Raul Zurita-Milla

A review on reflective remote sensing and data assimilation techniques for enhanced agroecosystem modeling

Abstract During the last 50 years, the management of agroecosystems has been undergoing major changes to meet the growing demand for food, timber, fibre and fuel. As a result of this intensified use, the ecological status of many agroecosystems has been severely deteriorated. Modeling the behavior of agroecosystems is, therefore, of great help since it allows the definition of management strategies that maximize (crop) production while minimizing the environmental impacts. Remote sensing can support such modeling by offering information on the spatial and temporal variation of important canopy state variables which would be very difficult to obtain otherwise. In this paper, we present an overview of different methods that can be used to derive biophysical and biochemical canopy state variables from optical remote sensing data in the VNIR-SWIR regions. The overview is based on an extensive literature review where both statistical–empirical and physically based methods are discussed. Subsequently, the prevailing techniques of assimilating remote sensing data into agroecosystem models are outlined. The increasing complexity of data assimilation methods and of models describing agroecosystem functioning has significantly increased computational demands. For this reason, we include a short section on the potential of parallel processing to deal with the complex and computationally intensive algorithms described in the preceding sections. The studied literature reveals that many valuable techniques have been developed both for the retrieval of canopy state variables from reflective remote sensing data as for assimilating the retrieved variables in agroecosystem models. However, for agroecosystem modeling and remote sensing data assimilation to be commonly employed on a global operational basis, emphasis will have to be put on bridging the mismatch between data availability and accuracy on one hand, and model and user requirements on the other. This could be achieved by integrating imagery with different spatial, temporal, spectral, and angular resolutions, and the fusion of optical data with data of different origin, such as LIDAR and radar/microwave.

Unmixing-Based Landsat TM and MERIS FR Data Fusion

An unmixing-based data fusion technique is used to generate images that have the spatial resolution of Landsat Thematic Mapper (TM) and the spectral resolution provided by the Medium Resolution Imaging Spectrometer (MERIS) sensor. The method requires the optimization of the following two parameters: the number of classes used to classify the TM image and the size of the MERIS ldquowindowrdquo (neighborhood) used to solve the unmixing equations. The ERGAS index is used to assess the quality of the fused images at the TM and MERIS spatial resolutions and to assist with the identification of the best combination of the two parameters that need to be optimized. Results indicate that it is possible to successfully downscale MERIS full resolution data to a Landsat-like spatial resolution while preserving the MERIS spectral resolution.

Applicability of the PROSPECT model for Norway spruce needles

The potential applicability of the leaf radiative transfer model PROSPECT (version 3.01) was tested for Norway spruce (Picea abies (L.) Karst.) needles collected from stress resistant and resilient trees. Direct comparison of the measured and simulated leaf optical properties between 450–1000 nm revealed the requirement to recalibrate the PROSPECT chlorophyll and dry matter specific absorption coefficients k ab(λ) and k m(λ). The subsequent validation of the modified PROSPECT (version 3.01.S) showed close agreement with the spectral measurements of all three needle age‐classes tested; the root mean square error (RMSE) of all reflectance (ρ) values within the interval of 450–1000 nm was equal to 1.74%, for transmittance (τ) it was 1.53% and for absorbance (α) it was 2.91%. The total chlorophyll concentration, dry matter content, and leaf water content were simultaneously retrieved by a constrained inversion of the original PROSPECT 3.01 and the adjusted PROSPECT 3.01.S. The chlorophyll concentration estimated by inversion of both model versions was similar, but the inversion accuracy of the dry matter and water content was significantly improved. Decreases in RMSE from 0.0079 g cm−2 to 0.0019 g cm−2 for dry matter and from 0.0019 cm to 0.0006 cm for leaf water content proved the improved performance of the recalibrated PROSPECT version 3.01.S.

Using MERIS on Envisat for land cover mapping in the Netherlands

This paper describes the results of a feasibility study to test the usefulness of MERIS for land cover mapping. The Netherlands was used as a test site because of its highly fragmented landscape. Results showed that the geometric and radiometric properties of the studied MERIS images of the Netherlands are suitable for land applications. Calculation of principal components and correlation coefficients revealed that the 15 MERIS bands provided a lot of redundant spectral information. For land applications, information came from the visible part of the spectrum on the one hand and from the near‐infrared part on the other hand. In addition, the red‐edge slope of the reflectance curve (in particular MERIS band 9 at about 708 nm) provided supplementary information. The Dutch land use database LGN5 was used as a reference for classifications in this study after aggregation from 25 m to 300 m and recoding to 7 relevant land cover classes. For land cover classification best results in terms of classification accuracies were obtained for the image of 14 July 2003. For the seven land cover classes selected the overall classification accuracy was 67.2%. A multitemporal classification did not improve the overall classification accuracy.

Effects of MERIS L1b radiometric calibration on regional land cover mapping and land products

The information derived from remotely sensed data must be carefully used because there are many sources of error that potentially affect its quality. In this respect, an accurate full calibration process of any sensor is essential because it can minimise various uncertainties and errors that reduce the final quality of the products derived from remotely sensed data. This paper presents several calibration efforts performed on MERIS data and subsequently focuses on the smile effect as well as on the vicarious calibration corrections. The implications of these corrections are evaluated using a MERIS full resolution level 1b image that was acquired over The Netherlands. A thematic approach, based on regional land cover mapping using linear spectral unmixing, and a continuous approach, based on continuous variables (FAPAR, MTCI and NDVI), are used to quantify these implications. Even though MERIS has a very high radiometric quality, results point out that these radiometric effects are consistently present in the final MERIS products. We also conclude that MERIS, after including all potential corrections investigated here, does not exhibit significant (radiometric) deficiencies. However, from a strict point of view, all the proposed radiometric corrections should be applied to the data so that the retrieval of quantitative information can be done with the highest possible quality. The use of fully radiometrically corrected data will also facilitate multitemporal comparisons. Therefore, we finally conclude that a systematic application of all relevant calibration parameters will increase the long term comparability of MERIS measurements in such a way that more emphasis can be put on the retrieval of MERIS products.

Using MERIS fused images for land-cover mapping and vegetation status assessment in heterogeneous landscapes

In this paper we evaluate the potential of ENVISAT–Medium Resolution Imaging Spectrometer (MERIS) fused images for land-cover mapping and vegetation status assessment in heterogeneous landscapes. A series of MERIS fused images (15 spectral bands; 25 m pixel size) is created using the linear mixing model and a Landsat Thematic Mapper (TM) image acquired over the Netherlands. First, the fused images are classified to produce a map of the eight main land-cover types of the Netherlands. Subsequently, the maps are validated using the Dutch land-cover/land-use database as a reference. Then, the fused image with the highest overall classification accuracy is selected as the best fused image. Finally, the best fused image is used to compute three vegetation indices: the normalized difference vegetation index (NDVI) and two indices specifically designed to monitor vegetation status using MERIS data: the MERIS terrestrial chlorophyll index (MTCI) and the MERIS global vegetation index (MGVI). Results indicate that the selected data fusion approach is able to downscale MERIS data to a Landsat-like spatial resolution. The spectral information in the fused images originates fully from MERIS and is not influenced by the TM data. Classification results for the TM and for the best fused image are similar and, when comparing spectrally similar images (i.e. TM with no short-wave infrared bands), the results of the fused image outperform those of TM. With respect to the vegetation indices, a good correlation was found between the NDVI computed from TM and from the best fused image (in spite of the spectral differences between these two sensors). In addition, results show the potential of using MERIS vegetation indices computed from fused images to monitor individual fields. This is not possible using the original MERIS full resolution image. Therefore, we conclude that MERIS–TM fused images are very useful to map heterogeneous landscapes.

Multisensor and multiresolution image fusion using the linear mixing model

In satellite sensor design we observe a trade-off between sensors with a high spatial res-olution having only a few spectral bands and a low revisit frequency on the one hand,and sensors with a medium to low spatial resolution having many spectral bands and ahigh revisit time on the other hand. Many applications require a combination of a highspatial, spectral, and temporal resolution. In this chapter image fusion of a high spatialresolution (Landsat Thematic Mapper) and a high spectral resolution (Envisat MERIS)image based on the linear mixing model is presented. This approach is also known as spa-tial unmixing or unmixing-based data fusion. An optimisation of the number of classesused to classify the high spatial resolution image and the size of the neighbourhood, forwhich the unmixing equations are solved, is presented. It is illustrated for a test area inthe Netherlands. Results show the feasibility of this approach yielding fused images withthe spatial resolution of the high resolution image and with the spectral information fromthe low spatial resolution image. The quality of the fused images is evaluated using thespectral and spatial ERGAS index. Main advantage of the presented technique based onthe linear mixing model is that the fused images do not include the spectral informationof the high spatial resolution image in the x93nal result in any way.3.1 IntroductionTerrestrialvegetationplaysanimportantroleinbiochemicalcycles,liketheglobalcarboncycle.Informationonthetypeofvegetationisimportantforsuchstudies.Thisisthematicinformation referring to the biome type or the land cover type. In addition, once we havethethematicclassinformation,weneedquantitativeinformationonvegetationproperties.

Physically-based retrievals of Norway spruce canopy variables from very high spatial resolution hyperspectral data

This study was conducted to answer two research questions: (1) what is the spatial variability of the leaf optical properties between 400-1600 nm (hemispherical-directional reflectance, transmittance, absorption) within young Norway spruce crowns, and (2) how to design a suitable physically-based approach retrieving the total chlorophyll content of a complex coniferous canopy from very high spatial resolution (0.4 m) hyperspectral data? It was proved that sun-exposed needles of current age-class statistically differ (alpha-level = 0.01) from rest of the needles in reflectance between 510-760 nm. Last four age-classes of sun-exposed needles were also found to be significantly different from almost all age-classes of sun-shaded needles in transmittance from 760-1350 nm. An operational estimation of chlorophyll a+b content (Cab) from an airborne AISA Eagle hyperspectral image was proposed by means of a PROSPECT-DART inversion employing an artificial neural network (ANN). A spatial pattern of estimated Cab was successfully validated against the Cab map produced by a vegetation index ANCB650-720. Coefficients of determination (R2) between ground measured and retrieved Cab were 0.81 and 0.83, respectively, with root mean square errors (RMSE) of 2.72 mug cm-2 for ANN and 3.27 mug cm-2 for ANCB650-720.

Assessment of long-term vicarious calibration efforts of MERIS on land product quality

Since the launch of MERIS on ENVISAT long term activities using vicarious calibration approaches are set in place to monitor potential drifts in calibration in the radiance products of MERIS. We are using a stable, well monitored reference calibration site (Railroad Valley, Nevada, USA) to derive calibration uncertainties of MERIS over time. We are using interpolation of uncertainties to derive a second set of uncertainties for a national data validation in the Netherlands. A satellite image derived land use map of the Netherlands (LGN4) is used to determine the largest homogeneous land use classes using a standard purity index (SPI). Potential adjacency effects are minimized using moving window filters on the pixels of the aggregated map. Multiple error propagation is being used to assess the impact of calibration accuracy on land use classification. A classification in 9 land use classes is finally performed on MERIS FR images of the Netherlands using image based spectral unmixing and matched filtering with endmembers derived from the LGN. We conclude that the classification performance may significantly be increased, when taking into account long-term vicarious calibration results.

Possibilities of MERIS for sub-pixel regional land cover mapping

The Medium Resolution Imaging Spectrometer, MERIS, on board of ENVISAT-1 fulfils the information gap between the current high and low spatial resolution sensors. In this respect, the use of MERIS full resolution data (300 m pixel size) has a great potential for regional and global land cover mapping. However, the spectral and temporal resolutions of MERIS (15 narrow bands and a revisit time of 2-3 days, respectively) might be further exploited in order to get land cover information at a more detailed scale. The performance of MERIS for extracting sub-pixel land cover information was evaluated in this study. An iterative linear spectral unmixing method designed to optimize the number of endmembers per pixel was used to classify 2 MERIS full resolution images acquired over The Netherlands. The latest version of the Dutch land use database, the LGN5, was used as a reference dataset both for the validation and for the selection of the endmembers. This dataset was first thematically aggregated to the main 9 land cover types and then spatially aggregated from its original 25m to 300m. Because the fractions of the different land cover types present in each MERIS pixel were computed during the aggregation, a sub-pixel accuracy assessment could be done (in addition to the traditional assessment based on a hard classification). Results pointed out that MERIS has a great potential for providing sub-pixel land cover information because the classification accuracies were up to 60%. The correct number of endmembers to unmix every pixel was adequately identified by the iterative linear spectral unmixing. Future research efforts should be put in making use of the high revisit time of the MERIS sensor (temporal unmixing).