Search results for "leaf area"
showing 10 items of 124 documents
Integrating Domain Knowledge in Data-Driven Earth Observation With Process Convolutions
2022
The modelling of Earth observation data is a challenging problem, typically approached by either purely mechanistic or purely data-driven methods. Mechanistic models encode the domain knowledge and physical rules governing the system. Such models, however, need the correct specification of all interactions between variables in the problem and the appropriate parameterization is a challenge in itself. On the other hand, machine learning approaches are flexible data-driven tools, able to approximate arbitrarily complex functions, but lack interpretability and struggle when data is scarce or in extrapolation regimes. In this paper, we argue that hybrid learning schemes that combine both approa…
Retrieval of Crop Variables from Proximal Multispectral UAV Image Data Using PROSAIL in Maize Canopy
2022
Mapping crop variables at different growth stages is crucial to inform farmers and plant breeders about the crop status. For mapping purposes, inversion of canopy radiative transfer models (RTMs) is a viable alternative to parametric and non-parametric regression models, which often lack transferability in time and space. Due to the physical nature of RTMs, inversion outputs can be delivered in sound physical units that reflect the underlying processes in the canopy. In this study, we explored the capabilities of the coupled leaf–canopy RTM PROSAIL applied to high-spatial-resolution (0.015 m) multispectral unmanned aerial vehicle (UAV) data to retrieve the leaf chlorophyll content (LC…
Soil Moisture Retrieved From a Combined Optical and Passive Microwave Approach
2016
Abstract With the current remote sensing technology developments, and in particular those at L-band (1.2–1.4 GHz) frequencies such as the Soil Moisture and Ocean Salinity and the Soil Moisture Active and Passive missions, new approaches concerning passive microwave and its combination with existing optical technologies have become of special interest for the estimation of surface soil moisture. One of these new approaches is the combination of optical and passive microwave data based on a semiempirical approach derived from the general radiative transfer equation. The objective of this chapter is to present some applications of the combined optical-passive microwave approaches over several …
Estimation of leaf area index using PROSAIL based LUT inversion, MLRA-GPR and empirical models: Case study of tropical deciduous forest plantation, N…
2020
Abstract Forests play a vital role in biological cycles and environmental regulation. To understand the key processes of forest canopies (e.g., photosynthesis, respiration and transpiration), reliable and accurate information on spatial variability of Leaf Area Index (LAI), and its seasonal dynamics is essential. In the present study, we assessed the performance of biophysical parameter (LAI) retrieval methods viz. Look-Up Table (LUT)-inversion, MLRA-GPR (Machine Learning Regression Algorithm- Gaussian Processes Regression) and empirical models, for estimating the LAI of tropical deciduous plantation using ARTMO (Automated Radiative Transfer Models Operator) tool and Sentinel-2 satellite im…
Brown and green LAI mapping through spectral indices
2015
Abstract When crops senescence, leaves remain until they fall off or are harvested. Hence, leaf area index (LAI) stays high even when chlorophyll content degrades to zero. Current LAI approaches from remote sensing techniques are not optimized for estimating LAI of senescent vegetation. In this paper a two-step approach has been proposed to realize simultaneous LAI mapping over green and senescent croplands. The first step separates green from brown LAI by means of a newly proposed index, ‘Green Brown Vegetation Index (GBVI)’. This index exploits two shortwave infrared (SWIR) spectral bands centred at 2100 and 2000 nm, which fall right in the dry matter absorption regions, thereby providing…
Estimating the phenological dynamics of irrigated rice leaf area index using the combination of PROSAIL and Gaussian Process Regression
2021
The growth of rice is a sequence of three different phenological phases. This sequence of change in rice phenology implies that the condition of the plant during the vegetative phase relates directly to the health of leaves functioning during the reproductive and ripening phases. As such, accurate monitoring is important towards understanding rice growth dynamics. Leaf Area Index (LAI) is an important indicator of rice yields and the availability of this information during key phenological phases can support more informed farming decisions. Satellite remote sensing has been adopted as a proxy to field measurements of LAI and with the launch of freely available high resolution Satellite imag…
Crop and irrigation water management using high resolution remote sensing and agrohydrological models
2006
A combined agrohydrological and remote sensing approach, called SIMODIS (Simulation and Management of On‐Demand Irrigation Systems) (D’Urso, 2001), has been used in a Sicilian test area to simulate the operation of on‐demand irrigation system. In SIMODIS the spatial distribution of crop factor, Kc, is directly calculated from canopy variables r (albedo), LAI (Leaf Area Index) and hc (crop height) derived from satellite‐based canopy spectral reflectance. Coupling these canopy variables with a specific data set of soil properties, the SIMODIS procedure was setup to simulate, in a distributed way, the water balance and, therefore, the irrigation deliveries for a set of 136 grape fields. For th…
Estimation of winter leaf area index and sky view fraction for snow modelling in boreal coniferous forests: consequences on snow mass and energy bala…
2012
Abstract in Undetermined Leaf area index (LAI) and canopy coverage are important parameters when modelling snow process in coniferous forests, controlling interception and transmitting radiation. Estimates of LAI and sky view factor show large variability depending on the estimation method used, and it is not clear how this is reflected in the calculated snow processes beneath the canopy. In this study, the winter LAI and sky view fraction were estimated using different optical and biomass-based approximations in several boreal coniferous forest stands in Fennoscandia with different stand density, age and site latitude. The biomass-based estimate of LAI derived from forest inventory data wa…
Responses of Prunus ferganensis, Prunus persica and two interspecific hybrids to moderate drought stress
2003
Prunus ferganensis (Kost. & Riab) Kov. & Kost, a close relative of the cultivated peach (Prunus persica (L.) Batsch.), is native to arid regions of central Asia and may possess traits valuable for improving drought tolerance of commercial peach varieties. One distinguishing feature of P. ferganensis is its prominent, elongated, unbranched leaf venation pattern, which behaves as a simple recessive trait in segregating populations of P. ferganensis x P. persica hybrids. To understand whether this trait could be used as a marker in breeding for drought tolerance, we investigated the association between leaf morphological and physiological parameters related to drought response in P. ferganensi…
Multi-Season Phenology Mapping of Nile Delta Croplands Using Time Series of Sentinel-2 and Landsat 8 Green LAI
2022
Space-based cropland phenology monitoring substantially assists agricultural managing practices and plays an important role in crop yield predictions. Multitemporal satellite observations allow analyzing vegetation seasonal dynamics over large areas by using vegetation indices or by deriving biophysical variables. The Nile Delta represents about half of all agricultural lands of Egypt. In this region, intensifying farming systems are predominant and multi-cropping rotations schemes are increasing, requiring a high temporal and spatial resolution monitoring for capturing successive crop growth cycles. This study presents a workflow for cropland phenology characterization and mapping based on…