scholarly journals An Improved Method for Pan-Tropical Above-Ground Biomass and Canopy Height Retrieval Using CYGNSS

2021 ◽  
Vol 13 (13) ◽  
pp. 2491
Author(s):  
Fade Chen ◽  
Fei Guo ◽  
Lilong Liu ◽  
Yang Nan
Keyword(s):  
Soil Moisture ◽  
Correlation Coefficient ◽  
Traditional Method ◽  
Satellite System ◽  
Canopy Height ◽  
Above Ground Biomass ◽  
Improved Method ◽  
Ground Biomass ◽  
Artificial Neural Network Ann ◽  
Global Navigation Satellite

An improved method for retrieving Above-ground Biomass (AGB) and Canopy Height (CH) based on an observable from Cyclone Global Navigation Satellite System (CYGNSS), soil moisture from Soil Moisture Active Passive (SMAP) and location is proposed. The observable derived from CYGNSS is more sensitive to vegetation. The CYGNSS observable, soil moisture and the location are used as the input features of an Artificial Neural Network (ANN) to retrieve AGB and CH. The sensitivity analysis of the CYGNSS observable to target parameters shows that the proposed observable is more sensitive to AGB/CH than the conventional observable. The AGB/CH retrievals of the improved method show that it has better performance than that of the traditional method, especially in the areas with AGB in the range of 0 to100 Mg/ha and CH in the range of 0 to10 m. For AGB retrievals, the root mean square error (RMSE) and correlation coefficient are 64.84 Mg/ha and 0.80 in the range of 0 to 550 Mg/ha. Compared with the traditional method, the RMSE is decreased by 11.63%, while the correlation coefficient is increased by 5.26%. For CH retrievals, the RMSE and correlation coefficient are 5.97 m and 0.83 in the range of 0 to 45 m. The RMSE is decreased by 12.59%, while the correlation coefficient is increased by 5.06%. The analysis of the improved method in different areas shows that the performance of the improved method over the area with high vegetation is better than the area with low vegetation. The results obtained here further strengthens the capability of GNSS-R for global AGB/CH retrievals as well as different land cover areas.

scholarly journals Above-Ground Biomass Retrieval over Tropical Forests: A Novel GNSS-R Approach with CyGNSS

2020 ◽  
Vol 12 (9) ◽  
pp. 1368 ◽  
Author(s):  
Hugo Carreno-Luengo ◽  
Guido Luzi ◽  
Michele Crosetto
Keyword(s):  
Soil Moisture ◽  
Tropical Forests ◽  
Empirical Relationship ◽  
Global Navigation Satellite Systems ◽  
Navigation Satellite ◽  
Above Ground Biomass ◽  
Laser Altimeter ◽  
Ground Biomass ◽  
Global Navigation ◽  
Global Navigation Satellite

An assessment of the National Aeronautics and Space Administration NASA’s Cyclone Global Navigation Satellite System (CyGNSS) mission for biomass studies is presented in this work on rain, coniferous, dry, and moist tropical forests. The main objective is to investigate the capability of Global Navigation Satellite Systems Reflectometry (GNSS-R) for biomass retrieval over dense forest canopies from a space-borne platform. The potential advantage of CyGNSS, as compared to monostatic Synthetic Aperture Radar (SAR) missions, relies on the increasing signal attenuation by the vegetation cover, which gradually reduces the coherent scattering component σ coh , 0 . This term can only be collected in a bistatic radar geometry. This point motivates the study of the relationship between several observables derived from Delay Doppler Maps (DDMs) with Above-Ground Biomass (AGB). This assessment is performed at different elevation angles θ e as a function of Canopy Height (CH). The selected biomass products are obtained from data collected by the Geoscience Laser Altimeter System (GLAS) instrument on-board the Ice, Cloud, and land Elevation Satellite (ICESat-1). An analysis based on the first derivative of the experimentally derived polynomial fitting functions shows that the sensitivity requirements of the Trailing Edge TE and the reflectivity Γ reduce with increasing biomass up to ~ 350 and ~ 250 ton/ha over the Congo and Amazon rainforests, respectively. The empirical relationship between TE and Γ with AGB is further evaluated at optimum angular ranges using Soil Moisture Active Passive (SMAP)-derived Vegetation Optical Depth ( VOD ), and the Polarization Index ( PI ). Additionally, the potential influence of Soil Moisture Content (SMC) is investigated over forests with low AGB.

scholarly journals Computation Approach for Quantitative Dielectric Constant from Time Sequential Data Observed by CYGNSS Satellites

2021 ◽  
Vol 13 (11) ◽  
pp. 2032
Author(s):  
Junchan Lee ◽  
Sunil Bisnath ◽  
Regina S.K. Lee ◽  
Narin Gavili Kilane
Keyword(s):  
Dielectric Constant ◽  
Soil Moisture ◽  
Global Navigation Satellite System ◽  
Measurement Data ◽  
Satellite System ◽  
Computation Method ◽  
Navigation Satellite ◽  
Local Soil ◽  
Global Navigation ◽  
Global Navigation Satellite

This paper describes a computation method for obtaining dielectric constant using Global Navigation Satellite System reflectometry (GNSS-R) products. Dielectric constant is a crucial component in the soil moisture retrieval process using reflected GNSS signals. The reflectivity for circular polarized signals is combined with the dielectric constant equation that is used for radiometer observations. Data from the Cyclone Global Navigation Satellite System (CYGNSS) mission, an eight-nanosatellite constellation for GNSS-R, are used for computing dielectric constant. Data from the Soil Moisture Active Passive (SMAP) mission are used to measure the soil moisture through its radiometer, and they are considered as a reference to confirm the accuracy of the new dielectric constant calculation method. The analyzed locations have been chosen that correspond to sites used for the calibration and validation of the SMAP soil moisture product using in-situ measurement data. The retrieved results, especially in the case of a specular point around Yanco, Australia, show that the estimated results track closely to the soil moisture results, and the Root Mean Square Error (RMSE) in the estimated dielectric constant is approximately 5.73. Similar results can be obtained when the specular point is located near the Texas Soil Moisture Network (TxSON), USA. These results indicate that the analysis procedure is well-defined, and it lays the foundation for obtaining quantitative soil moisture content using the GNSS reflectometry results. Future work will include applying the computation product to determine the characteristics that will allow for the separation of coherent and incoherent signals in delay Doppler maps, as well as to develop local soil moisture models.

scholarly journals Potential Applications of GNSS-R Observations over Agricultural Areas: Results from the GLORI Airborne Campaign

2018 ◽  
Vol 10 (8) ◽  
pp. 1245 ◽  
Author(s):  
Mehrez Zribi ◽  
Erwan Motte ◽  
Nicolas Baghdadi ◽  
Frédéric Baup ◽  
Sylvia Dayau ◽  
...  
Keyword(s):  
Soil Moisture ◽  
Signal To Noise Ratio ◽  
Soil Surface ◽  
Satellite System ◽  
Area Index ◽  
Vegetation Height ◽  
Study Sites ◽  
Global Navigation Satellite ◽  
Vegetation Characteristic ◽  
Agricultural Areas

The aim of this study is to analyze the sensitivity of airborne Global Navigation Satellite System Reflectometry (GNSS-R) on soil surface and vegetation cover characteristics in agricultural areas. Airborne polarimetric GNSS-R data were acquired in the context of the GLORI’2015 campaign over two study sites in Southwest France in June and July of 2015. Ground measurements of soil surface parameters (moisture content) and vegetation characteristics (leaf area index (LAI), and vegetation height) were recorded for different types of crops (corn, sunflower, wheat, soybean, vegetable) simultaneously with the airborne GNSS-R measurements. Three GNSS-R observables (apparent reflectivity, the reflected signal-to-noise-ratio (SNR), and the polarimetric ratio (PR)) were found to be well correlated with soil moisture and a major vegetation characteristic (LAI). A tau-omega model was used to explain the dependence of the GNSS-R reflectivity on both the soil moisture and vegetation parameters.

scholarly journals Deriving surface soil moisture from reflected GNSS signal observations from a grassland site in southwestern France

2017 ◽  
Author(s):  
Sibo Zhang ◽  
Jean-Christophe Calvet ◽  
José Darrozes ◽  
Nicolas Roussel ◽  
Frédéric Frappart ◽  
...  
Keyword(s):  
Soil Moisture ◽  
Land Surface ◽  
Land Surface Model ◽  
Ground Surface ◽  
Satellite System ◽  
Surface Model ◽  
Global Navigation Satellite ◽  
Grassland Site

Abstract. This work aims to assess the estimation of surface volumetric soil moisture (VSM) using the Global Navigation Satellite System Interferometric Reflectometry (GNSS-IR) technique. Year-round observations were acquired from a grassland site in southwestern France using an antenna consecutively placed at two contrasting heights above the ground surface (3.3 or 29.4 m). The VSM retrievals are compared with two independent reference datasets: in situ observations of soil moisture, and numerical simulations of soil moisture and vegetation biomass from the ISBA (Interactions between Soil, Biosphere and Atmosphere) land surface model. Scaled VSM estimates can be retrieved throughout the year removing vegetation effects by the separation of growth and senescence periods and by the filtering of the GNSS-IR observations that are most affected by vegetation. Antenna height has no significant impact on the quality of VSM estimates. Comparisons between the VSM GNSS-IR retrievals and the in situ VSM observations at a depth of 5 cm show a good agreement (R2 = 0.86 and RMSE = 0.04 m3 m−3). It is shown that the signal is sensitive to the grass litter water content and that this effect triggers differences between VSM retrievals and in situ VSM observations at depths of 1 cm and 5 cm, especially during light rainfall events.

scholarly journals Quality Improvement of Satellite Soil Moisture Products by Fusing with In-Situ Measurements and GNSS-R Estimates in the Western Continental U.S.

2018 ◽  
Vol 10 (9) ◽  
pp. 1351 ◽  
Author(s):  
Hongzhang Xu ◽  
Qiangqiang Yuan ◽  
Tongwen Li ◽  
Huanfeng Shen ◽  
Liangpei Zhang ◽  
...  
Keyword(s):  
Soil Moisture ◽  
Water Cycle ◽  
Satellite System ◽  
Global Scale ◽  
Generalized Regression Neural Network ◽  
Level 3 ◽  
Global Navigation Satellite ◽  
Microwave Sensors ◽  
Surface Fusion

Soil moisture is a key component of the water cycle budget. Sensing soil moisture using microwave sensors onboard satellites is an effective way to retrieve surface soil moisture (SSM) at a global scale, but the retrieval accuracy in some regions is inadequate due to the complicated factors influencing the general retrieval process. On the other hand, monitoring soil moisture directly through in-situ devices is capable of providing high-accuracy SSM measurements, but the distribution of such stations is sparse. Recently, the Global Navigation Satellite System interferometric Reflectometry (GNSS-R) method was used to derive field-scale SSM, which can serve as a supplement to contemporary sparse in-situ soil moisture networks. On this basis, it is of great research significance to explore the fusion of these different kinds of SSM data, so as to improve the present satellite SSM products with regard to their data accuracy. In this paper, a multi-source point-surface fusion method based on the generalized regression neural network (GRNN) model is applied to fuse the Soil Moisture Active Passive (SMAP) Level 3 radiometer SSM daily product with in-situ measured and GNSS-R estimated SSM data from five soil moisture networks in the western continental U.S. The results show that the GRNN model obtains a fairly good performance, with a cross-validation R value of approximately 0.9 and a ubRMSE of 0.044 cm3 cm−3. Furthermore, the fused SSM product agrees well with the site-specific SSM data in terms of time and space, which demonstrates that the proposed GRNN model is able to construct the non-linear relationship between the point- and surface-scale SSM.

Growth and yield response of winter wheat to soil warming and rainfall patterns

2010 ◽  
Vol 148 (5) ◽  
pp. 553-566 ◽  
Author(s):  
R. H. PATIL ◽  
M. LAEGDSMAND ◽  
J. E. OLESEN ◽  
J. R. PORTER
Keyword(s):  
Climate Change ◽  
Soil Moisture ◽  
Winter Wheat ◽  
Rainfall Amount ◽  
Growth And Yield ◽  
Above Ground Biomass ◽  
Soil Warming ◽  
Ground Biomass ◽  
Rainfall Frequency ◽  
Crop Development

SUMMARYIt is predicted that climate change will increase not only seasonal air and soil temperatures in northern Europe but also the variability of rainfall patterns. This may influence temporal soil moisture regimes and the growth and yield of winter wheat. A lysimeter experiment was carried out in 2008/09 with three factors: rainfall amount, rainfall frequency and soil warming (two levels in each factor), on sandy loam soil in Denmark. The soil warming treatment included non-heated as the control and an increase in soil temperature by 5°C at 100 mm depth as heated. The rainfall treatment included the site mean for 1961–90 as the control and the projected monthly mean change for 2071–2100 under the International Panel on Climate Change (IPCC) A2 scenario for the climate change treatment. Projected monthly mean changes in rainfall compared to the reference period 1961–90 show, on average, 31% increase during winter (November–March) and 24% decrease during summer (July–September) with no changes during spring (April–June). The rainfall frequency treatment included mean monthly rainy days for 1961–90 as the control and a reduced frequency treatment with only half the number of rainy days of the control treatment, without altering the monthly mean rainfall amount. Mobile rain-out shelters, automated irrigation system and insulated heating cables were used to impose the treatments.Soil warming hastened crop development during early stages (until stem elongation) and shortened the total crop growing season by 12 days without reducing the period taken for later development stages. Soil warming increased green leaf area index (GLAI) and above-ground biomass during early growth, which was accompanied by an increased amount of nitrogen (N) in plants. However, the plant N concentration and its dilution pattern during later developmental stages followed the same pattern in both heated and control plots. Increased soil moisture deficit was observed only during the period when crop growth was significantly enhanced by soil warming. However, soil warming reduced N concentration in above-ground biomass during the entire growing period, except at harvest, by advancing crop development. Soil warming had no effect on the number of tillers, but reduced ear number and increased 1000 grain weight. This did not affect grain yield and total above-ground biomass compared with control. This suggests that genotypes with a longer vegetative period would probably be better adapted to future warmer conditions. The rainfall pattern treatments imposed in the present study did not influence either soil moisture regimes or performance of winter wheat, though the crop receiving future rainfall amount tended to retain more green leaf area. There was no significant interaction between the soil warming and rainfall treatments on crop growth.

scholarly journals High Spatial-Temporal Resolution Estimation of Ground-Based Global Navigation Satellite System Interferometric Reflectometry (GNSS-IR) Soil Moisture Using the Genetic Algorithm Back Propagation (GA-BP) Neural Network

2021 ◽  
Vol 10 (9) ◽  
pp. 623
Author(s):  
Yajie Shi ◽  
Chao Ren ◽  
Zhiheng Yan ◽  
Jianmin Lai
Keyword(s):  
Neural Network ◽  
Machine Learning ◽  
Soil Moisture ◽  
Temporal Resolution ◽  
Bp Neural Network ◽  
Back Propagation ◽  
Satellite System ◽  
Environmental Data ◽  
Navigation Satellite ◽  
Global Navigation Satellite

Soil moisture is one of the critical variables in maintaining the global water cycle balance. Moreover, it plays a vital role in climate change, crop growth, and environmental disaster event monitoring, and it is important to monitor soil moisture continuously. Recently, Global Navigation Satellite System interferometric reflectometry (GNSS-IR) technology has become essential for monitoring soil moisture. However, the sparse distribution of GNSS-IR soil moisture sites has hindered the application of soil moisture products. In this paper, we propose a multi-data fusion soil moisture inversion algorithm based on machine learning. The method uses the Genetic Algorithm Back-Propagation (GA-BP) neural network model, by combining GNSS-IR site data with other surface environmental parameters around the site. In turn, soil moisture is obtained by inversion, and we finally obtain a soil moisture product with a high spatial and temporal resolution of 500 m per day. The multi-surface environmental data include latitude and longitude information, rainfall, air temperature, land cover type, normalized difference vegetation index (NDVI), and four topographic factors (elevation, slope, slope direction, and shading). To maximize the spatial and temporal resolution of the GNSS-IR technique within a machine learning framework, we obtained satisfactory results with a cross-validated R-value of 0.8660 and an ubRMSE of 0.0354. This indicates that the machine learning approach learns the complex nonlinear relationships between soil moisture and the input multi-surface environmental data. The soil moisture products were analyzed compared to the contemporaneous rainfall and National Aeronautics and Space Administration (NASA)’s soil moisture products. The results show that the spatial distribution of the GA-BP inversion soil moisture products is more consistent with rainfall and NASA products, which verifies the feasibility of using this experimental model to generate 500 m per day the GA-BP inversion soil moisture products.

scholarly journals Mangrove Above-Ground Biomass and Carbon Stock in the Karimunjawa-Kemujan Islands Estimated from Unmanned Aerial Vehicle-Imagery

2022 ◽  
Vol 14 (2) ◽  
pp. 706
Author(s):  
Anindya Wirasatriya ◽  
Rudhi Pribadi ◽  
Sigit Bayhu Iryanthony ◽  
Lilik Maslukah ◽  
Denny Nugroho Sugianto ◽  
...  
Keyword(s):  
Unmanned Aerial Vehicle ◽  
Carbon Stock ◽  
Digital Terrain Model ◽  
Satellite System ◽  
Allometric Equation ◽  
Surface Model ◽  
Above Ground Biomass ◽  
Ground Biomass ◽  
Carbon Biomass ◽  
Aerial Vehicle

Blue carbon ecosystems in the Karimunjawa Islands may play a vital role in absorbing and storing the releasing carbon from the Java Sea. The present study investigated mangrove above-ground biomass (AGB) and carbon stock in the Karimunjawa-Kemujan Islands, the largest mangrove area in the Karimunjawa Islands. Taking the aerial photos from an Unmanned Aerial Vehicle combined with Global Navigation Satellite System (GNSS) measurements, we generated Digital Surface Model (DSM) and Digital Terrain Model (DTM) with high accuracy. We calculated mangrove canopy height by subtracting DSM from DTM and then converted it into Lorey’s height. The highest mangrove canopy is located along the coastline facing the sea, ranging from 8 m to 15 m. Stunted mangroves 1 m to 8 m in height are detected mainly in the inner areas. AGBs were calculated using an allometric equation destined for the Southeast and East Asia region. Above-ground carbon biomass is half of AGB. The AGB and carbon biomass of mangroves in the Karimunjawa-Kemujan Islands range from 8 Mg/ha to 328 Mg/ha, and from 4 MgC/ha to 164 MgC/ha, respectively. With a total area of 238.98 ha, the potential above-ground carbon stored in the study area is estimated as 16,555.46 Mg.

LiDAR remote sensing of forest structure

2003 ◽  
Vol 27 (1) ◽  
pp. 88-106 ◽  
Author(s):  
Kevin Lim ◽  
Paul Treitz ◽  
Michael Wulder ◽  
Benoît St-Onge ◽  
Martin Flood
Keyword(s):  
Remote Sensing ◽  
Forest Structure ◽  
Forest Ecosystems ◽  
Canopy Height ◽  
Forest Monitoring ◽  
Light Detection And Ranging ◽  
Lidar Data ◽  
Above Ground Biomass ◽  
Ground Biomass ◽  
Canopy Volume

Light detection and ranging (LiDAR) technology provides horizontal and vertical information at high spatial resolutions and vertical accuracies. Forest attributes such as canopy height can be directly retrieved from LiDAR data. Direct retrieval of canopy height provides opportunities to model above-ground biomass and canopy volume. Access to the vertical nature of forest ecosystems also offers new opportunities for enhanced forest monitoring, management and planning.

Earth Surface Monitoring with Spire’s New GNSS Reflectometry (GNSS-R) CubeSats

2020 ◽  
Author(s):  
Vahid Freeman ◽  
Dallas Masters ◽  
Philp Jales ◽  
Stephan Esterhuizen ◽  
Ellie Ebrahimi ◽  
...  
Keyword(s):  
Soil Moisture ◽  
Spatial Resolution ◽  
Precise Orbit Determination ◽  
Satellite System ◽  
Detection Algorithm ◽  
Earth Surface ◽  
Wind Speeds ◽  
Gnss Reflectometry ◽  
The Uk ◽  
Global Navigation Satellite

<p>Spire Global operates the world’s largest and rapidly growing constellation of CubeSats performing GNSS based science and Earth observation. The Spire constellation, performs a variety of GNSS science, including radio occultation (GNSS-RO), ionosphere and space weather measurements, and precise orbit determination. In December 2019, Spire launched two new satellites to perform GNSS reflectometry (GNSS-R). GNSS-R is a relatively new technique based on a passive bistatic radar system. The potential of space-borne GNSS-R observations for ocean and land applications has been demonstrated by other GNSS-R missions, including the NASA Cyclone Global Navigation Satellite System (CYGNSS) and the UK’s Technology Demonstration Satellite, TechDemoSat (TDS-1). </p><p>We present initial results from these new Spire GNSS-R satellites that are primarily focused on retrieving soil moisture but also estimate other Earth surface properties such as ocean wind speeds and flood inundation/wetland mapping. Prior to the launch of Spire’s GNSS-R satellites and in preparation for Level-2 data production, we developed algorithms and processing chains for land applications. We will present Spire's Soil Moisture (SM) retrieval method using CYGNSS observations. We evaluated the implemented SM change detection algorithm by comparing the Spire’s daily SM product with NASA’s Soil Moisture Active Passive (SMAP) observations and in-situ SM measurements. The results of study indicate remarkable retrieval skills of the GNSS-R technique for soil moisture monitoring at a medium spatial resolution. Spire’s GNSS-R satellites are tuned for land applications with a series of hardware and software optimizations for better signal calibration and acquiring many more data per satellite compared to CYGNSS. A more robust GNSS-R SM retrieval at finer spatial resolution will be possible in the near future after having more Spire satellites in orbit.</p><p>Spire’s current and future GNSS-R satellites will provide unprecedented sub-daily global coverage with sub-kilometer spatial resolution. Such intensive data acquisition is of great importance for many land and ocean applications. </p>

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