Methodology for combining optical and microwave remote sensing in agricultural crop monitoring : the sugar beet crop as special case

Accurate and up-to-date information on agricultural production is a vital component in running present market economies. In Europe considerable differences between c es in their agricultural production have led to a complex system of rules and subsidies which all rely on a certain level of accuracy regarding agricultural statistics (such as acreage and yield). At national level and regional level, such statistics have been collected so far by using conventional methods, which are mostly based on knowledge and experience from the past. Before using this information on a European level, there is a growing need for combining new information techniques and present knowledge to provide realistic estimates of crop yield and production on a lower scale level.Yield prediction is an important tool for industry, fanners and policy makers, facilitating logistic planning of transportation and production, storage and sale at national level and planning at farm level. In this thesis, the study is concentrated on the application of observation or remote sensing (RS) techniques to crop growth monitoring of agricultural crops in the Netherlands. A common crop in the Netherlands is sugar beet crop and this crop served as a perfect illustration for validation of the developed methodology in this study. The objective of this study is to understand how optical and microwave remote sensing may be used in a synergetic way in order to develop a methodology, that can be used to monitor crop growth and predict crop yield together with existing knowledge.More specifically, the study presented in this thesis aims to reveal (1) how useful information on biophysical properties of agricultural crops estimated with airborne remote sensing is for crop growth monitoring and yield prediction, (2) how successful this information can be utilized in the developed methodology for combining crop growth and remote sensing and (3) whether there are possibilities to apply this methodology for operational crop growth monitoring and yield prediction procedures by using airborne and to some lesser extent the current available spaceborne sensors.The thesis work is subdivided in three parts. Part I outlines the theory and background supporting the thesis methodology and the combination methodology itself In Part IL the test data are presented and, for the case study, the synergy of the combination of information is studied, especially for the multi-sensor airborne campaign MAC Europe 1991. Here the research questions I and 2 are being studied. The application of the methodology (research question 3) described in this thesis is evaluated in Part 111, accompanied by concluding remarks and recommendations.In Chapter 2, an inventory of the information estimated with RS is made in order to prepare the development of a methodology to monitor growth and production of agricultural crops with RS techniques. The major objective of this study is the investigation of the possibilities of a synergistic use of both optical and microwave RS data. Therefore, a review of the state of the art in modelling in the reflective optical and microwave region of the electromagnetic (EM) spectrum is performed. Furthermore, the most suitable models are selected and validated with the data from campaigns held in the Flevoland Province of the Netherlands as good as possible. It appeared that semi-empirical RS models, describing the observation of crops in a simplified physical way, could be calibrated and validated better than the complex radiative transfer models. The CLAIR model in the optical region has proven to be applicable over the different growing seasons, while the semi-empirical Cloud model in the microwave region revealed an unstable behaviour. Both models are calibrated with campaign data and were applied under strict conditions in this study in order to supply actual crop status information on respectively leaf area index (LAI) from the optical and biomass in the microwave model by inversion. From sensitivity analysis of the more complex radiative transfer (RS) models canopy structure appeared to be another important factor in the observation of crops as well in the optical as in the microwave region. Canopy structure information is not clearly incorporated in the semi-empirical RS models and therefore difficult to estimate. Changes in canopy structure have been recognised as specific features in time series of RS observation of the crop during the growing season, especially in microwave RS observations. The sugar beet crop revealed some characteristic features during the growing season, but not as clear as the vertically structured cereal crops, like winter wheat. Crop development related to changes in canopy structure in the case of winter wheat showed more potential for detection in RS time series as for sugar beet.In Chapter 3, a general methodology is proposed for combining the information (RS data, field data and models) of different sources in order to monitor crop growth and predict the yield. The underlying physiological processes of crop growth are studied for linkage of crop growth models with RS information. The SUCROS-type of crop growth model for the sugar beet crop from the School of de Wit from Wageningen appeared to be very suitable for this study, because of its detailed description of crop growth modelling and its status of being well initialised for crop growth conditions for sugar beet in the Flevoland Province. In this chapter, different methods were developed to calibrate the crop growth model with the actual information estimated by RS. The combination methods are:· Direct modelling method: Calibration of crop growth model with a forward RS model. By comparing the simulated RS signal with the observed RS data optimization of the most important variables of the crop growth model is performed.· Inverse modelling method: Calibration of crop growth model with an inverse RS model. In this method crop variables estimated with an inverse RS model are compared with crop variables of the crop growth model and used for optimization of the most important crop growth variables of the crop growth model.· Feature modelling method: Calibration of crop growth model by using characteristic information from RS time-series, which is mostly related to a change in structure of the canopy owing to changes in development stage of the crop.The direct model-based approach is only used for reference for the other methods and is developed in former research.The inverse model-based approach combines LAI and biomass estimated by optical and microwave RS model inversion with the crop growth model. The crop growth model was calibrated with this information and their estimation accuracies by using the reciproke of the standard deviation, which reflects the 'state of the art' in the RS modelling.The feature-based approach completes the methodology by detection of features in RS time series information on changes in canopy structure possibly related to crop development stages, which provide another source of information to calibrate the crop growth model as well. The overall methodology comprises the combination of the two approaches.Chapter 4 comprises a brief overview of data sets from campaigns at the Flevoland test site held in the past. In order to study the effect of synergism of optical and microwave RS data, conditioned data sets were required and aspects of quality and quantity of data in campaigns were discussed. The criteria for the synergy study were best met by the data set of the MAC Europe 1991 campaign compared to the other available data sets. For testing the combination methodologies of Chapter 3, the data from the airborne MAC Europe 1991 campaign were selected for the synergy study. This campaign was held at the time of the thesis study, so specific additional measurements could be collected like measurements on canopy structure. For RS model calibration and validation as well as for crop growth model initialization the Agriscatt 1987 and 1988 campaigns proved particularly suitable, because of the highly detailed information on field measurements. The ROVE data set from the late seventies provided measurements of high temporal frequency and were used for study of the impact of canopy structure on microwave backscatter and with that to illustrate specific radar features. The spaceborne ERS-1 time series from 1992 and 1993 were selected in order to discuss the potential of microwave satellite RS for operational crop growth monitoring in the last chapter and were not explicitly used in the study. A total processing line and a database for RS data interpretation was set up to prepare the study.In Chapter 5, the proposed combination methods of Chapter 3 were applied with contemporaneous (simultaneous) and non-contemporaneous recordings of airborne optical and microwave sensors of the MAC Europe 1991 campaign. The configuration of the airborne RS data was selected for this study on basis of the current optical (SPOT and Landsat) and microwave (ERS-1/2 and JERS-1) satellite configurations. The performance of the methods was measured by comparing the simulated yield as a result of the calibrated crop growth model and the actual measured yield figures at a specific harvest date.The inverse method is tested on the selected data set. The inverse RS model estimates LAI and biomass with a certain accuracy. The accuracy depends on the success of calibration of the (direct) RS model. It appeared that estimation of LAI from the optical model 'CLAIR' is at least twice as good as estimation of LAI from microwave model 'Cloud'. The combination of the crop growth model with optical data only gave good results. The added value of microwave data to this is present when no optical data are available (e.g. bad weather conditions). Using the information from both the airborne optical and microwave sensors weighted with the reciproke of the standard deviations the combination methods yielded success especially when the RS data was acquired in the beginning of the growing season. In this period the LAI can be well estimated, especially with optical RS models. Later in the growing season other information was found in RS time-series. With special attention to microwave time-series information on changes in canopy structure has been found and validated with field measurements of leaf angle distributions with respect to sugar beet. In the case of sugar beet these changes in structure are not clearly related to development of the crop. However, this is more pronounced in the case of cereals (e.g. winter wheat). This is information is also a source of calibration of the crop growth model. However, the accuracy of the feature found in the time-series is not high enough to calibrate the already well initialized crop growth model. When the observation frequency is high enough (weekly) then this information could be used for estimating the moment of sowing by using the meteorological information during the growing season.Chapter 6 discusses the practical application of the methodology. An important aspect is that the level of study is translated from field to regional level in order to find practical use for the method in conventional prediction strategies of the present (food) processing industry. The generalization step appeared to give new information. An example is that e.g. the minimum. in standard deviation in backscatter time-series from ERS-1 for all sugar beet fields in the Southern part of the Flevoland Province appeared to be related to a regional crop closure of the sugar beet for two different years (1992 and 1993). It is obvious that information estimated with RS models for each specific crop is valuable for crop growth monitoring when the moment and density of the RS measurement is well chosen during the growing season. This imposes high requirements to the present available satellite systems (ERS-1/2, JERS-1, Radarsat, SPOT, Landsat, etc.)Chapter 7 presents the main conclusions and recommendations for further research. More research is needed to calibrate and validate RS models for application in crop growth monitoring. The present generation crop growth models evolve towards reliable tools for yield forecasting and impose high requirements on quality of the RS information in order to be valuable for operational purposes. The methods used in this study gave good results in the case for airborne RS data on field level. Airborne optical and microwave RS information appeared to give synergetic results when combining with a crop growth model. The step towards a more operational monitoring method is expected to be difficult. The latter should be studied into more detail by using a more simple crop growth model and regional information from RS

Integration of Satellite and Financial Data to Model Future Economic Impact of Citrus Crops (Final Project Report)

This study analyzed the health and overall landcover of citrus crops in Florida. The analysis was completed using Landsat satellite imagery available free of charge from the University of Maryland Global Landcover Change Facility. The project hypothesized that combining citrus production (economic) data with citrus area per county derived from spectral signatures would yield correlations between observable spectral reflectance throughout the year, and the fiscal impact of citrus on local economies. A positive correlation between these two data types would allow us to predict the economic impact of citrus using spectral data analysis to determine final crop harvests

Optical and radar remote sensing applied to agricultural areas in europe

The global population growth, as well as the social and economic importance that the agricultural sector has in many regions of the world, makes it very important to develop methods to monitor the status of crops, to improve their management, as well as to be able to make early estimates of the agricultural production. One of the main causes of uncertainty in the production of crops is due to the weather, for example, in arid and semiarid regions of the world, periods of drought can generate big losses in agricultural production, which may result in famine. Thus, FAO, during their summit in June 2008, stressed the need to increase agricultural production as a measure to strengthen food security and reduce malnutrition in the world. Concern for increasing crop production, has generated, during the last decades, significant changes in agricultural techniques. For example, there has been a widespread use of pesticides, genetically modified crops, as well as an increase in intensive farming. In turn, the market influences crop rotations, and as a consequence, changes in the spatial distribution of crops are very common. Therefore, in order to make estimates of agricultural production, it is also necessary to map regularly the crop fields, as well as their state of development. The aim of this thesis is to develop methods based on remote sensing data, in the radar and optical spectral regions, in order to monitor crops, as well as a to map them. The results of this thesis can be combined with other techniques, especially with models of crop growth, to improve the prediction of crops. The optical remote sensing methods for classifying and for the cartography of crops are well established and can be considered almost operational. The disadvantage of the methods based on optical data is that they are not applicable to regions of the world where cloud coverage is frequent. In such cases, the use of radar data is more advisable. However, the classification methods using radar data are not as well established as the optical ones, therefore, there is a need for more scientific studies in this field. As a consequence, this thesis focuses on the classification of crops using radar data, particularly using AIRSAR airborne data and ASAR satellite data

Crop classification from full-year fully-polarimetric L-band UAVSAR time-series using the Random Forest algorithm

Accurate and timely information on the distribution of crop types is vital to agricultural management, ecosystem services valuation and food security assessment. Synthetic Aperture Radar (SAR) systems have become increasingly popular in the field of crop monitoring and classification. However, the potential of time-series polarimetric SAR data has not been explored extensively, with several open scientific questions (e.g. the optimal combination of image dates for crop classification) that need to be answered. In this research, the usefulness of full year (both 2011 and 2014) L-band fully-polarimetric Uninhabited Aerial Vehicle Synthetic Aperture Radar (UAVSAR) data in crop classification was fully investigated over an agricultural region with a heterogeneous distribution of crop categories. In total, 11 crop classes including tree crops (almond and walnut), forage crops (grass, alfalfa, hay, and clover), a spring crop (winter wheat), and summer crops (corn, sunflower, tomato, and pepper), were discriminated using the Random Forest (RF) algorithm. The SAR input variables included raw linear polarization channels as well as polarimetric parameters derived from Cloude-Pottier (CP) and Freeman-Durden (FD) decompositions. Results showed clearly that the polarimetric parameters yielded much higher classification accuracies than linear polarizations. The combined use of all variables (linear polarizations and polarimetric parameters) produced the maximum overall accuracy of 90.50 % and 84.93 % for 2011 and 2014, respectively, with a significant increase of approximately 8 percentage points compared with linear polarizations alone. The variable importance provided by the RF illustrated that the polarimetric parameters had a far greater influence than linear polarizations, with the CP parameters being much more important than the FD parameters. The most important acquisitions were the images dated during the peak biomass stage (July and August) when the differences in structural characteristics between most crops were the largest. At the same time, the images in spring (April and May) and autumn (October) also contributed to the crop classification since they respectively provided unique information for discriminating fruit crops (almond and walnut) as well as summer crops (corn, sunflower, and tomato). As a result, the combined use of only four acquisitions (dated May, July, August, and October for 2011 and April, June, August, and October for 2014) was adequate to achieve a nearly-optimal overall accuracy. In light of the promising classification accuracies demonstrated in this research, it becomes increasingly viable to provide accurate and up-to-date crops inventories over large areas based solely on multitemporal polarimetric SAR

Polarimetric Synthetic Aperture Radar

This open access book focuses on the practical application of electromagnetic polarimetry principles in Earth remote sensing with an educational purpose. In the last decade, the operations from fully polarimetric synthetic aperture radar such as the Japanese ALOS/PalSAR, the Canadian Radarsat-2 and the German TerraSAR-X and their easy data access for scientific use have developed further the research and data applications at L,C and X band. As a consequence, the wider distribution of polarimetric data sets across the remote sensing community boosted activity and development in polarimetric SAR applications, also in view of future missions. Numerous experiments with real data from spaceborne platforms are shown, with the aim of giving an up-to-date and complete treatment of the unique benefits of fully polarimetric synthetic aperture radar data in five different domains: forest, agriculture, cryosphere, urban and oceans

Full year crop monitoring and separability assessment with fully-polarimetric L-band UAVSAR:A case study in the Sacramento Valley, California

Spatial and temporal information on plant and soil conditions is needed urgently for monitoring of crop productivity. Remote sensing has been considered as an effective means for crop growth monitoring due to its timely updating and complete coverage. In this paper, we explored the potential of L-band fully-polarimetric Uninhabited Aerial Vehicle Synthetic Aperture Radar (UAVSAR) data for crop monitoring and classification. The study site was located in the Sacramento Valley, in California where the cropping system is relatively diverse. Full season polarimetric signatures, as well as scattering mechanisms, for several crops, including almond, walnut, alfalfa, winter wheat, corn, sunflower, and tomato, were analyzed with linear polarizations (HH, HV, and VV) and polarimetric decomposition (Cloude–Pottier and Freeman–Durden) parameters, respectively. The separability amongst crop types was assessed across a full calendar year based on both linear polarizations and decomposition parameters. The unique structure-related polarimetric signature of each crop was provided by multitemporal UAVSAR data with a fine temporal resolution. Permanent tree crops (almond and walnut) and alfalfa demonstrated stable radar backscattering values across the growing season, whereas winter wheat and summer crops (corn, sunflower, and tomato) presented drastically different patterns, with rapid increase from the emergence stage to the peak biomass stage, followed by a significant decrease during the senescence stage. In general, the polarimetric signature was heterogeneous during June and October, while homogeneous during March-to-May and July-to-August. The scattering mechanisms depend heavily upon crop type and phenological stage. The primary scattering mechanism for tree crops was volume scattering (>40%), while surface scattering (>40%) dominated for alfalfa and winter wheat, although double-bounce scattering (>30%) was notable for alfalfa during March-to-September. Surface scattering was also dominant (>40%) for summer crops across the growing season except for sunflower and tomato during June and corn during July-to-October when volume scattering (>40%) was the primary scattering mechanism. Crops were better discriminated with decomposition parameters than with linear polarizations, and the greatest separability occurred during the peak biomass stage (July-August). All crop types were completely separable from the others when simultaneously using UAVSAR data spanning the whole growing season. The results demonstrate the feasibility of L-band SAR for crop monitoring and classification, without the need for optical data, and should serve as a guideline for future research

Polarimetric Synthetic Aperture Radar, Principles and Application

Demonstrates the benefits of the usage of fully polarimetric synthetic aperture radar data in applications of Earth remote sensing, with educational and development purposes. Includes numerous up-to-date examples with real data from spaceborne platforms and possibility to use a software to support lecture practicals. Reviews theoretical principles in an intuitive way for each application topic. Covers in depth five application domains (forests, agriculture, cryosphere, urban, and oceans), with reference also to hazard monitorin