Synergy between SMOS-MIRAS and Landsat-OLI/TIRS Data for Soil Moisture Mapping before, during, and after Flash-Flood Storm in Southwestern Morocco

This chapter deals with soil moisture (SM) characterization over the Guelmim city and its neighborhood in the Southwestern Morocco that has been flooded several times over the past 50 years. To achieve this, space-borne SMOS and Landsat-8 OLI/TIRS data were preprocessed to correct several radiometric anomalies, and they were used. The SMOS brightness temperature data acquired before, during, and after the storm with 1-day temporal resolution and coarse spatial resolution (25 km) were transformed to the SM maps. OLI and TIRS data with moderate spatial and temporal resolutions were converted to Normalized Difference Vegetation Index (NDVI) and Land Surface Temperature (LST) to retrieve the Soil Moisture Index (SMI) maps. The results obtained were analyzed, intercompared, and validated against the compiled SM values from rainfall database (SM-RFE) delivered by NOAA climate prediction center Rainfall Estimator (RFE) for Africa. SMOS results show how the spatial variation of SM changes extremely at the regional scale before, during, and after the flash flood day-to-day. The SMI results converge toward the same conclusions showing a drastic SM change before and after flash flood highlighting the impact of inundation and the mud accumulation. By reference to the measured SM-RFE datasets, the validation of the derived SM maps exhibits a significant correlation (R2 ≥ 0.89). Globally, we observe a good complementarity among the considered data sources and processing methods for SM spatial information extraction, and the potential of their integration for the development of a prediction and monitoring model for flash flooding at the regional and local scales

La télédétection et les indices de végétation pour la détection de la végétation éparse et moyennement dense cas de l'environnement urbain

The specific objectives of this thesis are twofold. First, our goal is to develop a vegetation index which characterizes sparse and moderately dense vegetation covers, independently from exterior physical disturbances namely: the effect of soil optical properties, i.e. color and brightness, related to the heterogeneity and specificities of this environment, the disturbances introduced by the atmosphere which are variable through time and space and, the effect of spatial and spectral resolutions specific to each sensor. These factors control the interaction processes between the electromagnetic radiation, the atmosphere, the vegetation cover and the underlying soil and, consequently introduce quite severe limitations for the detection of vegetation covers using vegetation indices. Secondly, we evaluate the contribution of the vegetation index to classification precision for thematic mapping applications. For this purpose, we carried out our analyses based on ground-based spectroradiometric data, narrow spatial (7 m) and spectral (30 nm) airborne dam (MEIS-II) and other wide spatial and spectral resolution satellite (TM) data. The study of the sensitivity of vegetation indices to atmospheric disturbances was carried out using the H5S radiative transfer model. As to the analysis of the contribution of the vegetation index to classification precision, we used the maximum likelihood algorithm, and verified the precision by means of the Kappa coefficient. In order to study the spectral properties of bare soils on vegetation covers, we propose a radiative transfer model which permits to decompose the resulting reflectance measured at ground level over a"soil-vegetation cover" mixture into two principal components: the first is intrinsect to the vegetation cover and the second, characteristic of the underlying bare soil, is transmitted through the vegetation cover. The results of the ground simulations for different rates of vegetation cover and different soil colors and brightnesses demonstrate the performance of the proposed model for enhancing the effect of soil optical properties on individual spectral reflectances and consequently, on vegetation indices. The analysis of the results based on the ground measurements, the airborne or satellite data and the simulations of the H5S atmospheric model show that the vegetation indices converge towards the same conclusions and demonstrate that none of the indices remains stable and independent in relation to overall exterior effects. However, the TSAVI and ARVI indices are distinct from the others by their complementary characteristics. Based on the individual performances of these two indices, we propose a new vegetation index: the TSARVI (Transformed Soil Atmospherically Resistant Vegetation Index). This new index has the advantage of adequately describing sparse ar moderately sparse vegetation independently from soil effects, the atmosphere and sensor characteristics."-- Résumé abrégé par UMI

Spatial variability mapping of crop residue using hyperion (EO-1) hyperspectral data

Sherpa Romeo green journal; open accessSoil management practices that maintain crop residue cover and reduce tillage improve soil structure, increase organic matter content in the soil, positively influence water infiltration, evaporation and soil temperature, and play an important role in fixing CO2 in the soil. Consequently, good residue management practices on agricultural land have many positive impacts on soil quality, crop production quality and decrease the rate of soil erosion. Several studies have been undertaken to develop and test methods to derive information on crop residue cover and soil tillage using empirical and semi-empirical methods in combination with remote sensing data. However, these methods are generally not sufficiently rigorous and accurate for characterizing the spatial variability of crop residue cover in agricultural fields. The goal of this research is to investigate the potential of hyperspectral Hyperion (Earth Observing-1, EO-1) data and constrained linear spectral mixture analysis (CLSMA) for percent crop residue cover estimation and mapping. Hyperion data were acquired together with ground-reference measurements for validation purposes at the beginning of the agricultural season (prior to spring crop planting) in Saskatchewan (Canada). At this time, only bare soil and crop residue were present with no crop cover development. In order to extract the crop residue fraction, the images were preprocessed, and then unmixed considering the entire spectral range (427 nm–2355 nm) and the pure spectra (endmember). The results showed that the correlation between ground-reference measurements and extracted fractions from the Hyperion data using CLMSA showed that the model was overall a very good predictor for crop residue percent cover (index of agreement (D) of 0.94, coefficient of determination (R2) of 0.73 and root mean square error (RMSE) of 8.7%) and soil percent cover (D of 0.91, R2 of 0.68 and RMSE of 10.3%). This performance of Hyperion is mainly due to the spectral band characteristics, especially the availability of contiguous narrow bands in the short-wave infrared (SWIR) region, which is sensitive to the residue (lignin and cellulose absorption features).Ye

Cartographie de l’érosion hydrique en zone montagneuse : cas du bassin versant des Aït Bou Goumez, Haut Atlas, Maroc

Ce travail présente la cartographie de l’érosion hydrique en zones montagneuses (Haute Atlas, Maroc) en se basant sur trois facteurs principaux : la friabilité du substratum rocheux, le degré de pente et la densité du couvert végétal. La carte du potentiel érosif est obtenue par la somme d’indices attribués aux couches thématiques (la friabilité du substratum rocheux, le degré de la pente et la densité du couvert végétal) multipliés par le pourcentage de contribution de chaque facteur. Dans un environnement de SIG, les pourcentages de contribution ont été obtenus par itération en se référant à la réalité de terrain. Les pourcentages 50%, 30% et 20% retenus correspondent respectivement à la friabilité du substratum, le degré de la pente et la densité du couvert végétal. Ce travail peut être utilisé pour cartographier le potentiel érosif dans d’autres bassins versant du Haut Atlas central qui présentent des faciès géologiques, une topographie et un couvert végétal similaires à la zone des Aït Bou Goumez.El objetivo de este trabajo es la realizacion de la cartografía de erosión hídrica en zonas montañosas (Alto Atlas, Marruecos), basándose sobre tres principales factores: la friabilidad del sustrato litológico, el buzamiento y la densidad de la coberteza vegetal. El mapa del potencial erosivo se obtiene por la suma de índices relativos a las capas temáticas (la friabilidad del sustrato litológico, el buzamiento y la densidad de la coberteza vegeta) multiplicado por los porcentajes de contribución de cada factor. Estos porcentajes se han obtenido por iteración refériéndose a la realidad del terreno. Los porcentajes 50%, 30% y 20% retenidos corresponden respectivamente a la friabilidad del sustrato litológico, el buzamiento y la densidad de la coberteza vegetal. Este trabajo puede ser utilizado para cartografiar el potencial erosivo en otras cuencas de drenaje del Alto Atlas central que presentan facies geológicas, topografía y coberteza vegetal similares a la zona de Aït Bou Goumez

An Image Segmentation Based on a Genetic Algorithm for Determining Soil Coverage by Crop Residues

Determination of the soil coverage by crop residues after ploughing is a fundamental element of Conservation Agriculture. This paper presents the application of genetic algorithms employed during the fine tuning of the segmentation process of a digital image with the aim of automatically quantifying the residue coverage. In other words, the objective is to achieve a segmentation that would permit the discrimination of the texture of the residue so that the output of the segmentation process is a binary image in which residue zones are isolated from the rest. The RGB images used come from a sample of images in which sections of terrain were photographed with a conventional camera positioned in zenith orientation atop a tripod. The images were taken outdoors under uncontrolled lighting conditions. Up to 92% similarity was achieved between the images obtained by the segmentation process proposed in this paper and the templates made by an elaborate manual tracing process. In addition to the proposed segmentation procedure and the fine tuning procedure that was developed, a global quantification of the soil coverage by residues for the sampled area was achieved that differed by only 0.85% from the quantification obtained using template images. Moreover, the proposed method does not depend on the type of residue present in the image. The study was conducted at the experimental farm “El Encín” in Alcalá de Henares (Madrid, Spain)

Site-specific seeding using multi-sensor and data fusion techniques : a review

Site-specific seeding (SSS) is a precision agricultural (PA) practice aiming at optimizing seeding rate and depth, depending on the within field variability in soil fertility and yield potential. Unlike other site-specific applications, SSS was not adopted sufficiently by farmers due to some technological and practical challenges that need to be overcome. Success of site-specific application strongly depends on the accuracy of measurement of key parameters in the system, modeling and delineation of management zone maps, accurate recommendations and finally the right choice of variable rate (VR) technologies and their integrations. The current study reviews available principles and technologies for both map-based and senor-based SSS. It covers the background of crop and soil quality indicators (SQI), various soil and crop sensor technologies and recommendation approaches of map-based and sensor-based SSS applications. It also discusses the potential of socio-economic benefits of SSS against uniform seeding. The current review proposes prospective future technology synthesis for implementation of SSS in practice. A multi-sensor data fusion system, integrating proper sensor combinations, is suggested as an essential approach for putting SSS into practice