scholarly journals Multifrequency and Full-Polarimetric SAR Assessment for Estimating Above Ground Biomass and Leaf Area Index in the Amazon Várzea Wetlands

2018 ◽  
Vol 10 (9) ◽  
pp. 1355 ◽  
Author(s):  
Luciana Pereira ◽  
Luiz Furtado ◽  
Evlyn Novo ◽  
Sidnei Sant’Anna ◽  
Veraldo Liesenberg ◽  
...  
Keyword(s):  
Leaf Area Index ◽  
Leaf Area ◽  
Floodplain Forest ◽  
Single Frequency ◽  
Above Ground Biomass ◽  
Alos Palsar ◽  
Area Index ◽  
Ground Biomass ◽  
L Band ◽  
Sar Data

The aim of this study is to evaluate the potential of multifrequency and Full-polarimetric Synthetic Aperture Radar (SAR) data for retrieving both Above Ground Biomass (AGB) and Leaf Area Index (LAI) in the Amazon floodplain forest environment. Two specific questions were proposed: (a) Does multifrequency SAR data perform more efficiently than single-frequency data in estimating LAI and AGB of várzea forests?; and (b) Are quad-pol SAR data more efficient than single- and dual-pol SAR data in estimating LAI and AGB of várzea forest? To answer these questions, data from different sources (TerraSAR-X Multi Look Ground Range Detected (MGD), Radarsat-2 Standard Qual-Pol, advanced land observing satellite (ALOS)/ phased-arrayed L-band SAR (PALSAR-1). Fine-beam dual (FDB) and quad Polarimetric mode) were combined in 10 different scenarios to model both LAI and AGB. A R-platform routine was implemented to automatize the selection of the best regression models. Results indicated that ALOS/PALSAR variables provided the best estimates for both LAI and AGB. Single-frequency L-band data was more efficient than multifrequency SAR. PALSAR-FDB HV-dB provided the best LAI estimates during low-water season. The best AGB estimates at high-water season were obtained by PALSAR-1 quad-polarimetric data. The top three features for estimating AGB were proportion of volumetric scattering and both the first and second dominant phase difference between trihedral and dihedral scattering, extracted from Van Zyl and Touzi decomposition, respectively. The models selected for both AGB and LAI were parsimonious. The Root Mean Squared Error (RMSEcv), relative overall RMSEcv (%) and R2 value for LAI were 0.61%, 0.55% and 13%, respectively, and for AGB, they were 74.6 t·ha−1, 0.88% and 46%, respectively. These results indicate that L-band (ALOS/PALSAR-1) has a high potential to provide quantitative and spatial information about structural forest attributes in floodplain forest environments. This potential may be extended not only with PALSAR-2 data but also to forthcoming missions (e.g., NISAR, Global Ecosystems Dynamics Investigation Lidar (GEDI), BIOMASS, Tandem-L) for promoting wall-to-wall AGB mapping with a high level of accuracy in dense tropical forest regions worldwide.

scholarly journals Assessment of Leaf Area Index Models Using Harmonized Landsat and Sentinel-2 Surface Reflectance Data over a Semi-Arid Irrigated Landscape

2020 ◽  
Vol 12 (19) ◽  
pp. 3121
Author(s):  
Roya Mourad ◽  
Hadi Jaafar ◽  
Martha Anderson ◽  
Feng Gao
Keyword(s):  
Linear Regression ◽  
Leaf Area Index ◽  
Leaf Area ◽  
Landsat 8 ◽  
Surface Reflectance ◽  
Above Ground Biomass ◽  
Area Index ◽  
Ground Biomass ◽  
Semi Arid ◽  
Sentinel 2

Leaf area index (LAI) is an essential indicator of crop development and growth. For many agricultural applications, satellite-based LAI estimates at the farm-level often require near-daily imagery at medium to high spatial resolution. The combination of data from different ongoing satellite missions, Sentinel 2 (ESA) and Landsat 8 (NASA), provides this opportunity. In this study, we evaluated the leaf area index generated from three methods, namely, existing vegetation index (VI) relationships applied to Harmonized Landsat-8 and Sentinel-2 (HLS) surface reflectance produced by NASA, the SNAP biophysical model, and the THEIA L2A surface reflectance products from Sentinel-2. The intercomparison was conducted over the agricultural scheme in Bekaa (Lebanon) using a large set of in-field LAIs and other biophysical measurements collected in a wide variety of canopy structures during the 2018 and 2019 growing seasons. The major studied crops include herbs (e.g., cannabis: Cannabis sativa, mint: Mentha, and others), potato (Solanum tuberosum), and vegetables (e.g., bean: Phaseolus vulgaris, cabbage: Brassica oleracea, carrot: Daucus carota subsp. sativus, and others). Additionally, crop-specific height and above-ground biomass relationships with LAIs were investigated. Results show that of the empirical VI relationships tested, the EVI2-based HLS models statistically performed the best, specifically, the LAI models originally developed for wheat (RMSE:1.27), maize (RMSE:1.34), and row crops (RMSE:1.38). LAI derived through European Space Agency’s (ESA) Sentinel Application Platform (SNAP) biophysical processor underestimated LAI and provided less accurate estimates (RMSE of 1.72). Additionally, the S2 SeLI LAI algorithm (from SNAP biophysical processor) produced an acceptable accuracy level compared to HLS-EVI2 models (RMSE of 1.38) but with significant underestimation at high LAI values. Our findings show that the LAI-VI relationship, in general, is crop-specific with both linear and non-linear regression forms. Among the examined indices, EVI2 outperformed other vegetation indices when all crops were combined, and therefore it can be identified as an index that is best suited for a unified algorithm for crops in semi-arid irrigated regions with heterogeneous landscapes. Furthermore, our analysis shows that the observed height-LAI relationship is crop-specific and essentially linear with an R2 value of 0.82 for potato, 0.79 for wheat, and 0.50 for both cannabis and tobacco. The ability of the linear regression to estimate the fresh and dry above-ground biomass of potato from both observed height and LAI was reasonable, yielding R2: ~0.60.

Leaf Area Index and Above-Ground Biomass of terra firme Rain Forest and Adjacent Clearings in Amazonia

1993 ◽  
Vol 7 (3) ◽  
pp. 310 ◽  
Author(s):  
A.-L. C. McWilliam ◽  
J. M. Roberts ◽  
O. M. R. Cabral ◽  
M. V. B. R. Leitao ◽  
A. C. L. de Costa ◽  
...  
Keyword(s):  
Leaf Area Index ◽  
Leaf Area ◽  
Rain Forest ◽  
Above Ground Biomass ◽  
Terra Firme ◽  
Area Index ◽  
Ground Biomass

scholarly journals (12) Influence of Single and Biculture Cover Crops with or without Synthetic Nitrogen on Leaf Area Index and Above Ground Biomass Yields of Sweet Corn

HortScience ◽  
2006 ◽  
Vol 41 (4) ◽  
pp. 1063B-1063
Author(s):  
Wayne F. Whitehead ◽  
Bharat P. Singh
Keyword(s):  
Leaf Area Index ◽  
Leaf Area ◽  
Cover Crops ◽  
Cover Crop ◽  
Sweet Corn ◽  
Dry Weight ◽  
Above Ground Biomass ◽  
Hairy Vetch ◽  
Area Index ◽  
Ground Biomass

During the 2004–05 growing season, a study was conducted to determine effect of cover crop, their mixture and fertilizer N rates on above ground biomass (AGB) yields, and Leaf Area Index (LAI) of Bt sweet corn. The following cover crop nitrogen fertility treatments were applied using randomized complete-block design with three replications: 1) fall-0 N, fallow; spring-0 N, 2) fall-0 N, abruzzi rye; spring-0 N, 3) fall-0 N, hairy vetch; spring-0 N, 4) fall-0 N, abruzzi rye+hairy vetch; spring-0 N, 5) fall-0 N, fallow; spring-101 kg N/ha, 6) fall-0 N, abruzzi rye; spring-101 kg N/ha, 7) fall-0 N, hairy vetch; spring-101 kg N/ha, 8) fall-0 N, abruzzi rye+hairy vetch; spring-101 kg N/ha, 9) fall-0 N, fallow; spring-202 kg N/ha, 10) fall-0 N, abruzzi rye; spring-202 kg N/ha, 11) fall-0 N, hairy vetch; spring-202 kg N/ha, and 12) fall-0 N, abruzzi rye+hairy vetch; spring-202 kg N/ha. In Spring 2005, `Attribute BSS0977' bi-color (BC) supersweet (sh2) corn seeds were field planted. AGB yields were collected during harvest week while LAI was recorded at tasseling (6/27), silking (7/8) and one week after harvest (7/25). Hairy vetch; spring-101 kg N/ha produced highest LAI at tasseling (2.18), silking (2.73), and one week after harvest (2.57). Lowest LAI at tasseling (1.12) and silking(1.60) were produced by abruzzi rye; spring-0 N with fallow; spring-0 N producing lowest LAI (1.40) one week after harvest. Maximum AGB fresh (40.5 Mg/ha) and dry weight (12.1 Mg/ha) yields were produced by hairy vetch; spring-101kg N/ha, while minimum AGB fresh (9.6 Mg/ha) and dry weight (3.6 Mg/ha) yields were produced by abruzzi rye; spring-0 N. Results imply LAI at each growth stage and AGB yields of this BCsh2 corn variety are best supported by hairy vetch supplemented with N at 101 kg/ha.

Wheat leaf area index retrieval using RISAT-1 hybrid polarized SAR data

2019 ◽  
Vol 35 (8) ◽  
pp. 905-915 ◽  
Author(s):  
Thota Sivasankar ◽  
Dheeraj Kumar ◽  
Hari Shanker Srivastava ◽  
Parul Patel
Keyword(s):  
Leaf Area Index ◽  
Leaf Area ◽  
Area Index ◽  
Wheat Leaf ◽  
Sar Data

Analysis of water dynamics in the soil-plant-atmosphere continuum using a multi-sensor approach

2020 ◽  
Author(s):  
David Chaparro ◽  
Thomas Jagdhuber ◽  
Dara Entekhabi ◽  
María Piles ◽  
Anke Fluhrer ◽  
...  
Keyword(s):  
Soil Moisture ◽  
Leaf Area Index ◽  
Water Content ◽  
Leaf Area ◽  
Optical Depth ◽  
Time Lag ◽  
Water Storage ◽  
Water Dynamics ◽  
Area Index ◽  
L Band

<p>Changing climate patterns have increased hydrological extremes in many regions [1]. This impacts water and carbon cycles, potentially modifying vegetation processes and thus terrestrial carbon uptake. It is therefore crucial to understand the relationship between the main water pools linked to vegetation (i.e., soil moisture, plant water storage, and atmospheric water deficit), and how vegetation responds to changes of these pools. Hence, the goal of this research is to understand the water pools and fluxes in the soil-plant-atmosphere continuum (SPAC) and their relationship with vegetation responses.</p><p>Our study spans from April 2015 to March 2019 and is structured in two parts:</p><p>Firstly, relative water content (RWC) is estimated using a multi-sensor approach to monitor water storage in plants. This is at the core of our research approach towards water pool monitoring within SPAC. Here, we will present a RWC dataset derived from gravimetric moisture content (<em>mg</em>) estimates using the method first proposed in [2], and further validated in [3]. This allows retrieving RWC and <em>mg</em> independently from biomass influences. Here, we apply this method using a sensor synergy including (i) vegetation optical depth from SMAP L-band radiometer (L-VOD), (ii) vegetation height (VH) from ICESat-2 Lidar and (iii) vegetation volume fraction (d) from AQUARIUS L-band radar. RWC status and temporal dynamics will be discussed.</p><p>Secondly, water dynamics in the SPAC and their impact on leaf changes are analyzed. We will present a global, time-lag correlation analysis among: (i) the developed RWC maps, (ii) surface soil moisture from SMAP (SM), (iii) vapor pressure deficit (VPD; from MERRA reanalysis [4]), and (iv) leaf area index (LAI; from MODIS [5]). Resulting time-lag and correlation maps, as well as analyses of LAI dynamics as a function of SPAC, will be presented at the conference.</p><p> </p><p>References</p><p>[1] IPCC. (2013). Annex I: Atlas of global and regional climate projections. In: van Oldenborgh, et al. (Eds.) Climate Change 2013: The Physical Science Basis (pp. 1311-1393). Cambridge University Press.</p><p>[2] Fink, A., et al. (2018). Estimating Gravimetric Moisture of Vegetation Using an Attenuation-Based Multi-Sensor Approach. In IGARSS 2018 (pp. 353-356). IEEE.</p><p>[3] Meyer, T., et al. Estimating Gravimetric Water Content of a Winter Wheat Field from L-Band Vegetation Optical Depth, Remote Sens. 2019, 11(20), 2353</p><p>[4] NASA (2019). Modern-Era Retrospective analysis for Research and Applications, Version 2. Accessed 2020-01-14 from https://gmao.gsfc.nasa.gov/reanalysis/MERRA-2/.</p><p>[5] Myneni, R., et al. (2015). MOD15A2H MODIS/Terra Leaf Area Index/FPAR 8-Day L4 Global 500m SIN Grid V006. Accessed 2020-01-14 from https://doi.org/10.5067/MODIS/MOD15A2H.006.</p>

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