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 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 and Irrigation Mapping in A Semi-Arid Region, Based on the Synergetic Use of Sentinel-1 and Sentinel-2 Data

2018 ◽  
Vol 10 (12) ◽  
pp. 1953 ◽  
Author(s):  
Safa Bousbih ◽  
Mehrez Zribi ◽  
Mohammad El Hajj ◽  
Nicolas Baghdadi ◽  
Zohra Lili-Chabaane ◽  
...  
Keyword(s):  
Soil Moisture ◽  
Arid Region ◽  
Vegetation Index ◽  
Normalized Difference Vegetation Index ◽  
Cloud Model ◽  
Remotely Sensed Data ◽  
Semi Arid Region ◽  
Semi Arid ◽  
Sentinel 2 ◽  
Reference Fields

This paper presents a technique for the mapping of soil moisture and irrigation, at the scale of agricultural fields, based on the synergistic interpretation of multi-temporal optical and Synthetic Aperture Radar (SAR) data (Sentinel-2 and Sentinel-1). The Kairouan plain, a semi-arid region in central Tunisia (North Africa), was selected as a test area for this study. Firstly, an algorithm for the direct inversion of the Water Cloud Model (WCM) was developed for the spatialization of the soil water content between 2015 and 2017. The soil moisture retrieved from these observations was first validated using ground measurements, recorded over 20 reference fields of cereal crops. A second method, based on the use of neural networks, was also used to confirm the initial validation. The results reported here show that the soil moisture products retrieved from remotely sensed data are accurate, with a Root Mean Square Error (RMSE) of less than 5% between the two moisture products. In addition, the analysis of soil moisture and Normalized Difference Vegetation Index (NDVI) products over cultivated fields, as a function of time, led to the classification of irrigated and rainfed areas on the Kairouan plain, and to the production of irrigation maps at the scale of individual fields. This classification is based on a decision tree approach, using a combination of various statistical indices of soil moisture and NDVI time series. The resulting irrigation maps were validated using reference fields within the study site. The best results were obtained with classifications based on soil moisture indices only, with an accuracy of 77%.

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 Monitoring Surface Soil Moisture Content over the Vegetated Area by Integrating Optical and SAR Satellite Observations in the Permafrost Region of Tibetan Plateau

2020 ◽  
Vol 12 (1) ◽  
pp. 183 ◽  
Author(s):  
Chenyang Xu ◽  
John J. Qu ◽  
Xianjun Hao ◽  
Di Wu
Keyword(s):  
Soil Moisture ◽  
Tibetan Plateau ◽  
Water Content ◽  
Vegetation Index ◽  
Surface Soil ◽  
Cloud Model ◽  
Incidence Angle ◽  
Permafrost Region ◽  
Surface Soil Moisture ◽  
The Impact

Surface soil moisture (SSM), the average water content of surface soil (up to 5 cm depth), plays a key role in the energy exchange within the ecosystem. We estimated SSM in areas with vegetation cover (grassland) by combining microwave and optical satellite measurements in the central Tibetan Plateau (TP) in 2015. We exploited TERRA moderate resolution imaging spectroradiometer (MODIS) and Sentinel-1A synthetic aperture radar (SAR) observations to estimate SSM through a simplified water-cloud model (sWCM). This model considers the impact of vegetation water content (VWC) to SSM retrieval by integrating the vegetation index (VI), the normalized difference water index (NDWI), or the normalized difference infrared index (NDII). Sentinel-1 SAR C-band backscattering coefficients, incidence angle, and NDWI/NDII were assimilated in the sWCM to monitor SSM. The soil moisture and temperature monitoring network on the central TP (CTP-SMTMN) measures SSM within the study area, and ground measurements were applied to train and validate the model. Via the proposed methods, we estimated the SSM in vegetated area with an R2 of 0.43 and a ubRMSE of 0.06 m3/m3 when integrating the NDWI and with an R2 of 0.45 and a ubRMSE of 0.06 m3/m3 when integrating the NDII.

scholarly journals Comparative Analysis of Landsat-8, Sentinel-2, and GF-1 Data for Retrieving Soil Moisture over Wheat Farmlands

2020 ◽  
Vol 12 (17) ◽  
pp. 2708 ◽  
Author(s):  
Qi Wang ◽  
Jiancheng Li ◽  
Taoyong Jin ◽  
Xin Chang ◽  
Yongchao Zhu ◽  
...  
Keyword(s):  
Soil Moisture ◽  
Optical Sensors ◽  
Vegetation Index ◽  
Normalized Difference Vegetation Index ◽  
Cloud Model ◽  
Vegetation Indices ◽  
Optical Data ◽  
Landsat 8 ◽  
Different Sources ◽  
Sentinel 2

Soil moisture is an important variable in ecological, hydrological, and meteorological studies. An effective method for improving the accuracy of soil moisture retrieval is the mutual supplementation of multi-source data. The sensor configuration and band settings of different optical sensors lead to differences in band reflectivity in the inter-data, further resulting in the differences between vegetation indices. The combination of synthetic aperture radar (SAR) data with multi-source optical data has been widely used for soil moisture retrieval. However, the influence of vegetation indices derived from different sources of optical data on retrieval accuracy has not been comparatively analyzed thus far. Therefore, the suitability of vegetation parameters derived from different sources of optical data for accurate soil moisture retrieval requires further investigation. In this study, vegetation indices derived from GF-1, Landsat-8, and Sentinel-2 were compared. Based on Sentinel-1 SAR and three optical data, combined with the water cloud model (WCM) and the advanced integral equation model (AIEM), the accuracy of soil moisture retrieval was investigated. The results indicate that, Sentinel-2 data were more sensitive to vegetation characteristics and had a stronger capability for vegetation signal detection. The ranking of normalized difference vegetation index (NDVI) values from the three sensors was as follows: the largest was in Sentinel-2, followed by Landsat-8, and the value of GF-1 was the smallest. The normalized difference water index (NDWI) value of Landsat-8 was larger than that of Sentinel-2. With reference to the relative components in the WCM model, the contribution of vegetation scattering exceeded that of soil scattering within a vegetation index range of approximately 0.55–0.6 in NDVI-based models and all ranges in NDWI1-based models. The threshold value of NDWI2 for calculating vegetation water content (VWC) was approximately an NDVI value of 0.4–0.55. In the soil moisture retrieval, Sentinel-2 data achieved higher accuracy than data from the other sources and thus was more suitable for the study for combination with SAR in soil moisture retrieval. Furthermore, compared with NDVI, higher accuracy of soil moisture could be retrieved by using NDWI1 (R2 = 0.623, RMSE = 4.73%). This study provides a reference for the selection of optical data for combination with SAR in soil moisture retrieval.

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.

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