Advancing agricultural monitoring for improved yield estimations using SPOT-VGT and PROBA-V type remote sensing data

Accurate and timely crop condition monitoring is crucial for food management and the economic development of any nation. However, accurately estimating crop yield from the field to global scales is a challenge. According to the global strategy of the World Bank, in order to improve national agricultural statistics, crop area, crop production, and crop yield are key variables that all countries should be able to provide. Crop yield assessment requires that both an estimation of the quantity of a product and the area provided for that product should be available. The definition seems simple; however, these measurements are time consuming and subject to error in many circumstances. Remote sensing is one of several methods used for crop yield estimation. The yield results from a combination of environmental factors, such as soil, weather, and farm management, which are responsible for the unique spectral signature of a crop captured by satellite images. Additionally, yield is an expression of the state, structure, and composition of the plant. Various indices, crop masks, and land observation sensors have been developed to remotely observe and control crops in different regions. This thesis focuses on how much low spatial resolution satellites, such as Project for On Board Autonomy Vegetation (PROBA V), can contribute to global crop monitoring by aiding the search for improved methods and datasets for better crop yield estimation. This thesis contains three chapters. The first chapter explores how an existing product, Dry Matter Productivity (DMP), that has been developed for Satellites Pour l’Observation de la Terre or Earth observing Satellites VeGeTation (SPOT VGT), and transferred to PROBA V, can be improved to more closely relate to yield anomalies across selected regions. This chapter also covers the testing of the contribution of stress factors to improve wheat and maize yield estimations. According to Monteith’s theory, crop biomass linearly correlates with the amount of Absorbed Photosynthetically Active Radiation (APAR) and constant Radiation Use Efficiency (RUE) downregulated by stress factors such as CO2, fertilization, temperature, and water stress. The objective of this chapter is to investigate the relative importance of these stress factors in relation to the regional biomass production and yield. The production efficiency model Copernicus Global Land Service Dry Matter Productivity (CGLS DMP), which follows Monteith’s theory, is modified and evaluated for common wheat and silage maize in France, Belgium, and Morocco using SPOT VGT for the 1999–2012 period. The correlations between the crop yield data and the cumulative modified DMP, CGLS DMP, Fraction of APAR (fAPAR), and Normalized Difference Vegetation Index (NDVI) values are analyzed for different crop growth stages. The best results are obtained when combinations of the most appropriate stress factors are included for each selected region, and the modified DMP during the reproductive stage is accumulated. Though no single solution can demonstrate an improvement of the global product, the findings support an extension of the methodology to other regions of the world. The second chapter demonstrates how PROBA V can be used effectively for crop identification mapping by utilizing spectral matching techniques and phenological characteristics of different crop types. The study sites are agricultural areas spread across the globe, located in Flanders (Belgium), Sria (Russia), Kyiv (Ukraine), and Sao Paulo (Brazil). The data are collected for the 2014–2015 season. For each pure pixel within a field, the NDVI profile of the crop type for its growing season is matched with the reference NDVI profile. Three temporal windows are tested within the growing season: green up to senescence, green up to dormancy, and minimum NDVI at the beginning of the growing season to minimum NDVI at the end of the growing season. In order of importance, the crop phenological development period, parcel size, shorter time window, number of ground truth parcels, and crop calendar similarity are the main reasons behind the differences between the results. The methodology described in this chapter demonstrates the potentials and limitations of using 100 m PROBA V with revisiting frequency every 5 days in crop identification across different regions of the world. The final chapter explores the trade off between the different spatial resolutions provided by PROBA V products versus the temporal frequency and, additionally, explores the use of thermal time to improve statistical yield estimations. The ground data are winter wheat yields at the field level for 39 fields across Northern France during one growing season 2014–2015. An asymmetric double sigmoid function is fitted, and the NDVI values are integrated over thermal time and over calendar time for the central pixel of the field, exploring different thresholds to mark the start and end of the cropping season. The integrated NDVI values with different NDVI thresholds are used as a proxy for yield. In addition, a pixel purity analysis is performed for different purity thresholds at the 100 m, 300 m, and 1 km resolutions. The findings demonstrate that while estimating winter wheat yields at the field level with pure pixels from PROBA V products, the best correlation is obtained with a 100 m resolution product. However, several fields must be omitted due to the lack of observations throughout the growing season with the 100 m resolution dataset, as this product has a lower temporal resolution compared to 300 m and 1 km. This thesis is a modest contribution to the remote sensing and data analysis field with its own merits, in particular with respect to PROBA V. The experiments provide interesting insight into the PROBA V dataset at 1 km, 300 m, and 100 m resolutions. Specifically, the results show that 100 m spatial resolution imagery could be used effectively and advantageously in agricultural crop monitoring and crop identification at local – field level – and regional – the administrative regions defined by the national governments – levels. Furthermore, this thesis discusses the limitations of using a low resolution satellite, such as the PROBA V 100 m dataset, in crop monitoring and identification. Also, several recommendations are made for space agencies that can be used when designing the new generation of satellites

Contribution of Remote Sensing on Crop Models: A Review

Crop growth models simulate the relationship between plants and the environment to predict the expected yield for applications such as crop management and agronomic decision making, as well as to study the potential impacts of climate change on food security. A major limitation of crop growth models is the lack of spatial information on the actual conditions of each field or region. Remote sensing can provide the missing spatial information required by crop models for improved yield prediction. This paper reviews the most recent information about remote sensing data and their contribution to crop growth models. It reviews the main types, applications, limitations and advantages of remote sensing data and crop models. It examines the main methods by which remote sensing data and crop growth models can be combined. As the spatial resolution of most remote sensing data varies from sub-meter to 1 km, the issue of selecting the appropriate scale is examined in conjunction with their temporal resolution. The expected future trends are discussed, considering the new and planned remote sensing platforms, emergent applications of crop models and their expected improvement to incorporate automatically the increasingly available remotely sensed products

Crop monitoring and yield estimation using polarimetric SAR and optical satellite data in southwestern Ontario

Optical satellite data have been proven as an efficient source to extract crop information and monitor crop growth conditions over large areas. In local- to subfield-scale crop monitoring studies, both high spatial resolution and high temporal resolution of the image data are important. However, the acquisition of optical data is limited by the constant contamination of clouds in cloudy areas. This thesis explores the potential of polarimetric Synthetic Aperture Radar (SAR) satellite data and the spatio-temporal data fusion approach in crop monitoring and yield estimation applications in southwestern Ontario. Firstly, the sensitivity of 16 parameters derived from C-band Radarsat-2 polarimetric SAR data to crop height and fractional vegetation cover (FVC) was investigated. The results show that the SAR backscatters are affected by many factors unrelated to the crop canopy such as the incidence angle and the soil background and the degree of sensitivity varies with the crop types, growing stages, and the polarimetric SAR parameters. Secondly, the Minimum Noise Fraction (MNF) transformation, for the first time, was applied to multitemporal Radarsat-2 polarimetric SAR data in cropland area mapping based on the random forest classifier. An overall classification accuracy of 95.89% was achieved using the MNF transformation of the multi-temporal coherency matrix acquired from July to November. Then, a spatio-temporal data fusion method was developed to generate Normalized Difference Vegetation Index (NDVI) time series with both high spatial and high temporal resolution in heterogeneous regions using Landsat and MODIS imagery. The proposed method outperforms two other widely used methods. Finally, an improved crop phenology detection method was proposed, and the phenology information was then forced into the Simple Algorithm for Yield Estimation (SAFY) model to estimate crop biomass and yield. Compared with the SAFY model without forcing the remotely sensed phenology and a simple light use efficiency (LUE) model, the SAFY incorporating the remotely sensed phenology can improve the accuracy of biomass estimation by about 4% in relative Root Mean Square Error (RRMSE). The studies in this thesis improve the ability to monitor crop growth status and production at subfield scale

Climate Change Impacts on Agriculture in Europe

COST Action 734 was launched thanks to the coordinated activity of 29 EU countries. The main objective of the Action was the evaluation of impacts from climate change and variability on agriculture for various European areas. Secondary objectives were: collection and review of existing agroclimatic indices and simulation models, to assess hazard impacts on European agricultural areas; to apply climate scenarios for the next few decades; the definition of harmonised criteria to evaluate the impacts of climate change and variability on agriculture; the definition of warning systems guidelines. Based on the result, possible actions (specific recommendations, suggestions, warning systems) were elaborated and proposed to the end-users, depending on their needs

Estimating maize grain yield from crop growth stages using remote sensing and GIS in the Free State Province, South Africa

Early yield prediction of a maize crop is important for planning and policy decisions. Many countries, including South Africa use the conventional techniques of data collection for maize crop monitoring and yield estimation which are based on ground-based visits and reports. These methods are subjective, very costly and time consuming. Empirical models have been developed using weather data. These are also associated with a number of problems due to the limited spatial distribution of weather stations. Efforts are being made to improve the accuracy and timeliness of yield prediction methods. With the launching of satellites, satellite data are being used for maize crop monitoring and yield prediction. Many studies have revealed that there is a correlation between remotely sensed data (vegetation indices) and crop yields. The satellite based approaches are less expensive, save time, data acquisition covers large areas and can be used to estimate maize grain yields before harvest. This study applied Landsat 8 satellite based vegetation indices, Normalized Difference Vegetation Index (NDVI), Soil Adjusted Vegetation Index (SAVI) and Moisture Stress Index (MSI) to predict maize crop yield. These vegetation indices were derived at different growth stages. The investigation was carried out in the Kopanong Local Municipality of the Free State Province, South Africa. Ground-based data (actual harvested maize yields) was collected from Department of Agriculture, Forestry and Fisheries (DAFF). Satellite images were acquired from Geoterra Image (Pty) Ltd and weather data was from the South African Weather Service (SAWS). Multilinear regression approaches were used to relate yields to the remotely sensed indices and meteorological data was used during the development of yield estimation models. The results showed that there are significant correlations between remotely sensed vegetation indices and maize grain yield; up to 63 percent maize yield was predicted from vegetation indices. The study also revealed that NDVI and SAVI are better yield predictors at reproductive growth stages of maize and MSI is a better index to estimate maize yield at both vegetative and reproductive growth stages. The results obtained in this study indicated that maize grain yields can be estimated using satellite indices at different maize growth stages

Review of the current models and approaches used for maize crop yield forecasting in sub-Saharan Africa, and their potential use in early warning systems

Agriculture is the mainstay of many developing economies, and successful production is intricately linked to food security, economic development, and regional stability. Estimates of crop yield for strategic grain crops, such as maize (Zea mays L.) have been used in national food security planning to develop response strategies in years of shortfalls and secure markets in years of surplus. Past studies have shown that despite the potential of models in maize crop yield assessment, they have not been effectively used in understanding seasonal and annual production dynamics. Thus, stakeholders require the availability of accurate and timely data on maize production potential and hence the development and application of crop yield models for maize yield estimation. However, current methods of assessing maize crop yields are based on field assessments, which are expensive, laborious and inaccurate. This mixed methods paper, therefore, aimed to; (i) review information sources for maize crop yield assessments, looking at their strengths, limitations, and potential for application in sub-Saharan Africa, (ii) perform trend and distribution analyses of publications in maize crop yield simulation, and (iii) discuss the challenges in the application of models in agriculture planning in the African agriculture systems

Terrestrial plant productivity and soil moisture constraints

Dolman, A.J. [Promotor]Jeu, R.M.H. de [Copromotor]Werf-, G.R. van der [Copromotor

IMPROVING DECISION SUPPORT TOOLS VIA INTEGRATION OF REMOTELY SENSED DATA IN CROP MODELS

La necessit\ue0 di garantire l\u2019accesso al cibo per una popolazione mondiale in continua crescita, adempiendo al contempo a precisi requisiti ambientali, rappresenta una grande sfida per il settore agroalimentare. Il successo, in questa sfida, pu\uf2 essere garantito da un utilizzo ottimale delle risorse aziendali, raggiungibile attraverso sforzi notevoli volti al monitoraggio e all\u2019analisi del sistema agricolo. I recenti sviluppi in campo satellitare e modellistico, forniscono strumenti adatti allo scopo specialmente in caso di un utilizzo integrato delle due tecnologie. Il progetto di ricerca, oggetto di questa tesi, mira alla formalizzazione e successiva valutazione di uno strumento, basato sull\u2019integrazione di modelli di simulazione e telerilevamento, per il supporto alle decisioni in agricoltura. L\u2019attivit\ue0 di ricerca, culminata nell\u2019applicazione dello strumento al caso studio, ha previsto la realizzazione di due attivit\ue0 preliminari. In prima istanza \ue8 stato eseguito uno studio di analisi di sensibilit\ue0 per accrescere la conoscenza del modello utilizzato e identificare i parametri, alla cui variazione, il modello risulta pi\uf9 sensibile. In questo studio sono state considerate sia serie climatiche attuali che proiezioni nel medio futuro e, utilizzando due diverse configurazioni del modello WOFOST (una standard ed una che permette di simulare l\u2019impatto degli eventi meteorologici estremi sulle colture), sono state simulate le cinque colture pi\uf9 coltivate in Europa. I risultati evidenziano una forte sensibilit\ue0 del modello ai parametri coinvolti nella simulazione degli organi di accumulo in quasi tutte le condizioni esplorate a meno dei casi in cui sono state riscontrate condizioni limitanti per la produttivit\ue0 delle colture. In queste condizioni il modello \ue8 infatti risultato pi\uf9 sensibile ai parametri che regolano la simulazione delle prime fasi di crescita delle colture. La seconda attivit\ue0 preliminare ha invece permesso di quantificare l\u2019impatto della componente soggettiva sulla precisione delle stime di indice di area fogliare (LAI), una variabile tra le pi\uf9 utilizzate per permettere l\u2019integrazione di modelli di simulazione e telerilevamento. Attraverso l\u2019applicazione del protocollo previsto dalla normativa ISO 5725 \ue8 stato possibile calcolare i limiti di ripetibilit\ue0 e riproducibilit\ue0 delle stime di LAI da immagini emisferiche e quindi ottenere una misura della loro precisione. I risultati ottenuti, dimostrano l\u2019affidabilit\ue0 della tecnica seguita per ottenere stime di LAI; la precisione ottenuta \ue8 stata infatti comparabile a quella che caratterizza altri strumenti in commercio. I risultati migliori sono stati ottenuti in caso di coperture vegetali continue ed omogenee, caratteristiche dei sistemi agricoli intensivi, sottolineando ulteriormente l\u2019affidabilit\ue0 di tale tecnica in questi contesti. Entrambe le attivit\ue0, qui brevemente riassunte, hanno permesso di definire un valido punto di partenza per l\u2019integrazione di modellistica e telerilevamento fornendo informazioni utili per la progettazione e la realizzazione del caso studio. In questa ultima attivit\ue0, un sistema di previsione ad alta risoluzione basato sull\u2019integrazione di modellistica e telerilevamento \ue8 stato formalizzato e quindi valutato utilizzando dati raccolti in risaia durante le annate 2014, 2015 e 2016. Il modello colturale WARM \ue8 stato integrato con serie temporali di LAI telerilevate, ricalibrando automaticamente quei parametri, identificati come i pi\uf9 influenti oppure strettamente legati alla simulazione del LAI. Il confronto dei risultati ottenuti adottando questo approccio con quelli ottenuti utilizzando solamente il modello colturale, ha permesso di evidenziare i miglioramenti nella stima della produttivit\ue0 del riso dovuti all\u2019integrazione di informazioni telerilevate. In generale la simulazione delle produttivit\ue0 del riso \ue8 risultata affetta da un ridotto RRMSE (13.8%), se confrontata con quella ottenuta usando solamente il modello (RRMSE = 15.7%). Inoltre l\u2019integrazione delle due tecnologie ad una elevata risoluzione spaziale (30 m 7 30 m), ha consentito di riprodurre la variabilit\ue0 interna di ciascun campo. I risultati ottenuti evidenziano la validit\ue0 del sistema proposto per la stima della produttivit\ue0 del riso ad un\u2019elevata risoluzione spaziale. Ci\uf2 detto, durante la valutazione del sistema sono emerse alcune criticit\ue0 legate ad incongruenze tra le variabili simulate e quelle telerilevate. Questi aspetti, cos\uec come la possibilit\ue0 di considerare altre colture e altri modelli di simulazione, pongono le basi per ricerche future.The need to fulfil sustainability requirements while increasing productions to feed the raising world\u2019s population represents a big challenge for the agricultural sector. To achieve this goal, improving management of resources at farm level is acknowledged as one of the most effective solutions. However, this requires intensive activities targeting cropping system monitoring and data processing. The advances in remote sensing and simulation technologies \u2013 especially when used in an integrated way \u2013 provide a valuable solution to support farmers and technicians in such a context. This research aims at setting up and evaluating pre-operational tools based on the integration of crop models and remotely sensed information to support decision making in cropping systems management. The research was articulated in two main preliminary activities before the application of crop models and remote sensing in a case study. Extensive sensitivity analysis experiments were performed to deepen the knowledge about model behaviour and to identify the most influential parameters for yield simulation. A wide range of conditions was investigated, considering both current weather and future climate projections, as well as five major crops cultivated in several European sites and using two different modelling solutions (the standard version of WOFOST and a version of the model improved for the simulation of the impact of extreme weather events). Model outputs were mainly influenced by parameters involved with storage organs development; nevertheless, in case limiting conditions were explored, simulations were influenced by parameters driving crop growth during early stages. Given leaf area index (LAI) data are those mostly used when crop models and remote sensing are integrated, the second activity targeted the quantification of the impact of subjectivity in LAI estimates from hemispherical images. Precision was determined via the application of the ISO 5725 validation protocol, thus leading to define repeatability and reproducibility limits. Results proved the reliability of LAI estimates from hemispherical images; the precision obtained was indeed comparable with that of other commercial instruments. The best results were obtained in case of high LAI and continuous canopy, further underlying the reliability of this method for intensive agricultural systems characterized by continuous and homogeneous canopies. Both the activities just presented aimed at defining a sound starting point for the coupling of crop models and remote sensing, providing useful information for the design of the case study. For this last activity, a high-resolution pre-operational system based on the WARM model and remotely sensed information was evaluated using observations from paddy rice fields during the seasons 2014, 2015 and 2016. The remotely sensed information, consisting in temporal series of LAI, were integrated in the model by automatically re-calibrating either parameters identified as the most influential or those strictly related with LAI dynamics. The system performances were compared with those obtained using the default parameterizations of the model. Results underlined the improvement in rice yield simulation after the integration of remotely sensed data, proving the reliability of the system. Overall, the simulation of rice yield was affected by a restrained RRMSE (13.8%), compared to the results obtained with the default model parameterizations (RRMSE = 15.7%). Moreover, the assimilation of remotely sensed information at high spatial resolution (30 m 7 30 m) led to satisfactorily describe the within-field yield variability. The obtained results make the proposed system a valuable solution to provide high-resolution estimates of rice productivity. Nonetheless, weakness were highlighted, related with some the inconsistencies between observed crop state variables and crop reflectance properties. This, as well as the possibility to consider other models and crops, lays the basis for further studies