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 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 A New Method for Estimating of Evapotranspiration and Surface Soil Moisture from Optical and Thermal Infrared Measurements: The Simplified Triangle

2019 ◽  
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 developed scheme is not tied to any particular sensor, it can also be implemented 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 Mapping the Groundwater Level and Soil Moisture of a Montane Peat Bog Using UAV Monitoring and Machine Learning

2021 ◽  
Vol 13 (5) ◽  
pp. 907
Author(s):  
Theodora Lendzioch ◽  
Jakub Langhammer ◽  
Lukáš Vlček ◽  
Robert Minařík
Keyword(s):  
Machine Learning ◽  
Soil Moisture ◽  
Spatial Data ◽  
Groundwater Level ◽  
Vegetation Index ◽  
Normalized Difference Vegetation Index ◽  
Sampling Strategy ◽  
Peat Bog ◽  
Strong Impact ◽  
Ground Truth Data

One of the best preconditions for the sufficient monitoring of peat bog ecosystems is the collection, processing, and analysis of unique spatial data to understand peat bog dynamics. Over two seasons, we sampled groundwater level (GWL) and soil moisture (SM) ground truth data at two diverse locations at the Rokytka Peat bog within the Sumava Mountains, Czechia. These data served as reference data and were modeled with a suite of potential variables derived from digital surface models (DSMs) and RGB, multispectral, and thermal orthoimages reflecting topomorphometry, vegetation, and surface temperature information generated from drone mapping. We used 34 predictors to feed the random forest (RF) algorithm. The predictor selection, hyperparameter tuning, and performance assessment were performed with the target-oriented leave-location-out (LLO) spatial cross-validation (CV) strategy combined with forward feature selection (FFS) to avoid overfitting and to predict on unknown locations. The spatial CV performance statistics showed low (R2 = 0.12) to high (R2 = 0.78) model predictions. The predictor importance was used for model interpretation, where temperature had strong impact on GWL and SM, and we found significant contributions of other predictors, such as Normalized Difference Vegetation Index (NDVI), Normalized Difference Index (NDI), Enhanced Red-Green-Blue Vegetation Index (ERGBVE), Shape Index (SHP), Green Leaf Index (GLI), Brightness Index (BI), Coloration Index (CI), Redness Index (RI), Primary Colours Hue Index (HI), Overall Hue Index (HUE), SAGA Wetness Index (TWI), Plan Curvature (PlnCurv), Topographic Position Index (TPI), and Vector Ruggedness Measure (VRM). Additionally, we estimated the area of applicability (AOA) by presenting maps where the prediction model yielded high-quality results and where predictions were highly uncertain because machine learning (ML) models make predictions far beyond sampling locations without sampling data with no knowledge about these environments. The AOA method is well suited and unique for planning and decision-making about the best sampling strategy, most notably with limited data.

scholarly journals A Simple Method for Converting 1-km Resolution Daily Clear-Sky LST into Real LST

2020 ◽  
Vol 12 (10) ◽  
pp. 1641
Author(s):  
Yunfei Zhang ◽  
Yunhao Chen ◽  
Jing Li ◽  
Xi Chen
Keyword(s):  
Land Surface ◽  
Missing Values ◽  
Vegetation Index ◽  
Normalized Difference Vegetation Index ◽  
Shortwave Radiation ◽  
Simple Method ◽  
The Real ◽  
Independent Variables ◽  
Clear Sky ◽  
Reconstruction Methods

Land-surface temperature (LST) plays a key role in the physical processes of surface energy and water balance from local through global scales. The widely used one kilometre resolution daily Moderate Resolution Imaging Spectroradiometer (MODIS) LST product has missing values due to the influence of clouds. Therefore, a large number of clear-sky LST reconstruction methods have been developed to obtain spatially continuous LST datasets. However, the clear-sky LST is a theoretical value that is often an overestimate of the real value. In fact, the real LST (also known as cloudy-sky LST) is more necessary and more widely used. The existing cloudy-sky LST algorithms are usually somewhat complicated, and the accuracy needs to be improved. It is necessary to convert the clear-sky LST obtained by the currently better-developed methods into cloudy-sky LST. We took the clear-sky LST, cloud-cover duration, downward shortwave radiation, albedo and normalized difference vegetation index (NDVI) as five independent variables and the real LST at the ground stations as the dependent variable to perform multiple linear regression. The mean absolute error (MAE) of the cloudy-sky LST retrieved by this method ranged from 3.5–3.9 K. We further analyzed different cases of the method, and the results suggested that this method has good flexibility. When we chose fewer independent variables, different clear-sky algorithms, or different regression tools, we also achieved good results. In addition, the method calculation process was relatively simple and can be applied to other research areas. This study preliminarily explored the influencing factors of the real LST and can provide a possible option for researchers who want to obtain cloudy-sky LST through clear-sky LST, that is, a convenient conversion method. This article lays the foundation for subsequent research in various fields that require real LST.

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

<p>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.  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 ° 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.</p>

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 Comparison and Ground Truthing of Different Remote and Proximal Sensing Platforms to Characterize Variability in a Hedgerow-Trained Vineyard

2021 ◽  
Vol 13 (11) ◽  
pp. 2056
Author(s):  
Cecilia Squeri ◽  
Stefano Poni ◽  
Salvatore Filippo Di Gennaro ◽  
Alessandro Matese ◽  
Matteo Gatti
Keyword(s):  
Spatial Resolution ◽  
Vegetation Index ◽  
Normalized Difference Vegetation Index ◽  
Growth Yield ◽  
Ground Truth ◽  
Large Degree ◽  
Proximal Sensing ◽  
Ground Truth Data ◽  
Ground Truthing ◽  
Sentinel 2

Appropriate characterization of intra-parcel variability is a key element for the effective application of precision farming techniques. Nowadays there are many platforms available to end users differing for pixel spatial resolution and the type of acquisition (remote or proximal). A challenging aspect pertaining to remote sensing image acquisition in the vineyard ecosystem is that, in a large majority of cases, vegetation is discontinuous and single rows alternate with strips of either bare or grassed soil. In this paper, four different satellite platforms (Sentinel-2, Spot-6, Pleiades, and WorldView-3) having different spatial resolution and MECS-VINE® proximity sensor were compared in terms of accuracy at describing spatial variability. Vineyard mapping was coupled with detailed ground truthing of growth, yield, and grape composition variables. The analysis was conducted based on vigor indices (Normalized Difference Vegetation Index or Canopy Index) and using the Moran Index (MI) to assess the degree of spatial auto-correlation for the different variables. The results obtained showed a large degree of intra-plot variability in the main agronomic parameters (pruning weight CV: 33.86%, yield: 32.09%). The univariate Moran index showed a log-linear function relating MI coefficients to the resolution levels. Comparison between vigor indices and agronomic data showed that the highest bivariate MI was reached by Pleiades followed by MECS-VINE® which also did not exhibit the negative effect of the border pixel owing to the proximal scanning acquisition. Despite WorldView-3′s high resolution (1.24 m pixel) allowing very detailed data imaging, the comparison with ground-truth data was not encouraging, probably due to the presence of pure ground pixels, while Sentinel-2 was affected by the oversized pixel at 10 m.

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>

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