scholarly journals Soil Moisture in the Biebrza Wetlands Retrieved from Sentinel-1 Imagery

2018 ◽  
Author(s):  
Katarzyna Dabrowska-Zielinska ◽  
Jan Musial ◽  
Alicja Malinska ◽  
Maria Budzynska ◽  
Radoslaw Gurdak ◽  
...  
Keyword(s):  
Soil Moisture ◽  
Vegetation Index ◽  
Normalized Difference Vegetation Index ◽  
Incidence Angle ◽  
Temporal Frequency ◽  
European Space Agency ◽  
Wetland Ecosystems ◽  
Space Agency ◽  
Moderate Resolution Imaging Spectroradiometer ◽  
The Difference

Soil moisture (SM) plays an essential role in environmental studies related to wetlands, an ecosystem sensitive to climate change. Hence, there is the need for its constant monitoring. SAR (Synthetic Aperture Radar) satellite imagery is the only mean to fulfill this objective regardless of the weather. The objective of the study was to develop the methodology for SM retrieval under wetland vegetation using Sentinel-1 (S-1) satellite data. The study was carried out during the years 2015–2017 in the Biebrza Wetlands, situated in northeastern Poland. At the Biebrza Wetlands, two Sentinel-1 validation sites were established, covering grassland and marshland biomes, where a network of 18 stations for soil moisture measurement was deployed. The sites were funded by the European Space Agency (ESA), and the collected measurements are available through the International Soil Moisture Network (ISMN). The NDVI (Normalized Difference Vegetation Index) was derived from the optical imagery of a MODIS (Moderate Resolution Imaging Spectroradiometer) sensor onboard the Terra satellite. The SAR data of the Sentinel-1 satellite with VH (vertical transmit and horizontal receive) and VV (vertical transmit and vertical receive) polarization were applied to soil moisture retrieval for a broad range of NDVI values and soil moisture conditions. The new methodology is based on research into the effect of vegetation on backscatter () changes under different soil moisture and vegetation (NDVI) conditions. It was found that the state of the vegetation may be described by the difference between  VH and  VV, or the ratio of  VV/VH, as calculated from the Sentinel-1 images. The most significant correlation coefficient for soil moisture was found for data that was acquired from the ascending tracks of the Sentinel-1 satellite, characterized by the lowest incidence angle, and SM at a depth of 5 cm. The study demonstrated that the use of the inversion approach, which was applied to the new developed models and includes the derived indices based on S-1, allowed the estimation of SM for peatlands with reasonable accuracy (RMSE ~ 10 vol. %). Due to the temporal frequency of the two S-1 satellites’ (S-1A and S-1B) acquisitions, it is possible to monitor SM changes every six days. The conclusion drawn from the study emphasizes a demand for the derivation of specific soil moisture retrieval algorithms that are suited for wetland ecosystems, where soil moisture is several times higher than in agricultural areas.

scholarly journals Soil Moisture in the Biebrza Wetlands Retrieved from Sentinel-1 Imagery

2018 ◽  
Vol 10 (12) ◽  
pp. 1979 ◽  
Author(s):  
Katarzyna Dabrowska-Zielinska ◽  
Jan Musial ◽  
Alicja Malinska ◽  
Maria Budzynska ◽  
Radoslaw Gurdak ◽  
...  
Keyword(s):  
Soil Moisture ◽  
Vegetation Index ◽  
Normalized Difference Vegetation Index ◽  
Cloud Model ◽  
Incidence Angle ◽  
European Space Agency ◽  
Wetland Ecosystems ◽  
Space Agency ◽  
Moderate Resolution Imaging Spectroradiometer ◽  
The Difference

The objective of the study was to estimate soil moisture (SM) from Sentinel-1 (S-1) satellite images acquired over wetlands. The study was carried out during the years 2015–2017 in the Biebrza Wetlands, situated in north-eastern Poland. At the Biebrza Wetlands, two Sentinel-1 validation sites were established, covering grassland and marshland biomes, where a network of 18 stations for soil moisture measurement was deployed. The sites were funded by the European Space Agency (ESA), and the collected measurements are available through the International Soil Moisture Network (ISMN). The SAR data of the Sentinel-1 satellite with VH (vertical transmit and horizontal receive) and VV (vertical transmit and vertical receive) polarization were applied to SM retrieval for a broad range of vegetation and soil moisture conditions. The methodology is based on research into the effect of vegetation on backscatter (σ°) changes under different soil moisture and Normalized Difference Vegetation Index (NDVI) values. The NDVI was derived from the optical imagery of a MODIS (Moderate Resolution Imaging Spectroradiometer) sensor onboard the Terra satellite. It was found that the state of the vegetation expressed by NDVI can be described by the indices such as the difference between σ° VH and VV, or the ratio of σ° VV/VH, as calculated from the Sentinel-1 images in the logarithmic domain. The most significant correlation coefficient for soil moisture was found for data that was acquired from the ascending tracks of the Sentinel-1 satellite, characterized by the lowest incidence angle, and SM at a depth of 5 cm. The study demonstrated that the use of the inversion approach, which was applied to the newly developed models using Water Cloud Model (WCM) that includes the derived indices based on S-1, allowed the estimation of SM for wetlands with reasonable accuracy (10 vol. %). The developed soil moisture retrieval algorithms based on S-1 data are suited for wetland ecosystems, where soil moisture values are several times higher than in agricultural areas.

scholarly journals IDENTIFICATION OF DROUGHT EXTENT BASED ON VCI USING SENTINEL DATA: A CASE STUDY OF THE EASTERN OF IAŞI COUNTY

2019 ◽  
Vol 13 (2) ◽  
pp. 179-186
Author(s):  
Paul Macarof ◽  
Florian Statescu ◽  
Cristian Iulian Birlica ◽  
Paul Gherasim
Keyword(s):  
Vegetation Index ◽  
Normalized Difference Vegetation Index ◽  
European Space Agency ◽  
Condition Index ◽  
Landsat 8 ◽  
Natural Phenomena ◽  
Space Agency ◽  
Vegetation Condition Index ◽  
Sentinel 2

In this study was analyzed zones affected by drought using Vegetation Condition Index (VCI), that is based on Normalized Difference Vegetation Index (NDVI). This fact, drought, is one of the most wide -spread and least understood natural phenomena. In this paper was used remote sensing (RS) data, kindly provided by The European Space Agency (ESA), namely Sentinel-2 (S-2) Multispectral Instrument (MSI) and wellkonwn images Landsat 8 Operational Land Imager (OLI). The RS images was processed in SNAP and ArcMap. Study Area, was considered the eastern of Iasi county. The main purpose of paper was to investigating if Sentinel images can be used for VCI analysis.

scholarly journals Mapping drought-induced changes in rice area in India

2019 ◽  
Vol 40 (21) ◽  
pp. 8146-8173 ◽  
Author(s):  
Gumma ◽  
Nelson ◽  
Yamano
Keyword(s):  
Vegetation Index ◽  
Normalized Difference Vegetation Index ◽  
Human Life ◽  
Rice Varieties ◽  
Rice Area ◽  
Multi Temporal ◽  
Geospatial Datasets ◽  
Moderate Resolution Imaging Spectroradiometer ◽  
The Difference ◽  
Matching Techniques

Rice is a staple food crop of India and is grown on 44 Mha (2011–12), 58.6% of which are irrigated. An inevitable phenomenon which looms over all aspects of human life and affects rice production in India is drought. Assessing drought damage using geospatial datasets available in the public domain, such as the Normalized Difference Vegetation Index (NDVI) derived from the Moderate Resolution Imaging Spectroradiometer (MODIS), can provide specific and local ecoregion information for developing drought-resistant rice varieties. Based on multi-temporal NDVI data and field observations in 2009, we developed a methodology to identify and map drought-affected areas in India. A long-term (10-year) average of NDVI during the rainy (kharif) season (June–October) was compared with NDVI from a known drought year (2002–03) to identify changes in rice area. Rainfall data from the Tropical Rainfall Monitoring Mission (TRMM) was used to support the drought analysis. Spectral matching techniques were used to categorise the drought-affected rice areas into three classes – severe, moderate, and mild based on the intensity of damage assessed through field sampling. Based on these ground survey samples, spectral signatures were generated. It was found that the rice area was about 16% less in the drought year (2002–03) than in a normal year (2000–01). A comparison of the MODIS-derived rice area affected by drought in 2002 for each state and district against the difference in the kharif season harvested rice area between 2000 and 2002 (from official statistics) revealed a substantial difference in harvested area in 2002 that was largely attributable to drought. An 84.7% correlation was found between the MODIS-derived drought-affected area in 2002 and the reduction in harvested area from 2000–01 to 2002–03. Good spatial correlation was found between the drought-affected rice areas and reduction of rice harvested areas in different rice ecologies, indicating the usefulness of such geospatial datasets in assessing abiotic stress such as drought and its consequences.

scholarly journals Evaluation of Fengyun-3C Soil Moisture Products Using In situ Data from the Chinese Automatic Soil Moisture Observation Stations: A Case Study in Henan Province, China

2018 ◽  
Author(s):  
Yongchao Zhu ◽  
Simon Pearson ◽  
Dongli Wu ◽  
Ruijing Sun ◽  
Shibo Fang
Keyword(s):  
Soil Moisture ◽  
Correlation Coefficient ◽  
Large Scale ◽  
Vegetation Index ◽  
Normalized Difference Vegetation Index ◽  
Vegetation Density ◽  
Statistical Parameters ◽  
In Situ Data ◽  
Moderate Resolution Imaging Spectroradiometer

Soil moisture (SM) products derived from passive satellite missions are playing an increasingly important role in agricultural applications, especially in crop monitoring and disaster warning. Evaluating the dependability of those products before they can be used on a large scale is crucial. In this study, we assessed the level 2 (L2) SM product from the Chinese Fengyun-3C (FY-3C) radiometer against in situ measurements collected from the Chinese Automatic Soil Moisture Observation Stations (CASMOS) during a one-year period from January 1 to December 31, 2016 in Henan, which is an agricultural province in China. Four statistical parameters were used to evaluate the products’ reliability: mean difference, root-mean-square error (RMSE), unbiased RMSE (ubRMSE), and the correlation coefficient. These statistical indicators revealed that the FY-3C L2 SM product generally did not agree with the in situ SM data from CASMOS. The time-series analysis further indicated that the correlations and estimated error were highly related to the growing periods of the crops in our study area. FY-3C L2 SM data tended to overestimate soil moisture during May, August, and September, when the crops reach their maximum vegetation density, and tended to underestimate the soil moisture content during the rest of the year. The averaged correlation coefficient between FY-3C SM and the Moderate Resolution Imaging Spectroradiometer (MODIS) normalized difference vegetation index was 0.55, which demonstrates that the vegetation water content of the crops considerably influences the SM product. To improve the accuracy of the FY-3C SM product, an improved algorithm that can filter out the influences of the crops should be applied in the future.

scholarly journals Quantifying the Reflectance Anisotropy Effect on Albedo Retrieval from Remotely Sensed Observations Using Archetypal BRDFs

2018 ◽  
Vol 10 (10) ◽  
pp. 1628 ◽  
Author(s):  
Hu Zhang ◽  
Ziti Jiao ◽  
Lei Chen ◽  
Yadong Dong ◽  
Xiaoning Zhang ◽  
...  
Keyword(s):  
Vegetation Index ◽  
Normalized Difference Vegetation Index ◽  
Least Squares Method ◽  
Distribution Functions ◽  
Relative Uncertainty ◽  
Uncertainty Range ◽  
Reflectance Anisotropy ◽  
Anisotropy Effect ◽  
Moderate Resolution Imaging Spectroradiometer ◽  
The Difference

The reflectance anisotropy effect on albedo retrieval was evaluated using the Moderate Resolution Imaging Spectroradiometer (MODIS) bidirectional reflectance distribution functions (BRDFs) product, and archetypal BRDFs. Shortwave-band archetypal BRDFs were established, and validated, based on the Anisotropy Flat indeX (AFX) and time series MODIS BRDF over tile h11v03. To generate surface albedo, archetypal BRDFs were used to fit simulated reflectance, based on the least squares method. Albedo was also retrieved based on the least root-mean-square-error (RMSE) method or normalized difference vegetation index (NDVI) based prior BRDF knowledge. The difference between those albedos and the MODIS albedo was used to quantify the reflectance anisotropy effect. The albedo over tile h11v03 for day 185 in 2009 was retrieved from single directional reflectance and the third archetypal BRDF. The results show that six archetypal BRDFs are sufficient to represent the reflectance anisotropy for albedo estimation. For the data used in this study, the relative uncertainty caused by reflectance anisotropy can reach up to 7.4%, 16.2%, and 20.2% for sufficient, insufficient multi-angular and single directional observations. The intermediate archetypal BRDFs may be used to improve the albedo retrieval accuracy from insufficient or single observations with a relative uncertainty range of 8–15%.

scholarly journals Recovery Rates of Wetland Vegetation Greenness in Severely Burned Ecosystems of Alaska Derived from Satellite Image Analysis

2018 ◽  
Vol 10 (9) ◽  
pp. 1456 ◽  
Author(s):  
Christopher Potter
Keyword(s):  
Vegetation Index ◽  
Normalized Difference Vegetation Index ◽  
Satellite Image ◽  
Burned Area ◽  
Recovery Rates ◽  
Wetland Ecosystems ◽  
Modis Ndvi ◽  
High Severity ◽  
Green Cover ◽  
Moderate Resolution Imaging Spectroradiometer

The analysis of wildfire impacts at the scale of less than a square kilometer can reveal important patterns of vegetation recovery and regrowth in freshwater Arctic and boreal regions. For this study, NASA Landsat burned area products since the year 2000, and a near 20-year record of vegetation green cover from the MODIS (Moderate Resolution Imaging Spectroradiometer) satellite sensor were combined to reconstruct the recovery rates and seasonal profiles of burned wetland ecosystems in Alaska. Region-wide breakpoint analysis results showed that significant structural change could be detected in the 250-m normalized difference vegetation index (NDVI) time series for the vast majority of wetland locations in the major Yukon river drainages of interior Alaska that had burned at high severity since the year 2001. Additional comparisons showed that wetland cover locations across Alaska that have burned at high severity subsequently recovered their green cover seasonal profiles to relatively stable pre-fire levels in less than 10 years. Negative changes in the MODIS NDVI, namely lower greenness in 2017 than pre-fire and incomplete greenness recovery, were more commonly detected in burned wetland areas after 2013. In the years prior to 2013, the NDVI change tended to be positive (higher greenness in 2017 than pre-fire) at burned wetland elevations lower than 400 m, whereas burned wetland locations at higher elevation showed relatively few positive greenness recovery changes by 2017.

scholarly journals SMOS-IC: An alternative SMOS soil moisture and vegetation optical depth product

2017 ◽  
Author(s):  
Roberto Fernandez-Moran ◽  
Amen Al-Yaari ◽  
Arnaud Mialon ◽  
Ali Mahmoodi ◽  
Ahmad Al Bitar ◽  
...  
Keyword(s):  
Soil Moisture ◽  
Optical Depth ◽  
Vegetation Index ◽  
Normalized Difference Vegetation Index ◽  
Weather Forecasting ◽  
Global Scale ◽  
Modis Ndvi ◽  
Level 3 ◽  
Moderate Resolution Imaging Spectroradiometer ◽  
Land Surfaces

The main goal of the Soil Moisture and Ocean Salinity (SMOS) mission over land surfaces is the production of global maps of soil moisture (SM) and vegetation optical depth (τ) based on multi-angular brightness temperature (TB) measurements at L-band. The operational SMOS Level 2 and Level 3 soil moisture algorithms account for different surface effects, such as vegetation opacity and soil roughness at 4 km resolution, in order to produce global retrievals of SM and τ. In this study, we present an alternative SMOS product which was developed by INRA (Institut National de la Recherche Agronomique) and CESBIO (Centre d’Etudes Spatiales de la BIOsphère). This SMOS-INRA-CESBIO (SMOS-IC) product provides daily SM and τ at the global scale and differs from the operational SMOS Level 3 (SMOSL3) product in the treatment of retrievals over heterogeneous pixels. Specifically, SMOS-IC is much simpler and does not account for corrections associated to the antenna pattern and the complex SMOS viewing angle geometry. It considers pixels as homogeneous to avoid uncertainties and errors linked to inconsistent auxiliary data sets which are used to characterize the pixel heterogeneity in the SMOS L3 algorithm. SMOS-IC also differs from the current SMOSL3 product (Version 300, V300) in the values of the effective vegetation scattering albedo (ω) and soil roughness parameters. An inter-comparison is presented in this study based on the use of ECMWF (European Center for Medium range Weather Forecasting) SM outputs and NDVI (Normalized Difference Vegetation Index) from MODIS (Moderate-Resolution Imaging Spectroradiometer). A 6 year (2010-2015) inter-comparison of the SMOS products SMOS-IC and SMOSL3 SM (V300) with ECMWF SM yielded higher correlations and lower ubRMSD (unbiased root mean square difference) for SMOS-IC over most of the pixels. In terms of τ, SMOS-IC τ was found to be better correlated to MODIS NDVI in most regions of the globe, with the exception of the Amazonian basin and of the northern mid-latitudes.

scholarly journals МОНІТОРИНГ СІЛЬСЬКОГОСПОДАРСЬКИХ КУЛЬТУР ІЗ ЗАСТОСУВАННЯМ КОСМІЧНИХ ЗНІМКІВ SENTINEL-2

2019 ◽  
pp. 99-108
Author(s):  
Максим В’ячеславович Марюшко ◽  
Руслан Едуардович Пащенко ◽  
Наталія Сергіївна Коблюк
Keyword(s):  
Vegetation Index ◽  
Normalized Difference Vegetation Index ◽  
Spatial Information ◽  
European Space Agency ◽  
Remote Sensing Data ◽  
Space Images ◽  
Space Imagery ◽  
Space Agency ◽  
Vegetation Phase ◽  
Sentinel 2

The subject of the study in the article is the growing need for the use of spatial information for efficient agricultural production, due to the growing tendency of Earth remote sensing data accessibility, which, due to the spatial and temporal resolution improvement, can be used in the land cover analysis and other related jobs. The goal is to review the obtaining process of satellite multispectral space imagery from Sentinel-2 and to consider the possibility of their use for monitoring crops during the entire vegetation phase. The tasks: to study the modern needs of agricultural producers in the field of analysis of land cover occupied by agricultural crops; the analysis of the European Space Agency programs and the global land program Copernicus, which uses spatial information from Sentinel-2 for use in the agricultural sector; estimation of the constellation characteristics of Sentinel-2, imaging equipment and remote sensing data processing results by ground services received from Internet services; the use of Sentinel-2 multispectral space imagery for monitoring crops during the entire vegetation phase. The following results were obtained. After analyzing agricultural producers needs and the European Space Agency program, the feasibility of using multispectral space images taken by the Multispectral Instrument installed on satellites Sentinel-2 was established. Free access to the space imagery database is provided through the Copernicus Open Access Hub Internet Service. For the researched territory, Poltava region, Chutov district, the village of Vilkhovatka, various time space images were obtained and the normalized difference vegetation index (NDVI) was calculated. Histogram analysis of the obtained vegetation index values distribution within a single field (corn to grain) allows to reveal a quantitative and qualitative change in biomass, indicating a change in the vegetative phase. Conclusions. The approach described in this paper allows to conduct monitoring of the cropping state during the vegetation phase using both qualitative – visual analysis and quantitative – NDVI index, criteria. The change in the values of the normalized difference vegetation index can reveal a change in the biomass state. However, for calculating the NDVI index, data from near-infrared and red channels is needed, which complicates the acquisition of the original image. Therefore, in order to obtain the quantitative criteria in subsequent jobs, it is expedient to consider the possibility of using fractal dimension, which will reduce the amount of input data required for calculations.

scholarly journals SMOS near real time soil moisture product: processor overview and first validation results

2017 ◽  
Author(s):  
Nemesio Rodríguez-Fernández ◽  
Joaquin Muñoz Sabater ◽  
Philippe Richaume ◽  
Patricia de Rosnay ◽  
Yann Kerr ◽  
...  
Keyword(s):  
Soil Moisture ◽  
Real Time ◽  
Surface Soil ◽  
Weather Prediction ◽  
European Space Agency ◽  
Retrieval Algorithm ◽  
Space Agency ◽  
Wide Range ◽  
The Difference ◽  
Level 2

Abstract. Measurements of the surface soil moisture (SM) content are important for a wide range of applications. Among them, operational hydrology and numerical weather prediction, for instance, need soil moisture information in near-real-time (NRT), typically not later than 3 hours after sensing. The European Space Agency (ESA) Soil Moisture and Ocean Salinity (SMOS) satellite is the first mission specifically designed to measure soil moisture from space. The ESA level 2 SM retrieval algorithm is based on a detailed geophysical modelling and cannot provide SM in NRT. This paper presents the new ESA SMOS NRT SM product. It uses a neural network (NN) to provide SM in NRT. The NN inputs are SMOS brightness temperatures for horizontal and vertical polarizations and incidence angles from 30º to 45º. In addition, the NN uses surface soil temperature from the European Centre for Medium Range Weather Forecasts (ECMWF) Integrated Forecast System (IFS). The NN was trained on SMOS Level 2 SM. The swath of the NRT SM retrieval is somewhat narrower (~ 915 km) than that of the L2 SM dataset (~ 1150 km), which implies a slightly lower revisit time. The new SMOS NRT SM product was compared to the SMOS Level 2 SM product. The NRT SM data shows a standard deviation of the difference with respect to the L2 data of

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