Pre-crop Values from Satellite Images for Various Previous and Subsequent Crop Combinations

Monocultural land use challenges sustainability of agriculture. Pre-crop value indicates the benefits of a previous crop for a subsequent crop in crop sequencing and facilitates diversifi-cation of agricultural systems. Traditional field experiments are resource intensive and evaluate pre-crop values only for a limited number of previous and subsequent crops. We deve-loped a dynamic method based on Sentinel-2 derived Norma-lized Difference Vegetation Index (NDVI) values to estimate pre-crop values on a field parcel scale. The NDVI-values were compared to the region specific 90th percentile of each crop and year and thereby, an NDVI-gap was determined. The NDVI-gaps for each subsequent crop in the case of mo-nocultural crop sequencing were compared to that for other previous crops in rotation and thereby, pre-crop values for a high number of previous and subsequent crop combinations were estimated. The pre-crop values ranged from +16% to -16%. Especially grain legumes and rapeseed were valuable as pre-crops, which is well in line with results from field expe-riments. Such data on pre-crop values can be updated and expanded every year. For the first time, a high number of previous and following crop combinations, originating from farmer’s fields, is available to support diversification of cur-rently monocultural crop sequencing patterns in agriculture

Scalable Crop Yield Prediction with Sentinel-2 Time Series and Temporal Convolutional Network

One of the precepts of food security is the proper functioning of the global food markets. This calls for open and timely intelligence on crop production on an agroclimatically meaningful territorial scale. We propose an operationally suitable method for large-scale in-season crop yield estimations from a satellite image time series (SITS) for statistical production. As an object-based method, it is spatially scalable from parcel to regional scale, making it useful for prediction tasks in which the reference data are available only at a coarser level, such as counties. We show that deep learning-based temporal convolutional network (TCN) outperforms the classical machine learning method random forests and produces more accurate results overall than published national crop forecasts. Our novel contribution is to show that mean-aggregated regional predictions with histogram-based features calculated from farm-level observations perform better than other tested approaches. In addition, TCN is robust to the presence of cloudy pixels, suggesting TCN can learn cloud masking from the data. The temporal compositing of information do not improve prediction performance. This indicates that with end-to-end learning less preprocessing in SITS tasks seems viable

A Clustering Framework for Monitoring Circadian Rhythm in Structural Dynamics in Plants From Terrestrial Laser Scanning Time Series

Terrestrial Laser Scanning (TLS) can be used to monitor plant dynamics with a frequency of several times per hour and with sub-centimeter accuracy, regardless of external lighting conditions. TLS point cloud time series measured at short intervals produce large quantities of data requiring fast processing techniques. These must be robust to the noise inherent in point clouds. This study presents a general framework for monitoring circadian rhythm in plant movements from TLS time series. Framework performance was evaluated using TLS time series collected from two Norway maples (Acer platanoides) and a control target, a lamppost. The results showed that the processing framework presented can capture a plant's circadian rhythm in crown and branches down to a spatial resolution of 1 cm. The largest movements in both Norway maples were observed before sunrise and at their crowns' outer edges. The individual cluster movements were up to 0.17 m (99th percentile) for the taller Norway maple and up to 0.11 m (99th percentile) for the smaller tree from their initial positions before sunset

Land use optimization tool for sustainable intensification of high-latitude agricultural systems

Recent studies assessing agricultural policies, including the EU’s Agri-Environment Scheme, have shown that these have been successful in attaining some environmental goals. In Finland, however, the economic situation of farms has dramatically fallen and hence, the actions do not result in social acceptability. Sustainable intensification is a means to combine the three dimensions of sustainability: environmental, economic and social. Here we introduce a novel land use optimization and planning tool for the sustainable intensification of high-latitude agricultural systems. The main rationale for the development of the tool was to achieve a systematic and comprehensive conception for land allocation across Finland, where field parcels vary substantially in their conditions. The developed tool has a three-step scoring system based on seven physical characteristics (parcel size, shape, slope, distance to the farm center and waterways, soil type and logistic advantages) and the productivity of field parcels. The productivity estimates are based on vegetation indices derived from optical satellite data. The tool allocates virtually all >1 million field parcels in Finland either to sustainable intensification, extensification or afforestation. The tool is dynamic in the sense that its boundary values for land allocation can be fixed according to changes in social targets and supporting policies. Additionally, it can be applied year after year by acknowledging new available data, e.g., on vegetation indices and field parcel rearrangements between farms. Furthermore, it can be applied to all farm types and across Finland. It is a tool for land use planning, implementation and monitoring, but its thorough implementation calls for further development of policy instruments, which are currently more supportive towards land sharing than land sparing activities

Crop loss identification at field parcel scale using satellite remote sensing and machine learning

Identifying crop loss at field parcel scale using satellite images is challenging: first, crop loss is caused by many factors during the growing season; second, reliable reference data about crop loss are lacking; third, there are many ways to define crop loss. This study investigates the feasibility of using satellite images to train machine learning (ML) models to classify agricultural field parcels into those with and without crop loss. The reference data for this study was provided by Finnish Food Authority (FFA) containing crop loss information of approximately 1.4 million field parcels in Finland covering about 3.5 million ha from 2000 to 2015. This reference data was combined with Normalised Difference Vegetation Index (NDVI) derived from Landsat 7 images, in which more than 80% of the possible data are missing. Despite the hard problem with extremely noisy data, among the four ML models we tested, random forest (with mean imputation and missing value indicators) achieved the average AUC (area under the ROC curve) of 0.688±0.059 over all 16 years with the range [0.602, 0.795] in identifying new crop-loss fields based on reference fields of the same year. To our knowledge, this is one of the first large scale benchmark study of using machine learning for crop loss classification at field parcel scale. The classification setting and trained models have numerous potential applications, for example, allowing government agencies or insurance companies to verify crop-loss claims by farmers and realise efficient agricultural monitoring

A Clustering Framework for Monitoring Circadian Rhythm in Structural Dynamics in Plants from Terrestrial Laser Scanning Time Series

Terrestrial Laser Scanning (TLS) can be used to monitor plant dynamics with a frequency of several times per hour and with sub-centimeter accuracy, regardless of external lighting conditions. TLS point cloud time series measured at short intervals produce large quantities of data requiring fast processing techniques. These must be robust to the noise inherent in point clouds. This study presents a general framework for monitoring circadian rhythm in plant movements from TLS time series. Framework performance was evaluated using TLS time series collected from two Norway maples (Acer platanoides) and a control target, a lamppost. The results showed that the processing framework presented can capture a plant's circadian rhythm in crown and branches down to a spatial resolution of 1 cm. The largest movements in both Norway maples were observed before sunrise and at their crowns' outer edges. The individual cluster movements were up to 0.17 m (99th percentile) for the taller Norway maple and up to 0.11 m (99th percentile) for the smaller tree from their initial positions before sunset

Charakterisierung von B-Lymphozyten als antigen-präsentierende Zellen für die in vitro Stimulation von T-Lymphozyten

B-Lymphozyten nehmen in der Immunantwort eine zentrale Stellung ein. Neben ihrer Rolle als Produzenten von Antikörpern sind sie auch wichtige Antigen-präsentierende Zellen (APZ). Anders als andere APZ können B-Zellen über ihren B-Zell-Rezeptor (BZR) spezifisch Antigene aus der Umgebung aufnehmen, um sie innerhalb der Zelle zu prozessieren und anschließend auf der Zelloberfläche mit Hilfe von MHC-II-Molekülen T-Helfer-Zellen zu präsentieren. Dies führt zur Stimulation von Antigen-spezifischen T-Zellen, welche durch die Expression von Aktivierungsmarkern im Durchflusszytometer detektiert werden können. In dieser Arbeit sollten menschliche B-Zellen in Bezug auf ihre Nutzbarkeit für die Auslösung spezifischer T-Zell-Antworten in vitro untersucht werden. Dazu wurde vor allem ihre Fähigkeit zur Antigenpräsentation untersucht. Diese Untersuchungen dienten einerseits dem Ziel, Methoden zur Detektion Antigen-spezifischer T-Zell-Antworten zu verbessern, andererseits sollten auch Methoden zur Beurteilung der B-Zell-Funktion entwickelt werden. Im Rahmen dieser Arbeit wurden B-Lymphozyten mit Modellantigenen beladen und ihre Fähigkeit getestet, T-Lymphozyten aus dem gleichen Spender zu aktivieren. Dazu wurden B- und T-Lymphozyten aus Vollblutproben oder Leukozytenkonzentraten isoliert. Als Modellantigene dienten das immundominante pp65-Antigen des humanen Cytomegalievirus (CMV) und der Grippeimpfstoff Vaxigrip. Um die B-Lymphozyten mit spezifischen Antigenen zu beladen, wurden diese an kleine (2μm) fluoreszierende Latex-Beads gekoppelt, die von B-Zellen internalisiert werden können und durch ihre Eigenfluoreszenz auch nach der Internalisierung im Durchflusszytometer nachgewiesen werden können. Um die Aufnahme der Beads durch B-Zellen zu vermitteln wurden die Beads außerdem mit Antikörpern gegen den BZR beschichtet. Es konnte gezeigt werden, dass die Beschichtung mit anti-BZR-Antikörpern notwendig und ausreichend ist, um eine Aufnahme der Beads in die B-Zellen zu vermitteln. Weiterhin konnte gezeigt werden, dass Antigen-beschichtete Beads, die in B-Zellen eingebracht werden, eine gute Methode darstellen, Antigen-spezifische T-Zell-Antworten in vitro auszulösen.B cells play a central part in the immune response. Besides their role as producers of antibodies, they are also important antigen-presenting cells (APCs). Unlike other APCs, B cells can specifically take up antigens from the environment via their B cells receptors (BCRs), process them, and present them to T helper cells. This causes the activation of antigen specific T cells that can be detected on the basis of their expression of surface activation markers by flow cytometry. The goal of this study was to evaluate the potential of using human B cells as APCs to elicit antigen-specific T-cell responses in vitro. We therefore studied the antigen-presenting capacity of B lymphocytes. The first aim of these studies was to improve diagnostic methods for detecting antigen-specific T cell responses in vitro. A further aim was to develop a method to evaluate the B cell function of antigen presentation in a diagnostic setting. In this study human B lymphocytes were loaded with model antigens, and their capacity to activate T lymphocytes from the same donor was tested. To this end, B and T lymphocytes were isolated from whole blood samples or leucocyte concentrates. The immunodominant pp65 protein of the human cytomegaly virus (CMV), as well as the influenza vaccine Vaxigrip were used as model antigens. These antigens were coupled to small (2μm) fluorescent latex microbeads that could be internalized by B cells, and that could be detected by flow cytometry even following their internalization. in order to facilitate the uptake of the microbeads by B cells, the beads were also coated with antibodies specific for the BCR. The results show that the coating of beads with anti-BCR antibodies is necessary and sufficient to promote uptake by B lymphocytes. Furthermore, it could be shown that the uptake of antigen-coated beads by B lymphocytes constitutes a good method for eliciting antigen-specific T cell responses in vitro

CropYield—Towards pre-harvest crop yield forecasts with satellite remote sensing : Final report

The general objective of this project was to enhance the crop statistics. To this end, we established a pilot case for an automated process for improved crop yield statistics by merging Earth observation (EO) data, the administrative data, agro-meteorological data and historical crop statistics survey data. The significance of the approach is that the previously very laborious data acquisition process from different sources and the processing of multistep modelling is now by design fully automated and can thus reduce spending on professional surveying. The main achievement is that a new artificial intelligence-based crop yield forecasting system can produce pre-harvest yield predictions for four main cereals (oats, barley, wheat, rye). Surveys are very costly in terms of time and expense. The same is true of gathering expert estimates on regional crop yield forecasts. During the last decade, EO systems have been shown to provide an effective means for large-scale crop monitoring and yield estimations. In this sense, this project has fulfilled its promise to establish a pilot case for an automated process of improved crop yield statistics by merging EO data and a data-driven modelling approach. As a result, we can produce several in-season crop yield forecasts, the first already in late June, around the same time as the Joint Research Centre’s European-wide forecast. From then on, the forecasts can be published, for example, at 10 day-intervals. The machine learning models implemented in this project achieved a highly promising level of accuracy in pre-harvest yield predictions for four main cereals (oats, barley, wheat, and rye) when compared to the Joint Research Centre’s and LUKE’s seasonal forecasts. However, the problem of choosing the best model remains. There was no clear winning model that reliably predicts yields at all times. Therefore, a model comparison will be the most important developmental task ahead. In the context of agricultural statistics, more accurate in-season forecasts of crop yields benefit sustainable agriculture and food security with better informed political decisions. In addition, reliable crop forecasts have market impacts. Moreover, EO-based applications can be globally applicable. We expect that within a few years our EO-based crop forecasting will be proven to be a sound method to replace in-season regional expert estimates, and in the foreseeable future it will also gradually replace annual farm surveys. The uptake of EO as a new data source in statistical production was more complex than initially expected. There are a myriad of approaches to monitoring crop yields, the main decisions to make being whether to utilise: i) optical or radar satellite data or both, ii) image mosaics or single images, iii) pixel-based or object-based image analysis. In addition, remote sensing requires specialized expertise, not to mention the specialized expertise needed in predictive modelling. We acknowledged the lack of remote sensing expertise and made a decision at the start to outsource the pre-processing of satellite images. With outsourcing the sustainability of the project may be jeopardized if the know-how outsourced cannot be fully transferred to the statistical production. In this sense, one significant achievement in the project has been the uptake of EO knowledge, with a substantial contribution from the National Land Survey of Finland, which became a sound part of our production system. As a result, we have the in-house readiness to apply EO as a new data source also to other statistical themes. It was concluded that country-wide forecasts seemed to work already in June, probably due to the inherited sampling weights from the crop production surveys. However, for the regional forecasts the sampling data was inadequate. For regional forecasts, we would need to sample fields to gain an equal spatial coverage. Moreover, for the northern regions the crop forecasting is reasonable only from July on due to the later sowing dates. Therefore, further study is needed to evaluate the best physiologically grounded observation window for each region. Deploying the forecasting pipeline requires further automatization. Especially at the end of the pipeline the validation of the results needs further scrutiny. Uploading the predictions to statistical production databases requires modifications to existing ICT-systems. In addition, prediction model architectures need to be revised and improved along with the new data from the coming years.202

Comparison of two-dimensional multitemporal Sentinel-2 data with three-dimensional remote sensing data sources for forest inventory parameter estimation over a boreal forest

National Forest Inventories (NFI) are key data and tools to better understand the role of forests in the global carbon budget. Traditionally inventories have been carried out as field work, which makes them laborious and expensive. In recent years, the development of various remote sensing techniques to improve the cost-efficiency of the NFIs has accelerated. The goal of this study is to determine the usability of open and free multitemporal multispectral satellite images from the European Space Agency's Sentinel-2 satellite constellation and to compare their usability in forest inventories against airborne laserscanning (ALS) and three-dimensional data obtained with high-resolution optical satellite images from WorldView-2 and Synthetic Aperture Radar (SAR) stereo data from TerraSAR-X. Ground reference consisted of field data collected over 74 boreal forest plots in Southern Finland in 2014 and 2016. Features utilizing both single- and multiple-date information were designed and tested for Sentinel-2 data. Due to high cloud cover, only four Sentinel-2 images were available for the multitemporal feature analysis of all reference plots within the monitoring window. Random Forest technique was used to find the best descriptive feature sets to model five forest inventory parameters (mean height, mean diameter at breast height, basal area, volume, above-ground biomass) from all input remote sensing data. The results confirmed that the higher spatial resolution input data correlated with more accurate forest inventory parameter predictions, which is in line with other results presented in literature. The addition of temporal information to the Sentinel-2 results showed limited variation in prediction accuracy between the single and multidate cases ranging from 0.45 to 1.5 percentage points, whereof mean height, basal area and aboveground biomass are lower for single date with relative RMSEs of 14.07%, 20.66% and 24.71% respectively. Diameter at breast height and volume are lower for multi date feature combination withrelative RMSEs of 18.38% and 27.21%. The results emphasize the importance of obtaining more evenly distributed data acquisitions over the growing season to fully exploit the potential of temporal features.Peer reviewe

Peltomaan talviaikaisen kasvipeitteisyysalan arviointi satelliittikaukokartoituksella

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