Biomass estimation based on hyperspectral and SAR data: an experimental study in South Tyrol, Italy

Author(s):  
Eugenia Chiarito ◽  
Francesca Cigna ◽  
Giovanni Cuozzo ◽  
Ludovica De Gregorio ◽  
Giacomo Fontanelli ◽  
...  

<p>Grasslands cover almost one third of the world’s terrestrial surface. In Alpine environments grassland vegetation fulfills various key environmental purposes such as water reservoir, slope stabilizer and carbon sink or fodder for livestock. At the same time Alpine regions are more affected by climatic changes than other geographic zones, potentially resulting in earlier green-up phases or an elevated exposure to drought events, hampering the growth and vitality of grassland vegetation. The scope of this study is to build an algorithm capable of biomass estimation using Support Vector Machine approach on hyperspectral and Synthetic Aperture Radar (SAR) data. To that purpose, field campaigns were carried out during 2017 and 2019 in Val Mazia (South Tyrol, Italy), where hyperspectral spectroradiometer samples were collected, as well as leaf area index (LAI), soil moisture, and above ground biomass measurements. Copernicus Sentinel-1 IW SAR backscattering data were used to complete the dataset.</p><p>The spectroradiometer was used to simulate the hyperspectral data of the Italian Space Agency (ASI)’s PRISMA mission, launched on 22 March 2019. Since the number of bands is larger than the number of samples, a prediction approach based on machine learning risks to model noise. The following two solutions were tested and compared: (i) the number of bands was reduced by resampling the data to match specifications of Copernicus Sentinel-2 Multispectral Instrument (MSI), and (ii) the data was simulated using the PROSPECT model, increasing the sample size.</p><p>In the first case correlation R<sup>2</sup> of 0.37 was found. Discrepancies were observed for high biomass values, which could be explained by the small number of samples available shortly before harvest. To mitigate this effect, data were simulated for high biomass based on field average values and standard deviation within each date. R<sup>2</sup> increased to 0.71 in this case, confirming the above mentioned hypothesis regarding the dataset representativeness.</p><p>In the case of PROSPECT model, the parameters were found by iterating each one within ranges defined in the bibliography, until the spectral signatures matched the field observations. The resulting parameters were the input for data simulation. A genetic algorithm feature selection was run to reduce the number of features, discarding those with little or redundant information followed by an SVR model applied to the most sensitive bands resulting in an R<sup>2</sup> of 0.53. These initial results will be used as a basis for future investigations to improve the prediction model, for example by extending the dataset with new field campaigns, including more simulated data at biomass peak, as made with Sentinel-2 resampled dataset, or by adding further input variables, such as leaf area index. Furthermore, the procedure will be performed for fresh biomass and water content estimations.</p><p>The results obtained pave the way for future implementation of the tested algorithms on PRISMA hyperspectral and COSMO-SkyMed X-band SAR data in the future.</p><p>This research is part of the ongoing project ‘Development of algorithms for estimation and monitoring of hydrological parameters from satellite and drone’, funded by ASI under grant agreement n.2018-37-HH.0.</p>

Land ◽  
2021 ◽  
Vol 10 (5) ◽  
pp. 505
Author(s):  
Gregoriy Kaplan ◽  
Offer Rozenstein

Satellite remote sensing is a useful tool for estimating crop variables, particularly Leaf Area Index (LAI), which plays a pivotal role in monitoring crop development. The goal of this study was to identify the optimal Sentinel-2 bands for LAI estimation and to derive Vegetation Indices (VI) that are well correlated with LAI. Linear regression models between time series of Sentinel-2 imagery and field-measured LAI showed that Sentinel-2 Band-8A—Narrow Near InfraRed (NIR) is more accurate for LAI estimation than the traditionally used Band-8 (NIR). Band-5 (Red edge-1) showed the lowest performance out of all red edge bands in tomato and cotton. A novel finding was that Band 9 (Water vapor) showed a very high correlation with LAI. Bands 1, 2, 3, 4, 5, 11, and 12 were saturated at LAI ≈ 3 in cotton and tomato. Bands 6, 7, 8, 8A, and 9 were not saturated at high LAI values in cotton and tomato. The tomato, cotton, and wheat LAI estimation performance of ReNDVI (R2 = 0.79, 0.98, 0.83, respectively) and two new VIs (WEVI (Water vapor red Edge Vegetation Index) (R2 = 0.81, 0.96, 0.71, respectively) and WNEVI (Water vapor narrow NIR red Edge Vegetation index) (R2 = 0.79, 0.98, 0.79, respectively)) were higher than the LAI estimation performance of the commonly used NDVI (R2 = 0.66, 0.83, 0.05, respectively) and other common VIs tested in this study. Consequently, reNDVI, WEVI, and WNEVI can facilitate more accurate agricultural monitoring than traditional VIs.


2018 ◽  
Vol 10 (5) ◽  
pp. 763 ◽  
Author(s):  
Manuel Campos-Taberner ◽  
Francisco García-Haro ◽  
Lorenzo Busetto ◽  
Luigi Ranghetti ◽  
Beatriz Martínez ◽  
...  

Author(s):  
Katarzyna Dabrowska-Zielinska ◽  
Maciej Bartold ◽  
Radoslaw Gurdak ◽  
Martyna Gatkowska ◽  
Wojciech Kiryla ◽  
...  

2019 ◽  
Vol 154 ◽  
pp. 189-201 ◽  
Author(s):  
Jie Wang ◽  
Xiangming Xiao ◽  
Rajen Bajgain ◽  
Patrick Starks ◽  
Jean Steiner ◽  
...  

2019 ◽  
Vol 35 (8) ◽  
pp. 905-915 ◽  
Author(s):  
Thota Sivasankar ◽  
Dheeraj Kumar ◽  
Hari Shanker Srivastava ◽  
Parul Patel

2020 ◽  
Vol 57 (7) ◽  
pp. 943-964
Author(s):  
Aleksi Räsänen ◽  
Sari Juutinen ◽  
Margaret Kalacska ◽  
Mika Aurela ◽  
Pauli Heikkinen ◽  
...  

2020 ◽  
Vol 12 (11) ◽  
pp. 1843 ◽  
Author(s):  
Andrew Revill ◽  
Anna Florence ◽  
Alasdair MacArthur ◽  
Stephen Hoad ◽  
Robert Rees ◽  
...  

Leaf area index (LAI) estimates can inform decision-making in crop management. The European Space Agency’s Sentinel-2 satellite, with observations in the red-edge spectral region, can monitor crops globally at sub-field spatial resolutions (10–20 m). However, satellite LAI estimates require calibration with ground measurements. Calibration is challenged by spatial heterogeneity and scale mismatches between field and satellite measurements. Unmanned Aerial Vehicles (UAVs), generating high-resolution (cm-scale) LAI estimates, provide intermediary observations that we use here to characterise uncertainty and reduce spatial scaling discrepancies between Sentinel-2 observations and field surveys. We use a novel UAV multispectral sensor that matches Sentinel-2 spectral bands, flown in conjunction with LAI ground measurements. UAV and field surveys were conducted on multiple dates—coinciding with different wheat growth stages—that corresponded to Sentinel-2 overpasses. We compared chlorophyll red-edge index (CIred-edge) maps, derived from the Sentinel-2 and UAV platforms. We used Gaussian processes regression machine learning to calibrate a UAV model for LAI, based on ground data. Using the UAV LAI, we evaluated a two-stage calibration approach for generating robust LAI estimates from Sentinel-2. The agreement between Sentinel-2 and UAV CIred-edge values increased with growth stage—R2 ranged from 0.32 (stem elongation) to 0.75 (milk development). The CIred-edge variance between the two platforms was more comparable later in the growing season due to a more homogeneous and closed wheat canopy. The single-stage Sentinel-2 LAI calibration (i.e., direct calibration from ground measurements) performed poorly (mean R2 = 0.29, mean NRMSE = 17%) when compared to the two-stage calibration using the UAV data (mean R2 = 0.88, mean NRMSE = 8%). The two-stage approach reduced both errors and biases by >50%. By upscaling ground measurements and providing more representative model training samples, UAV observations provide an effective and viable means of enhancing Sentinel-2 wheat LAI retrievals. We anticipate that our UAV calibration approach to resolving spatial heterogeneity would enhance the retrieval accuracy of LAI and additional biophysical variables for other arable crop types and a broader range of vegetation cover types.


2020 ◽  
Vol 58 (2) ◽  
pp. 826-840 ◽  
Author(s):  
Yuanheng Sun ◽  
Qiming Qin ◽  
Huazhong Ren ◽  
Tianyuan Zhang ◽  
Shanshan Chen

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