Mapping the Spatial Distribution of Winter Crops at Sub-Pixel Level Using AVHRR NDVI Time Series and Neural Nets

For large areas, it is difficult to assess the spatial distribution and inter-annual variation of crop acreages through field surveys. Such information, however, is of great value for governments, land managers, planning authorities, commodity traders and environmental scientists. Time series of coarse resolution imagery offer the advantage of global coverage at low costs, and are therefore suitable for large-scale crop type mapping. Due to their coarse spatial resolution, however, the problem of mixed pixels has to be addressed. Traditional hard classification approaches cannot be applied because of sub-pixel heterogeneity. We evaluate neural networks as a modeling tool for sub-pixel crop acreage estimation. The proposed methodology is based on the assumption that different cover type proportions within coarse pixels prompt changes in time profiles of remotely sensed vegetation indices like the Normalized Difference Vegetation Index (NDVI). Neural networks can learn the relation between temporal NDVI signatures and the sought crop acreage information. This learning step permits a non-linear unmixing of the temporal information provided by coarse resolution satellite sensors. For assessing the feasibility and accuracy of the approach, a study region in central Italy (Tuscany) was selected. The task consisted of mapping the spatial distribution of winter crops abundances within 1 km AVHRR pixels between 1988 and 2001. Reference crop acreage information for network training and validation was derived from high resolution Thematic Mapper/Enhanced Thematic Mapper (TM/ETM+) images and official agricultural statistics. Encouraging results were obtained demonstrating the potential of the proposed approach. For example, the spatial distribution of winter crop acreage at sub-pixel level was mapped with a cross-validated coefficient of determination of 0.8 with respect to the reference information from high resolution imagery. For the eight years for which reference information was available, the root mean squared error (RMSE) of winter crop acreage at sub-pixel level was 10%. When combined with current and future sensors, such as MODIS and Sentinel-3, the unmixing of AVHRR data can help in the building of an extended time series of crop distributions and cropping patterns dating back to the 80s.JRC.H.4-Monitoring Agricultural Resource

UAV Remote Sensing for High-Throughput Phenotyping and for Yield Prediction of Miscanthus by Machine Learning Techniques

Miscanthus holds a great potential in the frame of the bioeconomy, and yield prediction can help improve Miscanthus’ logistic supply chain. Breeding programs in several countries are attempting to produce high-yielding Miscanthus hybrids better adapted to different climates and end-uses. Multispectral images acquired from unmanned aerial vehicles (UAVs) in Italy and in the UK in 2021 and 2022 were used to investigate the feasibility of high-throughput phenotyping (HTP) of novel Miscanthus hybrids for yield prediction and crop traits estimation. An intercalibration procedure was performed using simulated data from the PROSAIL model to link vegetation indices (VIs) derived from two different multispectral sensors. The random forest algorithm estimated with good accuracy yield traits (light interception, plant height, green leaf biomass, and standing biomass) using a VIs time series, and predicted yield using a peak descriptor derived from a VIs time series with 2.3 Mg DM ha−1 of the root mean square error (RMSE). The study demonstrates the potential of UAVs’ multispectral images in HTP applications and in yield prediction, providing important information needed to increase sustainable biomass production

Using Low Resolution Satellite Imagery for Yield Prediction and Yield Anomaly Detection

Low resolution satellite imagery has been extensively used for crop monitoring and yield forecasting for over 30 years and plays an important role in a growing number of operational systems. The combination of their high temporal frequency with their extended geographical coverage generally associated with low costs per area unit makes these images a convenient choice at both national and regional scales. Several qualitative and quantitative approaches can be clearly distinguished, going from the use of low resolution satellite imagery as the main predictor of final crop yield to complex crop growth models where remote sensing-derived indicators play different roles, depending on the nature of the model and on the availability of data measured on the ground. Vegetation performance anomaly detection with low resolution images continues to be a fundamental component of early warning and drought monitoring systems at the regional scale. For applications at more detailed scales, the limitations created by the mixed nature of low resolution pixels are being progressively reduced by the higher resolution offered by new sensors, while the continuity of existing systems remains crucial for ensuring the availability of long time series as needed by the majority of the yield prediction methods used today.JRC.H.4-Monitoring Agricultural Resource

UAV and field spectrometer based remote sensing for maize phenotyping, varietal discrimination and yield forecasting.

Doctoral Degree. University of KwaZulu-Natal, Pietermaritzburg.Maize is the major staple food crop in the majority of Sub-Saharan African (SSA) countries. However, production statistics (croplands and yields) are rarely measured, and where they are recorded, accuracy is poor because the statistics are updated through the farm survey method, which is error-prone and is time-consuming, and expensive. There is an urgent need to use affordable, accurate, timely, and readily accessible data collection and spatial analysis tools, including robust data extraction and processing techniques for precise yield forecasting for decision support and early warning systems. Meeting Africa’s rising food demand, which is driven by population growth and low productivity requires doubling the current production of major grain crops like maize by 2050. This requires innovative approaches and mechanisms that support accurate yield forecasting for early warning systems coupled with accelerated crop genetic improvement. Recent advances in remote sensing and geographical information system (GIS) have enabled detailed cropland mapping, spatial analysis of land suitability, crop type, and varietal discrimination, and ultimately grain yield forecasting in the developed world. However, although remote sensing and spatial analysis afforded us unprecedented opportunities for detailed data collection, their application in maize in Africa is still limited. In Africa, the challenge of crop yield forecasting using remote sensing is a daunting task because agriculture is highly fragmented, cropland is spatially heterogeneous, and cropping systems are highly diverse and mosaic. The dearth of data on the application of remote sensing and GIS in crop yield forecasting and land suitability analysis is not only worrying but catastrophic to food security monitoring and early warning systems in a continent burdened with chronic food shortages. Furthermore, accelerated crop genetic improvement to increase yield and achieve better adaptation to climate change is an issue of increasing urgency in order to satisfy the ever-increasing food demand. Recently, crop improvement programs are exploring the use of remotely sensed data that can be used cost-effectively for varietal evaluation and analysis in crop phenotyping, which currently remains a major bottleneck in crop genetic improvement. Yet studies on evaluation of maize varietal response to abiotic and biotic stresses found in the target production environments are limited. Therefore, the aim of this study was to model spatial land suitability for maize production using GIS and explore the potential use of field spectrometer and unmanned aerial vehicles (UAV) based remotely sensed data in maize varietal discrimination, high-throughput phenotyping, and yield prediction. Firstly, an overview of major remote-sensing platforms and their applicability to estimating maize grain yield in the African agricultural context, including research challenges was provided. Secondly, maize land suitability analysis using GIS and analytical hierarchical process (AHP) was performed in Zimbabwe. Finally, the utility of proximal and UAV-based remotely sensed data for maize phenotyping, varietal discrimination, and yield forecasting were explored. The results showed that the use of remote sensing data in estimating maize yield in the African agricultural systems is still limited and obtaining accurate and reliable maize yield estimates using remotely sensed data remains a challenge due to the highly fragmented and spatially heterogeneous nature of the cropping systems. Our results underscored the urgent need to use sensors with high spatial, temporal and spectral resolution, coupled with appropriate classification techniques and accurate ground truth data in estimating maize yield and its spatiotemporal dynamics in heterogeneous African agricultural landscapes for designing appropriate food security interventions. In addition, using modern spatial analysis tools is effective in assessing land suitability for targeting location-specific interventions and can serve as a decision support tool for policymakers and land-use planners regarding maize production and varietal placement. Discriminating maize varieties using remotely sensed data is crucial for crop monitoring, high throughput phenotyping, and yield forecasting. Using proximal sensing, our study showed that maize varietal discrimination is possible at certain phenological growth stages at the field level, which is crucial for yield forecasting and varietal phenotyping in crop improvement. In addition, the use of proximal remote sensing data with appropriate pre-processing algorithms such as auto scaling and generalized least squares weighting significantly improved the discrimination ability of partial least square discriminant analysis, and identify optimal spectral bands for maize varietal discrimination. Using proximal sensing was not only able to discriminate maize varieties but also identified the ideal phenological stage for varietal discrimination. Flowering and onset of senescence appeared to be the most ideal stages for accurate varietal discrimination using our data. In this study, we also demonstrated the potential use of UAV-based remotely sensed data in maize varietal phenotyping in crop improvement. Using multi-temporal UAV-derived multispectral data and Random Forest (RF) algorithm, our study identified not only the optimal bands and indices but also the ideal growth stage for accurate varietal phenotyping under maize streak virus (MSV) infection. The RF classifier selected green normalized difference vegetation index (GNDVI), green Chlorophyll Index (CIgreen), Red-edge Chlorophyll Index (CIred-edge), and the Red band as the most important variables for classification. The results demonstrated that spectral bands and vegetation indices measured at the vegetative stage are the most important for the classification of maize varietal response to MSV. Further analysis to predict MSV disease and grain yield using UAV-derived multispectral imaging data using multiple models showed that Red and NIR bands were frequently selected in most of the models that gave the highest prediction precision for grain yield. Combining the NIR band with Red band improved the explanatory power of the prediction models. This was also true with the selected indices. Thus, not all indices or bands measure the same aspect of biophysical parameters or crop productivity, and combining them increased the joint predictive power, consequently increased complementarity. Overall, the study has demonstrated the potential use of spatial analysis tools in land suitability analysis for maize production and the utility of remotely sensed data in maize varietal discrimination, phenotyping, and yield prediction. These results are useful for targeting location-specific interventions for varietal placement and integrating UAV-based high-throughput phenotyping systems in crop genetic improvement to address continental food security, especially as climate change accelerates

Développement et validation d’un indice de production des prairies basé sur l’utilisation de séries temporelles de données satellitaires : application à un produit d’assurance en France

Une assurance indicielle est proposée en réponse à l'augmentation des sécheresses impactant les prairies. Elle se base sur un indice de production fourragère (IPF) obtenu à partir d'images satellitaires de moyenne résolution spatiale pour estimer l'impact de l'aléa dans une zone géographique définie. Le principal enjeu lié à la mise en place d'une telle assurance réside dans la bonne estimation des pertes subies. Les travaux de thèse s’articulent autour de deux objectifs : la validation de l'IPF et la proposition d'amélioration de cet indice. Un protocole de validation est construit pour limiter les problèmes liés à l'utilisation de produit de moyenne résolution et au changement d’échelle. L'IPF, confronté à des données de référence de différentes natures, montre de bonnes performances : des mesures de production in situ (R² = 0,81; R² = 0,71), des images satellitaires haute résolution spatiale (R² = 0,78 - 0,84) et des données issues de modélisation (R² = 0,68). Les travaux permettent également d'identifier des pistes d'amélioration pour la chaîne de traitement de l'IPF. Un nouvel indice, basé sur une modélisation semiempirique combinant les données satellitaires avec des données exogènes relatives aux conditions climatiques et à la phénologie des prairies, permet d'améliorer la précision des estimations de production de 18,6 %. L’ensemble des résultats obtenus ouvrent de nombreuses perspectives de recherche sur le développement de l'IPF et ses potentiels d'application dans le domaine assurantiel