scholarly journals Detecting Biophysical Characteristics and Nitrogen Status of Finger Millet at Hyperspectral and Multispectral Resolutions

2021 ◽  
Vol 2 ◽  
Author(s):  
Gurjinder S. Baath ◽  
K. Colton Flynn ◽  
Prasanna H. Gowda ◽  
Vijaya Gopal Kakani ◽  
Brian K. Northup
Keyword(s):  
Prediction Accuracy ◽  
Finger Millet ◽  
Canopy Cover ◽  
Hyperspectral Data ◽  
Canopy Height ◽  
Landsat 8 ◽  
Dry Biomass ◽  
Green Index ◽  
Crop Parameters ◽  
Sentinel 2

Finger millet (Eleusine coracana Gaertn L.) is an important grain crop for small farmers in many countries. Reliable estimates of crop parameters, such as crop growth and nitrogen (N) content, through remote sensing techniques can improve in-season management of finger millet. This study investigated the relationships of hyperspectral reflectance with canopy height, green canopy cover, leaf area index (LAI), and N concentrations of finger millet using an optimal waveband selection procedure with partial least square regression (PLSR). Predictive performance of 13 vegetation indices (VIs) computed from the original hyperspectral data as well as synthesized Landsat-8 and Sentinel-2 data were evaluated and compared for estimating various crop parameters with simple linear regression (SLR) and multilinear regression (MLR) models. The optimal wavebands determined by PLSR were mostly concentrated within 1,000–1,100 nm for both LAI and dry biomass but were scattered for other canopy parameters. The SLR statistics resulted in the simple ratio pigment index (SRPI) and red/green index (RGI) performing best when predicting LAI (R2v = 0.53–0.59) and canopy cover (R2v = 0.72–0.76). The blue/green index (BGI1) was strongly related to canopy height (R2v = 0.65–0.78), dry biomass (R2v = 0.42–0.49), and N concentration (R2v = 0.70–0.83) of finger millet, regardless of spectral resolutions. The MLR approach, using four maximum VIs as input variables, improved the prediction accuracy of N concentration by 14% compared to both SLR and waveband selection methods. VIs computed from synthesized Landsat-8 and Sentinel-2 satellite data resulted in similar or greater prediction accuracy than hyperspectral data for various canopy parameters of finger millet, indicating publicly accessible multispectral data could serve as alternative to hyperspectral data for improved crop management decisions via precision agriculture.

scholarly journals Comparison of Multi-Temporal PlanetScope Data with Landsat 8 and Sentinel-2 Data for Estimating Airborne LiDAR Derived Canopy Height in Temperate Forests

2020 ◽  
Vol 12 (11) ◽  
pp. 1876 ◽  
Author(s):  
Katsuto Shimizu ◽  
Tetsuji Ota ◽  
Nobuya Mizoue ◽  
Hideki Saito
Keyword(s):  
Time Series ◽  
Prediction Accuracy ◽  
Time Series Data ◽  
Airborne Lidar ◽  
Canopy Height ◽  
Forest Monitoring ◽  
Series Data ◽  
Landsat 8 ◽  
Multi Temporal ◽  
Sentinel 2

Developing accurate methods for estimating forest structures is essential for efficient forest management. The high spatial and temporal resolution data acquired by CubeSat satellites have desirable characteristics for mapping large-scale forest structural attributes. However, most studies have used a median composite or single image for analyses. The multi-temporal use of CubeSat data may improve prediction accuracy. This study evaluates the capabilities of PlanetScope CubeSat data to estimate canopy height derived from airborne Light Detection and Ranging (LiDAR) by comparing estimates using Sentinel-2 and Landsat 8 data. Random forest (RF) models using a single composite, multi-seasonal composites, and time-series data were investigated at different spatial resolutions of 3, 10, 20, and 30 m. The highest prediction accuracy was obtained by the PlanetScope multi-seasonal composites at 3 m (relative root mean squared error: 51.3%) and Sentinel-2 multi-seasonal composites at the other spatial resolutions (40.5%, 35.2%, and 34.2% for 10, 20, and 30 m, respectively). The results show that RF models using multi-seasonal composites are 1.4% more accurate than those using harmonic metrics from time-series data in the median. PlanetScope is recommended for canopy height mapping at finer spatial resolutions. However, the unique characteristics of PlanetScope data in a spatial and temporal context should be further investigated for operational forest monitoring.

scholarly journals Soil Organic Carbon Mapping Using Multispectral Remote Sensing Data: Prediction Ability of Data with Different Spatial and Spectral Resolutions

2019 ◽  
Vol 11 (24) ◽  
pp. 2947 ◽  
Author(s):  
Daniel Žížala ◽  
Robert Minařík ◽  
Tereza Zádorová
Keyword(s):  
Prediction Accuracy ◽  
Remote Sensing Data ◽  
Cost Effective ◽  
Hyperspectral Data ◽  
Landsat 8 ◽  
Prediction Ability ◽  
Multispectral Data ◽  
Multispectral Remote Sensing ◽  
Data Source ◽  
Sentinel 2

The image spectral data, particularly hyperspectral data, has been proven as an efficient data source for mapping of the spatial variability of soil organic carbon (SOC). Multispectral satellite data are readily available and cost-effective sources of spectral data compared to costly and technically demanding processing of hyperspectral data. Moreover, their continuous acquisition allows to develop a composite from time-series, increasing the spatial coverage of SOC maps. In this study, an evaluation of the prediction ability of models assessing SOC using real multispectral remote sensing data from different platforms was performed. The study was conducted on a study plot (1.45 km2) in the Chernozem region of South Moravia (Czechia). The adopted methods included field sampling and predictive modeling using satellite multispectral Sentinel-2, Landsat-8, and PlanetScope data, and multispectral UAS Parrot Sequoia data. Furthermore, the performance of a soil reflectance composite image from Sentinel-2 data was analyzed. Aerial hyperspectral CASI 1500 and SASI 600 data was used as a reference. Random forest, support vector machine, and the cubist regression technique were applied in the predictive modeling. The prediction accuracy of models using multispectral data, including Sentinel-2 composite, was lower (RPD range from 1.16 to 1.65; RPIQ range from 1.53 to 2.17) compared to the reference model using hyperspectral data (RPD = 2.26; RPIQ = 3.34). The obtained results show very similar prediction accuracy for all spaceborne sensors (Sentinel-2, Landsat-8, and PlanetScope). However, the spatial correlation between the reference mapping results obtained from the hyperspectral data and other maps using multispectral data was moderately strong. UAS sensors and freely available satellite multispectral data can represent an alternative cost-effective data source for remote SOC mapping on the local scale.

scholarly journals Kc and LAI Estimations Using Optical and SAR Remote Sensing Imagery for Vineyards Plots

2020 ◽  
Vol 12 (21) ◽  
pp. 3478
Author(s):  
Ofer Beeri ◽  
Yishai Netzer ◽  
Sarel Munitz ◽  
Danielle Ferman Mintz ◽  
Ran Pelta ◽  
...  
Keyword(s):  
Time Series ◽  
Temporal Resolution ◽  
Crop Coefficient ◽  
Landsat 8 ◽  
Area Index ◽  
Management Zone ◽  
Growing Seasons ◽  
Crop Parameters ◽  
Better Than ◽  
Sentinel 2

Daily or weekly irrigation monitoring conducted per sub-field or management zone is an important factor in vine irrigation decision-making. The objective is to determine the crop coefficient (Kc) and the leaf area index (LAI). Since the 1990s, optic satellite imagery has been utilized for this purpose, yet cloud-cover, as well as the desire to increase the temporal resolution, raise the need to integrate more imagery sources. The Sentinel-1 (a C-band synthetic aperture radar—SAR) can solve both issues, but its accuracy for LAI and Kc mapping needs to be determined. The goals of this study were as follows: (1) to test different methods for integrating SAR and optic sensors for increasing temporal resolution and creating seamless time-series of LAI and Kc estimations; and (2) to evaluate the ability of Sentinel-1 to estimate LAI and Kc in comparison to Sentinel-2 and Landsat-8. LAI values were collected at two vineyards, over three (north plot) and four (south plot) growing seasons. These values were converted to Kc, and both parameters were tested against optic and SAR indices. The results present the two Sentinel-1 indices that achieved the best accuracy in estimating the crop parameters and the best method for fusing the optic and the SAR data. Utilizing these achievements, the accuracy of the Kc and LAI estimations from Sentinel-1 were slightly better than the Sentinel-2′s and the Landsat-8′s accuracy. The integration of all three sensors into one seamless time-series not only increases the temporal resolution but also improves the overall accuracy.

scholarly journals Using ICESat-2 to Estimate and Map Forest Aboveground Biomass: A First Example

2020 ◽  
Vol 12 (11) ◽  
pp. 1824 ◽  
Author(s):  
Lana L. Narine ◽  
Sorin C. Popescu ◽  
Lonesome Malambo
Keyword(s):  
Land Cover ◽  
Aboveground Biomass ◽  
Canopy Cover ◽  
Canopy Height ◽  
Landsat 8 ◽  
Ecosystem Structure ◽  
Laser Altimeter ◽  
Weak Beam ◽  
Using Data ◽  
Forest Aboveground Biomass

National Aeronautics and Space Administration’s (NASA’s) Ice, Cloud, and land Elevation Satellite-2 (ICESat-2) provides rich insights over the Earth’s surface through elevation data collected by its Advanced Topographic Laser Altimeter System (ATLAS) since its launch in September 2018. While this mission is primarily aimed at capturing ice measurements, ICESat-2 also provides data over vegetated areas, offering the capability to gain insights into ecosystem structure and the potential to contribute to the sustainable management of forests. This study involved an examination of the utility of ICESat-2 for estimating forest aboveground biomass (AGB). The objectives of this study were to: (1) investigate the use of canopy metrics for estimating AGB, using data extracted from an ICESat-2 transect over forests in south-east Texas; (2) compare the accuracy for estimating AGB using data from the strong beam and weak beam; and (3) upscale predicted AGB estimates using variables from Landsat multispectral imagery and land cover and canopy cover maps, to generate a 30 m spatial resolution AGB map. Methods previously developed with simulated ICESat-2 data over Sam Houston National Forest (SHNF) in southeast Texas were adapted using actual data from an adjacent ICESat-2 transect over similar vegetation conditions. Custom noise filtering and photon classification algorithms were applied to ICESat-2’s geolocated photon data (ATL03) for one beam pair, consisting of a strong and weak beam, and canopy height estimates were retrieved. Canopy height parameters were extracted from 100 m segments in the along-track direction for estimating AGB, using regression analysis. ICESat-2-derived AGB estimates were then extrapolated to develop a 30 m AGB map for the study area, using vegetation indices from Landsat 8 Operational Land Imager (OLI), National Land Cover Database (NLCD) landcover and canopy cover, with random forests (RF). The AGB estimation models used few canopy parameters and suggest the possibility for applying well-developed methods for modeling AGB with airborne light detection and ranging (lidar) data, using processed ICESat-2 data. The final regression model achieved a R2 and root mean square error (RMSE) value of 0.62 and 24.63 Mg/ha for estimating AGB and RF model evaluation with a separate test set yielded a R2 of 0.58 and RMSE of 23.89 Mg/ha. Findings provide an initial look at the ability of ICESat-2 to estimate AGB and serve as a basis for further upscaling efforts.

scholarly journals Sentinel-2 and Landsat-8 Multi-Temporal Series to Estimate Topsoil Properties on Croplands

2021 ◽  
Vol 13 (17) ◽  
pp. 3345
Author(s):  
Fabio Castaldi
Keyword(s):  
Time Series ◽  
Soil Properties ◽  
Spatial Resolution ◽  
Prediction Accuracy ◽  
Prediction Models ◽  
Visible Region ◽  
Bare Soil ◽  
Landsat 8 ◽  
Economic Perspectives ◽  
Sentinel 2

The spatial and temporal monitoring of soil organic carbon (SOC), and other soil properties related to soil erosion, is extremely important, both from the environmental and economic perspectives. Sentinel-2 (S2) and Landsat-8 (L8) time series increase the probability to observe bare soil fields in croplands, and thus, monitor soil properties over large regions. In this regard, this work suggests an automated pixel-based approach to select only pure soil pixels in S2 and L8 time series, and to make a synthetic bare soil image (SBSI). The SBSIs and the soil properties measured in the framework of the European LUCAS survey were used to calibrate SOC, clay, and CaCO3 prediction models. The results highlight a high correlation between laboratory soil spectra and the SBSIs median spectra, especially for the SBSI obtained by a three-year S2 collection, which provides satisfactory results in terms of SOC prediction accuracy (RPD: 1.74). The comparison between S2 and L8 results demonstrated the higher capability of the S2 sensor in terms of SOC prediction accuracy, mainly due to the greater spatial resolution of the bands in the visible region. Whereas, neither S2 nor L8 could accurately predict the clay and CaCO3 content. This is because of the low spectral and spatial resolution of their SWIR bands that prevent the exploitation of the narrow spectral features related to these two soil attributes. The results of this study prove that large S2 time series can estimate and monitor SOC in croplands using an automated pixel-based approach that selects pure soil pixels and retrieves reliable synthetic soil spectra.

scholarly journals Spatial Patterns of Chemical Weathering at the Basal Tertiary Nonconformity in California from Multispectral and Hyperspectral Optical Remote Sensing

2019 ◽  
Vol 11 (21) ◽  
pp. 2528 ◽  
Author(s):  
Francis J. Sousa ◽  
Daniel J. Sousa
Keyword(s):  
Spatial Patterns ◽  
Chemical Weathering ◽  
Spectral Response ◽  
Imaging Spectroscopy ◽  
Information Loss ◽  
Hyperspectral Data ◽  
Landsat 8 ◽  
Optical Remote Sensing ◽  
Geologic Mapping ◽  
Sentinel 2

Visible through shortwave (VSWIR) spectral reflectance of the geologic units across the basal Tertiary nonconformity (BTN) is characterized at three spatially disparate locations in California. At two of these sites, location-specific spectral endmembers are obtained from AVIRIS imaging spectroscopy and linear spectral mixture models are used to visualize spatial patterns in chemical weathering associated with the BTN. Weathering patterns are found to match well with traditional geologic maps of the BTN at each site, but results show more spatially detailed quantitative geologic information about the spatial variability of chemical weathering near the nonconformity than is possible in a traditional geologic map. Spectral endmembers and unmixing results are also compared across locations. At the two locations with AVIRIS coverage, strong absorptions centered near 2200 nm are observed, consistent with previous geologic publications reporting intense chemical weathering at the BTN. Information loss associated with multispectral sampling of the reflectance continuum is also examined by resampling endmembers from the Maniobra location to mimic the spectral response functions of the WorldView 3, Sentinel-2 and Landsat 8 sensors. Simulated WorldView 3 data most closely approximate the full information content of the AVIRIS observations, resulting in nearly unbiased unmixing results for both endmembers. Mean fraction differences are −0.02 and +0.03 for weathered and unweathered endmembers, respectively. Sentinel-2 and Landsat 8 are unable to distinguish narrow, deep SWIR absorptions from changes in the overall amplitude of the SWIR spectral continuum, resulting in information loss and biased unmixing results. Finally, we characterize a third location using Sentinel-2 observations only. At this site we also find spectrally distinct features associated with several lithologies, providing new information relevant to the mapping of geologic contacts which is neither present in high spatial resolution visible imagery, nor in published geologic mapping. Despite these limitations, the spatial pattern of the Sentinel-2 and Landsat 8 fraction estimates is sufficiently similar to that of the WorldView 3 and AVIRIS fraction estimates to be useful for mapping purposes in cases where hyperspectral data are unavailable.

Comparison of Sentinel-2 and Landsat 8 in the estimation of boreal forest canopy cover and leaf area index

2017 ◽  
Vol 195 ◽  
pp. 259-274 ◽  
Author(s):  
Lauri Korhonen ◽  
Hadi ◽  
Petteri Packalen ◽  
Miina Rautiainen
Keyword(s):  
Boreal Forest ◽  
Leaf Area Index ◽  
Leaf Area ◽  
Forest Canopy ◽  
Canopy Cover ◽  
Landsat 8 ◽  
Area Index ◽  
Sentinel 2
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