Cereal yield modeling in Finland using optical and radar remote sensing : Special Issue Global Croplands

Peer reviewe

Integrating Vegetation Indices Models and Phenological Classification with Composite SAR and Optical Data for Cereal Yield Estimation in Finland (Part I)

Special Issue Microwave Remote Sensing.Abstract: During 1996–2006 the Ministry of Agriculture and Forestry in Finland, MTT Agrifood Research Finland and the Finnish Geodetic Institute carried out a joint remote sensing satellite research project. It evaluated the applicability of composite multispectral SAR and optical satellite data for cereal yield estimations in the annual crop inventory program. Three Vegetation Indices models (VGI, Infrared polynomial, NDVI and Composite multispetral SAR and NDVI) were validated to estimate cereal yield levels using solely optical and SAR satellite data (Composite Minimum Dataset). The average R2 for cereal yield (yb) was 0.627. The averaged composite SAR modeled grain yield level was 3,750 kg/ha (RMSE = 10.3%, 387 kg/ha) for high latitude spring cereals (4,018 kg/ha for spring wheat, 4,037 kg/ha for barley and 3,151 kg/ha for oats). Keywords: Composite multispectral modeling; SAR; classification; SatPhenClass algorithm; minimum dataset; cereal yield; phenology; LAI-bridge; CAP; IACS; FLPISPeer reviewe

Advances in Radar Remote Sensing of Agricultural Crops: A Review

There are enormous advantages of a review article in the field of emerging technology like radar remote sensing applications in agriculture. This paper aims to report select recent advancements in the field of Synthetic Aperture Radar (SAR) remote sensing of crops. In order to make the paper comprehensive and more meaningful for the readers, an attempt has also been made to include discussion on various technologies of SAR sensors used for remote sensing of agricultural crops viz. basic SAR sensor, SAR interferometry (InSAR), SAR polarimetry (PolSAR) and polarimetric interferometry SAR (PolInSAR). The paper covers all the methodologies used for various agricultural applications like empirically based models, machine learning based models and radiative transfer theorem based models. A thorough literature review of more than 100 research papers indicates that SAR polarimetry can be used effectively for crop inventory and biophysical parameters estimation such are leaf area index, plant water content, and biomass but shown less sensitivity towards plant height as compared to SAR interferometry. Polarimetric SAR Interferometry is preferable for taking advantage of both SAR polarimetry and SAR interferometry. Numerous studies based upon multi-parametric SAR indicate that optimum selection of SAR sensor parameters enhances SAR sensitivity as a whole for various agricultural applications. It has been observed that researchers are widely using three models such are empirical, machine learning and radiative transfer theorem based models. Machine learning based models are identified as a better approach for crop monitoring using radar remote sensing data. It is expected that the review article will not only generate interest amongst the readers to explore and exploit radar remote sensing for various agricultural applications but also provide a ready reference to the researchers working in this field

Using the space-borne NASA scatterometer (NSCAT) to determine the frozen and thawed seasons

We hypothesize that the strong sensitivity of radar backscatter to surface dielectric properties, and hence to the phase (solid or liquid) of any water near the surface should make space-borne radar observations a powerful tool for large-scale spatial monitoring of the freeze/thaw state of the land surface, and thus ecosystem growing season length. We analyzed the NASA scatterometer (NSCAT) backscatter from September 1996 to June 1997, along with temperature and snow depth observations and ecosystem modeling, for three BOREAS sites in central Canada. Because of its short wavelength (2.14 cm), NSCAT was sensitive to canopy and surface water. NSCAT had 25 km spatial resolution and approximately twice-daily temporal coverage at the BOREAS latitude. At the northern site the NSCAT signal showed strong seasonality, with backscatter around −8 dB in winter and −12 dB in early summer and fall. The NSCAT signal for the southern sites had less seasonality. At all three sites there was a strong decrease in backscatter during spring thaw (4–6 dB). At the southern deciduous site, NSCAT backscatter rose from −11 to −9.2 dB during spring leaf-out. All sites showed 1–2 dB backscatter shifts corresponding to changes in landscape water state coincident with brief midwinter thaws, snowfall, and extreme cold (Tmax\u3c−25°C). Freeze/thaw detection algorithms developed for other radar instruments gave reasonable results for the northern site but were not successful at the two southern sites. We developed a change detection algorithm based on first differences of 5-day smoothed NSCAT backscatter measurements. This algorithm had some success in identifying the arrival of freezing conditions in the autumn and the beginning of thaw in the spring. Changes in surface freeze/thaw state generally coincided with the arrival and departure of the seasonal snow cover and with simulated shifts in the directions of net carbon exchange at each of the study sites

Assimilation de données satellitaires pour le suivi des ressources en eau dans la zone Euro-Méditerranée

Une estimation plus précise de l'état des variables des surfaces terrestres est requise afin d'améliorer notre capacité à comprendre, suivre et prévoir le cycle hydrologique terrestre dans diverses régions du monde. En particulier, les zones méditerranéennes sont souvent caractérisées par un déficit en eau du sol affectant la croissance de la végétation. Les dernières simulations du GIEC (Groupe d'Experts Intergouvernemental sur l'Evolution du Climat) indiquent qu'une augmentation de la fréquence des sécheresses et des vagues de chaleur dans la région Euro-Méditerranée est probable. Il est donc crucial d'améliorer les outils et l'utilisation des observations permettant de caractériser la dynamique des processus des surfaces terrestres de cette région. Les modèles des surfaces terrestres ou LSMs (Land Surface Models) ont été développés dans le but de représenter ces processus à diverses échelles spatiales. Ils sont habituellement forçés par des données horaires de variables atmosphériques en point de grille, telles que la température et l'humidité de l'air, le rayonnement solaire et les précipitations. Alors que les LSMs sont des outils efficaces pour suivre de façon continue les conditions de surface, ils présentent encore des défauts provoqués par les erreurs dans les données de forçages, dans les valeurs des paramètres du modèle, par l'absence de représentation de certains processus, et par la mauvaise représentation des processus dans certaines régions et certaines saisons. Il est aussi possible de suivre les conditions de surface depuis l'espace et la modélisation des variables des surfaces terrestres peut être améliorée grâce à l'intégration dynamique de ces observations dans les LSMs. La télédétection spatiale micro-ondes à basse fréquence est particulièrement utile dans le contexte du suivi de ces variables à l'échelle globale ou continentale. Elle a l'avantage de pouvoir fournir des observations par tout-temps, de jour comme de nuit. Plusieurs produits utiles pour le suivi de la végétation et du cycle hydrologique sont déjà disponibles. Ils sont issus de radars en bande C tels que ASCAT (Advanced Scatterometer) ou Sentinel-1. L'assimilation de ces données dans un LSM permet leur intégration de façon cohérente avec la représentation des processus. Les résultats obtenus à partir de l'intégration de données satellitaires fournissent une estimation de l'état des variables des surfaces terrestres qui sont généralement de meilleure qualité que les simulations sans assimilation de données et que les données satellitaires elles-mêmes. L'objectif principal de ce travail de thèse a été d'améliorer la représentation des variables des surfaces terrestres reliées aux cycles de l'eau et du carbone dans le modèle ISBA grâce à l'assimilation d'observations de rétrodiffusion radar (sigma°) provenant de l'instrument ASCAT. Un opérateur d'observation capable de représenter les sigma° ASCAT à partir de variables simulées par le modèle ISBA a été développé. Une version du WCM (water cloud model) a été mise en œuvre avec succès sur la zone Euro-Méditerranée. Les valeurs simulées ont été comparées avec les observations satellitaires. Une quantification plus détaillée de l'impact de divers facteurs sur le signal a été faite sur le sud-ouest de la France. L'étude de l'impact de la tempête Klaus sur la forêt des Landes a montré que le WCM est capable de représenter un changement brutal de biomasse de la végétation. Le WCM est peu efficace sur les zones karstiques et sur les surfaces agricoles produisant du blé. Dans ce dernier cas, le problème semble provenir d'un décalage temporel entre l'épaisseur optique micro-ondes de la végétation et l'indice de surface foliaire de la végétation. Enfin, l'assimilation directe des sigma° ASCAT a été évaluée sur le sud-ouest de la France.More accurate estimates of land surface conditions are important for enhancing our ability to understand, monitor, and predict key variables of the terrestrial water cycle in various parts of the globe. In particular, the Mediterranean area is frequently characterized by a marked impact of the soil water deficit on vegetation growth. The latest IPCC (Intergovernmental Panel on Climate Change) simulations indicate that occurrence of droughts and warm spells in the Euro-Mediterranean region are likely to increase. It is therefore crucial to improve the ways of understanding, observing and simulating the dynamics of the land surface processes in the Euro-Mediterranean region. Land surface models (LSMs) have been developed for the purpose of representing the land surface processes at various spatial scales. They are usually forced by hourly gridded atmospheric variables such as air temperature, air humidity, solar radiation, precipitation, and are used to simulate land surface states and fluxes. While LSMs can provide a continuous monitoring of land surface conditions, they still show discrepancies due to forcing and parameter errors, missing processes and inadequate model physics for particular areas or seasons. It is also possible to observe the land surface conditions from space. The modelling of land surface variables can be improved through the dynamical integration of these observations into LSMs. Remote sensing observations are particularly useful in this context because they are able to address global and continental scales. Low frequency microwave remote sensing has advantages because it can provide regular observations in all-weather conditions and at either daytime or night-time. A number of satellite-derived products relevant to the hydrological and vegetation cycles are already available from C-band radars such as the Advanced Scatterometer (ASCAT) or Sentinel-1. Assimilating these data into LSMs permits their integration in the process representation in a consistent way. The results obtained from assimilating satellites products provide land surface variables estimates that are generally superior to the model estimates or satellite observations alone. The main objective of this thesis was to improve the representation of land surface variables linked to the terrestrial water and carbon cycles in the ISBA LSM through the assimilation of ASCAT backscatter (sigma°) observations. An observation operator capable of representing the ASCAT sigma° from the ISBA simulated variables was developed. A version of the water cloud model (WCM) was successfully implemented over the Euro-Mediterranean area. The simulated values were compared with those observed from space. A more detailed quantification of the influence of various factors on the signal was made over southwestern France. Focusing on the Klaus storm event in the Landes forest, it was shown that the WCM was able to represent abrupt changes in vegetation biomass. It was also found that the WCM had shortcomings over karstic areas and over wheat croplands. It was shown that the latter was related to a discrepancy between the seasonal cycle of microwave vegetation optical depth (VOD) and leaf area index (LAI). Finally, the direct assimilation of ASCAT sigma° observations was assessed over southwestern France

Characterizing Olive Grove Canopies by Means of Ground-Based Hemispherical Photography and Spaceborne RADAR Data

One of the main strengths of active microwave remote sensing, in relation to frequency, is its capacity to penetrate vegetation canopies and reach the ground surface, so that information can be drawn about the vegetation and hydrological properties of the soil surface. All this information is gathered in the so called backscattering coefficient (σ0). The subject of this research have been olive groves canopies, where which types of canopy biophysical variables can be derived by a specific optical sensor and then integrated into microwave scattering models has been investigated. This has been undertaken by means of hemispherical photographs and gap fraction procedures. Then, variables such as effective and true Leaf Area Indices have been estimated. Then, in order to characterize this kind of vegetation canopy, two models based on Radiative Transfer theory have been applied and analyzed. First, a generalized two layer geometry model made up of homogeneous layers of soil and vegetation has been considered. Then, a modified version of the Xu and Steven Water Cloud Model has been assessed integrating the canopy biophysical variables derived by the suggested optical procedure. The backscattering coefficients at various polarized channels have been acquired from RADARSAT 2 (C-band), with 38.5° incidence angle at the scene center. For the soil simulation, the best results have been reached using a Dubois scattering model and the VV polarized channel (r2 = 0.88). In turn, when effective LAI (LAIeff) has been taken into account, the parameters of the scattering canopy model are better estimated (r2 = 0.89). Additionally, an inversion procedure of the vegetation microwave model with the adjusted parameters has been undertaken, where the biophysical values of the canopy retrieved by this methodology fit properly with field measured values

Quantitative Estimation of Surface Soil Moisture in Agricultural Landscapes using Spaceborne Synthetic Aperture Radar Imaging at Different Frequencies and Polarizations

Soil moisture and its distribution in space and time plays an important role in the surface energy balance at the soil-atmosphere interface. It is a key variable influencing the partitioning of solar energy into latent and sensible heat flux as well as the partitioning of precipitation into runoff and percolation. Due to their large spatial variability, estimation of spatial patterns of soil moisture from field measurements is difficult and not feasible for large scale analyses. In the past decades, Synthetic Aperture Radar (SAR) remote sensing has proven its potential to quantitatively estimate near surface soil moisture at high spatial resolutions. Since the knowledge of the basic SAR concepts is important to understand the impact of different natural terrain features on the quantitative estimation of soil moisture and other surface parameters, the fundamental principles of synthetic aperture radar imaging are discussed. Also the two spaceborne SAR missions whose data was used in this study, the ENVISAT of the European Space Agency (ESA) and the ALOS of the Japanese Aerospace Exploration Agency (JAXA), are introduced. Subsequently, the two essential surface properties in the field of radar remote sensing, surface soil moisture and surface roughness are defined, and the established methods of their measurement are described. The in situ data used in this study, as well as the research area, the River Rur catchment, with the individual test sites where the data was collected between 2007 and 2010, are specified. On this basis, the important scattering theories in radar polarimetry are discussed and their application is demonstrated using novel polarimetric ALOS/PALSAR data. A critical review of different classical approaches to invert soil moisture from SAR imaging is provided. Five prevalent models have been chosen with the aim to provide an overview of the evolution of ideas and techniques in the field of soil moisture estimation from active microwave data. As the core of this work, a new semi-empirical model for the inversion of surface soil moisture from dual polarimetric L-band SAR data is introduced. This novel approach utilizes advanced polarimetric decomposition techniques to correct for the disturbing effects from surface roughness and vegetation on the soil moisture retrieval without the use of a priori knowledge. The land use specific algorithms for bare soil, grassland, sugar beet, and winter wheat allow quantitative estimations with accuracies in the order of 4 Vol.-%. Application of remotely sensed soil moisture patterns is demonstrated on the basis of mesoscale SAR data by investigating the variability of soil moisture patterns at different spatial scales ranging from field scale to catchment scale. The results show that the variability of surface soil moisture decreases with increasing wetness states at all scales. Finally, the conclusions from this dissertational research are summarized and future perspectives on how to extend the proposed model by means of improved ground based measurements and upcoming advances in sensor technology are discussed. The results obtained in this thesis lead to the conclusion that state-of-the-art spaceborne dual polarimetric L-band SAR systems are not only suitable to accurately retrieve surface soil moisture contents of bare as well as of vegetated agricultural fields and grassland, but for the first time also allow investigating within-field spatial heterogeneities from space

Earth resources: A continuing bibliography with indexes, issue 50

This bibliography lists 523 reports, articles and other documents introduced into the NASA scientific and technical information system between April 1 and June 30, 1986. Emphasis is placed on the use of remote sensing and geophysical instrumentation in spacecraft and aircraft to survey and inventory natural resources and urban areas. Subject matter is grouped according to agriculture and forestry, environmental changes and cultural resources, geodesy and cartography, geology and mineral resources, hydrology and water management, data processing and distribution systems, instrumentation and sensors, and economic analysis

Optimization of Soil Hydraulic Model Parameters Using Synthetic Aperture Radar Data: An Integrated Multidisciplinary Approach

It is widely recognized that Synthetic Aperture Radar (SAR) data are a very valuable source of information for the modeling of the interactions between the land surface and the atmosphere. During the last couple of decades, most of the research on the use of SAR data in hydrologic applications has been focused on the retrieval of land and biogeophysical parameters (e.g., soil moisture contents). One relatively unexplored issue consists of the optimization of soil hydraulic model parameters, such its, for example, hydraulic conductivity, values, through remote sensing. This is due to the fact that no direct relationships between the remote-sensing observations, more specifically radar backscatter values, and the parameter values can be derived. However, land surface models can provide these relationships. The objective of this paper is to retrieve a number of soil physical model parameters through a combination of remote sensing anti land surface modeling. Spatially distributed and multitemporal SAR-based soil moisture maps are the basis of the study. The surface soil moisture values are used in a parameter estimation procedure basest on the Extended Kalman Filter equations. In fact, the land surface model is, thus, used to determine the relationship between the soil physical parameters and the remote-sensing data. An analysis is then performed, relating the retrieved soil parameters to the soil texture data available over the study area. The results of the study show that there is a potential to retrieve soil physical model parameters through a combination of land surface modeling and remote sensing