María Amparo Gilabert

Linear spectral mixture modelling to estimate vegetation amount from optical spectral data

Abstract Spectral mixture modelling has developed in recent years as a suitable remote sensing tool for analysing the biophysical and compositional character of ground surfaces. In this paper the potentiality of the linear spectral mixture model to extract vegetation related parameters from 0·4-2·5 μm reflectance data has been tested. High spectral resolution reflectance measurements of soil-plant mixtures with different soil colour and plant densities were carried out in a laboratory experiment. The constrained least-squares and the factor analysis unmixing procedures were applied to generate endmember fractions of the components present in the mixtures and to test the validity of the model. It is concluded that the derived fraction of the vegetation endmember is less sensitive to soil background than the NDV[. The accuracy attainable by this modelling approach can be considered sufficient for many practical purposes, being operational in the monitoring of vegetation from satellite data.

An atmospheric correction method for the automatic retrieval of surface reflectances from TM images

Abstract Most of the atmospheric correction methods proposed in the literature are not easily applicable in reaJ cases. The most sophisticated models frequently require inputs which are not commonly available, whilst traditional simple dark object subtraction techniques do not generally give real reflectance values. In the present work an atmospheric correction method applicable to Landsat-TM data is described, which requires only inputs that are commonly available and the presence in the imaged scenes of some dark surfaces in TM bands 1 (blue) and 3 (red). The method consists of an inversion algorithm based on a simplified radiative transfer model in which the characteristics of atmospheric aerosols are estimated by the use of the path radiance in two TM bands rather than a priori assumed. On the basis of this information, which is crucial for determining the atmospheric properties, the retrieval of real reflectances from TM images is possible. The method can be applied to all TM scenes in which some dar...

Analyses of spectral-biophysical relationships for a corn canopy☆

Abstract The empirical study reported in this article focused on the relationships between vegetation properties and reflectance measured on a corn canopy throughout its phenological evolution by means of a truck-mounted speetroradiometer. The objective was to produce relationships between spectral indices [such as normalized difference vegetation index (NDVI) and the wavelength of the red edge] and biophysical parameters, such as leaf area index (LAI) and biomass, which should be useful for corn studies from remotely sensed data. The NDVI and the red-edge position were both found useful to describe some phenological stages for a corn canopy, because they both correlated statistically significantly with biophysical parameters, such as LAI and biomass. Coefficients of determination (r2) for the various relationships ranged from 0.94 to 0.98: however, leaf area index could be best estimated from NDVI by exponential equations, and biomass from the wavelength of the red edge by logarithmic equations.

Use of NOAA-AVHRR NDVI data for environmental monitoring and crop forecasting in the Sahel. Preliminary results

Abstract Several studies have shown that the NDVI calculated from NOAA-AVHRR data is related to annual rainfall and primary productivity in Sahelian areas. Such correlations, however, are affected by several environmental factors and have been tested only with data accumulated during rainy seasons, which is not ideal for the prediction of crop yield. In the present study a methodology of NOAA AVHRR data processing is presented which utilizes NDVI computed only in the first part of some rainy seasons and statistically takes into account the geographical variability in land resources and atmospheric conditions. From the first results of the application of the methodology in Niger, its potential has been shown both for environmental monitoring, and, more specifically, for crop yield assessment and forecasting.

Environmental monitoring and crop forecasting in the Sahel through the use of NOAA NDVI data. A case study: Niger 1986–89

Abstract Several investigations have shown that NOAA NDVI data accumulated during a rainy season can be related to total rainfall or final primary productivity in the Sahel. However, serious problems can arise when looking for quantitative relations to monitor and forecast crop yield from NDVI values. Geographical variability can affect such relations, while the use of data taken from a whole season is impractical for forecasting. The present paper proposes a complete methodology of NDVI data processing which only utilizes NOAA AVHRR scenes from the first part of successive rainy seasons. A series of basic corrections are first applied to the original data to obtain reliable NDVI maximum value composites at the middle of the rainy seasons considered. Next, the variability in land resources is accounted for by means of a standardization process which normalizes the mean NDVI levels of some areas on the relevant multi-temporal averages and standard deviations. In this way, good estimates of the actual condi...

Weathering process effects on spectral reflectance of rocks in a semi-arid environment

Spectral properties of rocks are mainly dependent on their mineralogical composition, which produces characteristic absorption features in different wavelength regions. This can be considered as a tool to recognise and discriminate different lithological units of an area by remotely-sensed data. Nevertheless, physical and chemical natural processes produce changes that modify to a considerable extent the mineralogical composition of the rock surface (weathered surface) which mask some of the spectral properties of the original surface (fresh surface). In the present study, various rock types (gypsum, carbonate, sandstone, lamproites, phyllite, and quartzite) were selected from a semi-arid region (SE Spain), pilot zone for MEDALUS Project, and their bidirectional reflectance factors were measured under laboratory conditions over the spectral region between 400 and 2500 nm. The study reveals that reflectance differences between the fresh and weathered surfaces (in brightness and presence of characteristic a...

Multitemporal Monitoring of Plant Area Index in the Valencia Rice District with PocketLAI

Leaf area index (LAI) is a key biophysical parameter used to determine foliage cover and crop growth in environmental studies in order to assess crop yield. Frequently, plant canopy analyzers (LAI-2000) and digital cameras for hemispherical photography (DHP) are used for indirect effective plant area index (PAIeff) estimates. Nevertheless, these instruments are expensive and have the disadvantages of low portability and maintenance. Recently, a smartphone app called PocketLAI was presented and tested for acquiring PAIeff measurements. It was used during an entire rice season for indirect PAIeff estimations and for deriving reference high-resolution PAIeff maps. Ground PAIeff values acquired with PocketLAI, LAI-2000, and DHP were well correlated (R2 = 0.95, RMSE = 0.21 m2/m2 for Licor-2000, and R2 = 0.94, RMSE = 0.6 m2/m2 for DHP). Complementary data such as phenology and leaf chlorophyll content were acquired to complement seasonal rice plant information provided by PAIeff. High-resolution PAIeff maps, which can be used for the validation of remote sensing products, have been derived using a global transfer function (TF) made of several measuring dates and their associated satellite radiances.

Noise Reduction and Gap Filling of fAPAR Time Series Using an Adapted Local Regression Filter

Time series of remotely sensed data are an important source of information for understanding land cover dynamics. In particular, the fraction of absorbed photosynthetic active radiation (fAPAR) is a key variable in the assessment of vegetation primary production over time. However, the fAPAR series derived from polar orbit satellites are not continuous and consistent in space and time. Filtering methods are thus required to fill in gaps and produce high-quality time series. This study proposes an adapted (iteratively reweighted) local regression filter (LOESS) and performs a benchmarking intercomparison with four popular and generally applicable smoothing methods: Double Logistic (DLOG), smoothing spline (SSP), Interpolation for Data Reconstruction (IDR) and adaptive Savitzky-Golay (ASG). This paper evaluates the main advantages and drawbacks of the considered techniques. The results have shown that ASG and the adapted LOESS perform better in recovering fAPAR time series over multiple controlled noisy scenarios. Both methods can robustly reconstruct the fAPAR trajectories, reducing the noise up to 80% in the worst simulation scenario, which might be attributed to the quality control (QC) MODIS information incorporated into these filtering algorithms, their flexibility and adaptation to the upper envelope. The adapted LOESS is particularly resistant to outliers. This method clearly outperforms the other considered methods to deal with the high presence of gaps and noise in satellite data records. The low RMSE and biases obtained with the LOESS method (|rMBE| < 8%; rRMSE < 20%) reveals an optimal reconstruction even in most extreme situations with long seasonal gaps. An example of application of the LOESS method to fill in invalid values in real MODIS images presenting persistent cloud and snow coverage is also shown. The LOESS approach is recommended in most remote sensing applications, such as gap-filling, cloud-replacement, and observing temporal dynamics in situ where rapid seasonal changes are produced.

Mapping Leaf Area Index With a Smartphone and Gaussian Processes

Leaf area index (LAI) is a key biophysical parameter used to determine foliage cover and crop growth in environmental studies. Smartphones are nowadays ubiquitous sensor devices with high computational power, moderate cost, and high-quality sensors. A smartphone app, which is called PocketLAI, was recently presented and tested for acquiring ground LAI estimates. In this letter, we explore the use of state-of-the-art nonlinear Gaussian process regression (GPR) to derive spatially explicit LAI estimates over rice using ground data from PocketLAI and Landsat 8 imagery. GPR has gained popularity in recent years because of its solid Bayesian foundations that offer not only high accuracy but also confidence intervals for the retrievals. We show the first LAI maps obtained with ground data from a smartphone combined with advanced machine learning. This letter compares LAI predictions and confidence intervals of the retrievals obtained with PocketLAI with those obtained with classical instruments, such as digital hemispheric photography (DHP) and LI-COR LAI-2000. This letter shows that all three instruments obtained comparable results, but PocketLAI is far cheaper. The proposed methodology hence opens a wide range of possible applications at moderate cost.

Estimation of crop coefficients by means of optimized vegetation indices for corn

A linear relationship between NDVI and basal crop coefficient (Kcb) allows to compute the spectral crop coefficient (Krcb). Due to the influence of soil variations varying surface humidity on NDVI, five soil optimized indices have been used to obtain a linear relationship normalized for soil background effect (SAVI, OSAVI, TSAVI, MSAVI and GESAVI). Data used on this work have been obtained from a field campaign for corn in the area of Barrax (Spain), describing crop growth stages with green fraction cover (GFC), and leaf area index (LAI). SAVI with optimized factor L set to 0.5 is a good estimator of Krcb from sparse to dense vegetation, nevertheless the soil line based index ( GESAVI) due to a wider range of variation are more sensitive to leaf variations at high levels of vegetation amount. Spectral crop coefficients obtained from SAVI and soil line based GESAVI are sensitive to crop hazards by weather anomalies and estimates in real time the basal crop coefficients to estimate the amount of water removed by the crop from the active root zone.