spatial resolution, thus drought maps based on soil moisture anomalies can be obtained at high spatial resolution on daily basis and made the flash drought analysis and monitoring become possible.</strong>

scholarly journals Temporal Calibration of an Evaporation-Based Spatial Disaggregation Method of SMOS Soil Moisture Data

2020 ◽  
Vol 12 (10) ◽  
pp. 1671
Author(s):  
Vivien-Georgiana Stefan ◽  
Olivier Merlin ◽  
Maria-José Escorihuela ◽  
Beatriz Molero ◽  
Jamal Chihrane ◽  
...  
Keyword(s):  
Soil Moisture ◽  
Linear Regression ◽  
Linear Model ◽  
Correlation Coefficient ◽  
Land Surface ◽  
Vegetation Index ◽  
Normalized Difference Vegetation Index ◽  
Remotely Sensed ◽  
Current Version ◽  
Soil Moisture Data

The resolution of current satellite surface soil moisture (SM) estimates is very low, of tens of kilometers, which proves to be insufficient for various agricultural and hydrological applications. Amongst the existing downscaling approaches of remotely sensed SM, DISPATCH (DISaggregation based on a Physical And Theoretical scale CHange) improves the resolution of SMOS (Soil Moisture and Ocean Salinity) soil moisture data using soil evaporative efficiency (SEE) estimates at high resolution (HR) and a SEE(SM) model implemented at low resolution (LR). Defined as the ratio of actual to potential soil evaporation, SEE can be derived from the remotely sensed land surface temperature (LST) and normalized difference vegetation index (NDVI). The current version of DISPATCH uses a linear SEE(SM) model. This study aims at improving the SEE(SM) model and testing different calibration strategies, to ultimately have more robust and better downscaled SM products. A nonlinear SEE(SM) model is introduced and its influence on the derived HR SM products is studied over a range of conditions. Each model, linear and nonlinear, is calibrated from remote sensing data on a daily and a multi-date basis. The approaches were tested over two mixed dry and irrigated areas in Catalonia, Spain, and over one dry area in Morocco. When using the linear model, better statistical results were generally obtained using a daily calibration (current version of DISPATCH), most notably over one Spanish site. However, the best results were systematically obtained for an annually calibrated nonlinear model, in terms of all metrics considered: correlation coefficient, slope of the linear regression, bias, unbiased root mean square error. In particular, when using the annually calibrated nonlinear SEE (SM) model, the temporal slope of the linear regression between disaggregated and in situ soil moisture increased to 1.16 and 0.75 for one Spanish site and for the Moroccan site (as opposed to 0.44 and 0.58, respectively, when using the linear model with a daily calibration). The temporal correlation coefficient increased to 0.47 and 0.54 over the Spanish sites (as opposed to 0.18 and 0.27, respectively, when using the linear model with a daily calibration). Those contrasted results indicate compensation effects between the model type and the calibration strategy. Taking into account studies that report the strong nonlinear behavior of the SEE with respect to SM, the introduction of the nonlinear SEE(SM) model in DISPATCH, combined with a multi-date calibration, is proven to perform significantly better under various conditions, leading to more robust disaggregated SM products. The SEE modeling based on the nonlinear SM model, with a multi-date calibration, could be integrated into the CATDS—Centre Aval de Traitement des Données SMOS as a future product, as well as into existing evapotranspiration models, which are based on a combination of thermal and microwave data.

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