Crop biophysical parameter retrieval from Sentinel-1 SAR data with a multi-target inversion of Water Cloud Model

Estimation of bio-and geophysical parameters from Earth observation (EO) data is essential for developing applications on crop growth monitoring. High spatio-temporal resolution and wide spatial coverage provided by EO satellite data are key inputs for operational crop monitoring. In Synthetic Aperture Radar (SAR) applications, a semi-empirical model (viz., Water Cloud Model (WCM)) is often used to estimate vegetation descriptors individually. However, a simultaneous estimation of these vegetation descriptors would be logical given their inherent correlation, which is seldom preserved in the estimation of individual descriptors by separate inversion models. This functional relationship between biophysical parameters is essential for crop yield models, given that their variations often follow different distribution throughout crop development stages. However, estimating individual parameters with independent inversion models presume a simple relationship (potentially linear) between the biophysical parameters. Alternatively, a multi-target inversion approach would be more effective for this aspect of model inversion compared to an individual estimation approach. In the present research, the multi-output support vector regression (MSVR) technique is used for inversion of the WCM from C-band dual-pol Sentinel-1 SAR data. Plant Area Index (PAI, m2 m−2) and wet biomass (W, kg m−2) are used as the vegetation descriptors in the WCM. The performance of the inversion approach is evaluated with in-situ measurements collected over the test site in Manitoba (Canada), which is a super-site in the Joint Experiment for Crop Assessment and Monitoring (JECAM) SAR inter-comparison experiment network. The validation results indicate a good correlation with acceptable error estimates (normalized root mean square error–nRMSE and mean absolute error–MAE) for both PAI and wet biomass for the MSVR approach and a better estimation with MSVR than single-target models (support vector regression–SVR). Furthermore, the correlation between PAI and wet biomass is assessed using the MSVR and SVR model. Contrary to the single output SVR, the correlation between biophysical parameters is adequately taken into account in MSVR based simultaneous inversion technique. Finally, the spatio-temporal maps for PAI and W at different growth stages indicate their variability with crop development over the test site.This research was supported in part by Shastri Indo-Candian Institute, New Delhi, India and the Spanish Ministry of Economy, Industry and Competitiveness, in part by the State Agency of Research (AEI), in part by the European Funds for Regional Development under project TEC2017-85244-C2-1-P

Dual polarimetric radar vegetation index for crop growth monitoring using sentinel-1 SAR data

Sentinel-1 Synthetic Aperture Radar (SAR) data have provided an unprecedented opportunity for crop monitoring due to its high revisit frequency and wide spatial coverage. The dual-pol (VV-VH) Sentinel-1 SAR data are being utilized for the European Common Agricultural Policy (CAP) as well as for other national projects, which are providing Sentinel derived information to support crop monitoring networks. Among the Earth observation products identified for agriculture monitoring, indicators of vegetation status are deemed critical by end-user communities. In literature, several experiments usually utilize the backscatter intensities to characterize crops. In this study, we have jointly utilized the scattering information in terms of the degree of polarization and the eigenvalue spectrum to derive a new vegetation index from dual-pol (DpRVI) SAR data. We assess the utility of this index as an indicator of plant growth dynamics for canola, soybean, and wheat, over a test site in Canada. A temporal analysis of DpRVI with crop biophysical variables (viz., Plant Area Index (PAI), Vegetation Water Content (VWC), and dry biomass (DB)) at different phenological stages confirms its trend with plant growth dynamics. For each crop type, the DpRVI is compared with the cross and co-pol ratio (σVH0/σVV0) and dual-pol Radar Vegetation Index (RVI = 4σVH0/(σVV0 + σVH0)), Polarimetric Radar Vegetation Index (PRVI), and the Dual Polarization SAR Vegetation Index (DPSVI). Statistical analysis with biophysical variables shows that the DpRVI outperformed the other four vegetation indices, yielding significant correlations for all three crops. Correlations between DpRVI and biophysical variables are highest for canola, with coefficients of determination (R2) of 0.79 (PAI), 0.82 (VWC), and 0.75 (DB). DpRVI had a moderate correlation (R2≳ 0.6) with the biophysical parameters of wheat and soybean. Good retrieval accuracies of crop biophysical parameters are also observed for all three crops.This work was supported by the Spanish Ministry of Science, Innovation and Universities, the State Agency of Research (AEI) and the European Funds for Regional Development (EFRD) under Project TEC2017-85244-C2-1-P

Crop development monitoring from Synthetic Aperture Radar (SAR) imagery

Satellite remote sensing plays a vital role in providing large-scale and timely data to stakeholders of the agricultural supply chain. This allows for informed decision-making that promotes sustainable and cost-effective crop management practices. In particular, data derived from satellite-based Synthetic Aperture Radar (SAR) systems, provide opportunities for continuous crop monitoring, taking advantage of its ability to acquire images during day or night and under almost all weather conditions. Moreover, an abundance of SAR data can be anticipated in the next 5 years with the launch of several international SAR missions. However, research on crop development monitoring with data from SAR satellites has not been as widely studied as with data derived from passive multi-spectral satellites and contributions can be made to the current state-of-the-art techniques. This thesis aims at improving the current knowledge on the use of satellite-based SAR imagery for crop development monitoring. This is approached by developing novel methodologies and detailed interpretations of multitemporal SAR and Polarimetric SAR (PolSAR) responses to crop growth in three different test sites. Chapter two presents a detailed analysis of the Sentinel-1 SAR satellite response to asparagus crop development in Peru, investigating the capabilities of the sensor to capture seasonality effects as well as providing an interpretation of the temporal backscatter signature. This is complemented with a case study where a multiple-output random forest regression algorithm is used to successfully retrieve crop growth stage from Sentinel-1 data and temperature measurements. Following the limitations identified with this approach, a methodology that builds upon ideas of Bayesian Filtering Frameworks (BFFs) for crop monitoring is proposed in chapter three. It incorporates Gaussian processes to model crop dynamics as well as to model the remote sensing response to the crop state. Using this approach, it is possible to derive daily predictions with the associated uncertainties, to combine in near-real-time data from active and passive satellites as well as to estimate past and future crop key events that are of strategic importance for different stakeholders. The final section of this thesis looks at the new developments of the SAR technology considering that future open access missions will provide Quad Polarimetric SAR data. An algorithm based on multitemporal PolSAR change detection is introduced in chapter four. It defines a Change Matrix to encode an interpretable representation of the crop dynamics as captured by the evolution of the scattering mechanisms over time. We use rice fields in Spain and multiple cereal crops in Canada to test the use of the algorithm for crop monitoring. A supervised learning-based crop type classification methodology is then proposed with the same method by using the encoded scattering mechanisms as input for a neural-network-based classifier, achieving comparable performances to state-of-the-art classifiers. The results obtained in this thesis represent novel additions to the literature that contribute to our understanding and successful use of SAR imagery for agricultural monitoring. For the first time, a detailed analysis of asparagus crops is presented. It is a key crop for agricultural exports of Peru, the largest exporter of asparagus in the world. Secondly, two key contributions to the state of the art BFFs for crop monitoring are presented: a) A better exploitation of the SAR temporal dimension and an application with freely available data and b) given that it is a learning-based approach, it overcomes current limitations of transferability among crop types and regions. Finally, the PolSAR change detection approach presented in the last thesis chapter, provides a novel and easy-to-interpret tool for both crop monitoring and crop type mapping applications

Satellite remote sensing priorities for better assimilation in crop growth models : winter wheat LAI and grassland mowing dates case studies

In a context of markets globalization, early, reliable and timely estimations of crop yields at regional to global scale are clearly needed for managing large agricultural lands, determining food pricing and trading policies but also for early warning of harvest shortfalls. Crop growth models are often used to estimate yields within the growing season. The uncertainties associated with these models contribute to the uncertainty of crop yield estimations and forecasts. Satellite remote sensing, through its ability to provide synoptic information on growth conditions over large geographic extents and in near real-time, is a key data source used to reduce uncertainties in biophysical models through data assimilation methods. This thesis aims at assessing possible improvements for the assimilation of remotely-sensed biophysical variables in crop growth models and to estimate their related errors reduction on modelled yield estimates. Assimilated observations are winter wheat leaf area index (LAI) and grassland mowing dates derived respectively from optical (MODIS) and microwave (ERS-2) data. These observations have been assimilated in WOFOST and LINGRA growth models. Observing System Simulation Experiments (OSSE) allowed assessing errors reduction on yield estimates after assimilation for different situations of accuracy and frequency of remotely-sensed estimates and for different assimilation strategies, indicating expected improvements with the currently available and forthcoming sensors; it supports also guidelines for future satellite missions. A main finding is the fact that yield estimate improvements are only possible for assimilation strategies able to correct the possible phenological discrepancies between the remotely-sensed and the simulated data. These phenological shifts arise mainly from uncertainties on the parameters and initial states driving the phenological stages in the models but are also due, in some situations, to lack of pixel purity because of the medium resolution of sensors such as MODIS. This thesis also identifies some of the main sources of uncertainties and assesses their impact on assimilation performances. The impact of water content and biomass on SAR backscattering of grasslands is specifically assessed. The backscattering of grasslands increases with the increases of water content and decreases with the biomass in dry conditions. Based on these results, methodologies to classify grasslands according to land use (mowing or grazing) and to detect mowings are designed and demonstrated. A good classification accuracy is observed (overall accuracy around 80%). Results for mowings detection are a bit lower as half of the mowings are correctly identified but the methodology can be considered as promising in particular in the perspective of very dense SAR time series as currently acquired operationally by Sentinel-1.(AGRO - Sciences agronomiques et ingénierie biologique) -- UCL, 201

Multi-target regressor chains with repetitive permutation scheme for characterization of built environments with remote sensing

Multi-task learning techniques allow the beneficial joint estimation of multiple target variables. Here, we propose a novel multi-task regression (MTR) method called ensemble of regressor chains with repetitive permutation scheme. It belongs to the family of problem transformation based MTR methods which foresee the creation of an individual model per target variable. Subsequently, the combination of the separate models allows obtaining an overall prediction. Our method builds upon the concept of so-called ensemble of regressor chains which align single-target models along a flexible permutation, i.e., chain. However, in order to particularly address situations with a small number of target variables, we equip ensemble of regressor chains with a repetitive permutation scheme. Thereby, estimates of the target variables are cascaded to subsequent models as additional features when learning along a chain, whereby one target variable can occupy multiple elements of the chain. We provide experimental evaluation of the method by jointly estimating built-up height and built-up density based on features derived from Sentinel-2 data for the four largest cities in Germany in a comparative setup. We also consider single-target stacking, multi-target stacking, and ensemble of regressor chains without repetitive permutation. Empirical results underline the beneficial performance properties of MTR methods. Our ensemble of regressor chain with repetitive permutation scheme approach achieved most frequently the highest accuracies compared to the other MTR methods, whereby mean improvements across the experiments of 14.5% compared to initial single-target models could be achieved

ALOS-2/PALSAR-2 Calibration, Validation, Science and Applications

Twelve edited original papers on the latest and state-of-art results of topics ranging from calibration, validation, and science to a wide range of applications using ALOS-2/PALSAR-2. We hope you will find them useful for your future research

Étude comparative d’indices de végétation radar à plusieurs fréquences et de l’indice de végétation optique (NDVI) pour le suivi de la croissance des cultures

De nos jours, la télédétection contribue énormément dans le domaine de l’agriculture. La possibilité d’acquisition des mesures en tout temps et la non sensibilité aux perturbations atmosphériques sont des avantages reconnus à la télédétection radar. Cette étude a pour objectif d’effectuer une analyse comparative des indices radar, à savoir l’indice de végétation radar (RVI) et l’indice de végétation radar à double polarisation (IVRDvv) dans trois fréquences (L, C et X) et de l’indice de végétation par différence normalisée (NDVI) utilisé en télédétection multispectrale optique dans un contexte de suivi de la croissance des cultures de blé, de canola, de maïs et de soja. Pour y parvenir, ces indices de végétation radar (RVI et IVRDvv) calculés à plusieurs fréquences et l’indice optique (NDVI) sont utilisés pour effectuer un suivi temporel de la croissance de ces quatre cultures. D’une part, l’efficacité des indices de végétation radar à traduire la quantité de la biomasse végétale disponible est analysée en déterminant l’indice et la fréquence les mieux adaptés au suivi de la croissance de chaque type de culture. D’autre part, la corrélation des indices de végétation radar (RVI et IVRDvv) et le NDVI par rapport à la quantité de la biomasse végétale est utilisée pour apprécier l’usage de ces indices de végétation radar comme alternative à l’utilisation du NDVI dans un contexte de suivi de la croissance des cultures de blé, de canola, de maïs et de soja. Les indices radar RVI (indice de végétation radar) et IVRDvv (indice de végétation radar à double polarisation) ont été calculés sur la base d’images acquises sur les sites des campagnes de terrain SMAP Validation Experiment 2012 (SMAPVEX12) et SMAP Validation Experiment 2016 in Manitoba (SMAPVEX16-MB) situés au Sud du Manitoba. Les données de biomasse végétale ainsi que l’indice de surface foliaire (LAI) ont été recueillis directement sur le terrain durant ces deux campagnes. Les données radar en bande L proviennent de la campagne SMAPVEX12, elles sont acquises par un Uninhabited Aerial Vehicule Synthetic Aperture Radar UAVSAR; celles utilisées en bande C et X ont été acquises durant la campagne SMAPVEX16-MB par les satellites Radarsat-2 et TerraSAR-X, respectivement. Les données optiques proviennent des images de Sentinelle-2. Le suivi de la croissance des cultures de blé, de canola, de maïs et de soja sur une base temporelle a permis de remarquer l’inefficacité de la bande L à évaluer la croissance des plantes. Le coefficient de rétrodiffusion dans cette bande est contrôlé par les paramètres de surface et particulièrement l’humidité du sol plutôt que la biomasse végétale. Les indices de végétation radar en bandes C et X ont présenté de bons résultats qui traduisent l’évolution de la quantité de la biomasse végétale disponible; la bande X étant toutefois beaucoup mieux corrélée à la biomasse végétale. Pour le blé, la quantité de biomasse végétale est mieux corrélée à l’IVRDvv en bande X (R = 0,9) que le NDVI (R = 0,7). De même, pour la culture de canola, la quantité de la biomasse disponible est légèrement mieux corrélée à l’IVRDvv en bande X (R =0,96) qu’au NDVI (R=0,9). D’autre part, le RVI et l’IVRDvv en bande C pour les cultures de maïs et de soja a montré des fortes corrélations avec le NDVI (R = 0,9). Ces résultats montrent que dans un contexte de suivi de la croissance des végétaux, les indices de végétation radar en bande C et X sont une alternative à l’indice de végétation par différence normalisée utilisé en télédétection optique

Artificial Neural Networks in Agriculture

Modern agriculture needs to have high production efficiency combined with a high quality of obtained products. This applies to both crop and livestock production. To meet these requirements, advanced methods of data analysis are more and more frequently used, including those derived from artificial intelligence methods. Artificial neural networks (ANNs) are one of the most popular tools of this kind. They are widely used in solving various classification and prediction tasks, for some time also in the broadly defined field of agriculture. They can form part of precision farming and decision support systems. Artificial neural networks can replace the classical methods of modelling many issues, and are one of the main alternatives to classical mathematical models. The spectrum of applications of artificial neural networks is very wide. For a long time now, researchers from all over the world have been using these tools to support agricultural production, making it more efficient and providing the highest-quality products possible

Remote Sensing of Plant Biodiversity

This Open Access volume aims to methodologically improve our understanding of biodiversity by linking disciplines that incorporate remote sensing, and uniting data and perspectives in the fields of biology, landscape ecology, and geography. The book provides a framework for how biodiversity can be detected and evaluated—focusing particularly on plants—using proximal and remotely sensed hyperspectral data and other tools such as LiDAR. The volume, whose chapters bring together a large cross-section of the biodiversity community engaged in these methods, attempts to establish a common language across disciplines for understanding and implementing remote sensing of biodiversity across scales. The first part of the book offers a potential basis for remote detection of biodiversity. An overview of the nature of biodiversity is described, along with ways for determining traits of plant biodiversity through spectral analyses across spatial scales and linking spectral data to the tree of life. The second part details what can be detected spectrally and remotely. Specific instrumentation and technologies are described, as well as the technical challenges of detection and data synthesis, collection and processing. The third part discusses spatial resolution and integration across scales and ends with a vision for developing a global biodiversity monitoring system. Topics include spectral and functional variation across habitats and biomes, biodiversity variables for global scale assessment, and the prospects and pitfalls in remote sensing of biodiversity at the global scale