Organic matter modeling at the landscape scale based on multitemporal soil pattern analysis using RapidEye data

This study proposes the development of a landscape-scale multitemporal soil pattern analysis (MSPA) method for organic matter (OM) estimation using RapidEye time series data analysis and GIS spatial data modeling, which is based on the methodology of Blasch et al. The results demonstrate (i) the potential of MSPA to predict OM for single fields and field composites with varying geomorphological, topographical, and pedological backgrounds and (ii) the method conversion of MSPA from the field scale to the multi-field landscape scale. For single fields, as well as for field composites, significant correlations between OM and the soil pattern detecting first standardized principal components were found. Thus, high-quality functional OM soil maps could be produced after excluding temporal effects by applying modified MSPA analysis steps. A regional OM prediction model was developed using four representative calibration test sites. The MSPA-method conversion was realized applying the transformation parameters of the soil-pattern detection algorithm used at the four calibration test sites and the developed regional prediction model to a multi-field, multitemporal, bare soil image mosaic of all agrarian fields of the Demmin study area in Northeast Germany. Results modeled at the landscape scale were validated at an independent test site with a resulting prediction error of 1.4 OM-% for the main OM value range of the Demmin study area

Pathways to wheat self-sufficiency in Africa

A growing urban population and dietary changes increased wheat import bills in Africa to 9% per year. Though wheat production in the continent has been increasing over the past decades, to varying degrees depending on regions, this has not been commensurate with the rapidly increasing demand for wheat. Analyses of wheat yield gaps show that there is ample opportunity to increase wheat production in Africa through improved genetics and agronomic practices. Doing so would reduce import dependency and increase wheat self-sufficiency at national level in many African countries. In view of the uncertainties revealed by the global COVID-19 pandemic, extreme weather events, and world security issues, national policies in Africa should re-consider the value of self-sufficiency in production of staple food crops, specifically wheat. This is particularly so for areas where water-limited wheat yield gaps can be narrowed through intensification on existing cropland and judicious expansion of rainfed and irrigated wheat areas. Increasing the production of other sources of calories (and proteins) should also be considered to reduce dependency on wheat imports

Organic Matter Modeling at the Landscape Scale Based on Multitemporal Soil Pattern Analysis Using RapidEye Data

This study proposes the development of a landscape-scale multitemporal soil pattern analysis (MSPA) method for organic matter (OM) estimation using RapidEye time series data analysis and GIS spatial data modeling, which is based on the methodology of Blasch et al. The results demonstrate (i) the potential of MSPA to predict OM for single fields and field composites with varying geomorphological, topographical, and pedological backgrounds and (ii) the method conversion of MSPA from the field scale to the multi-field landscape scale. For single fields, as well as for field composites, significant correlations between OM and the soil pattern detecting first standardized principal components were found. Thus, high-quality functional OM soil maps could be produced after excluding temporal effects by applying modified MSPA analysis steps. A regional OM prediction model was developed using four representative calibration test sites. The MSPA-method conversion was realized applying the transformation parameters of the soil-pattern detection algorithm used at the four calibration test sites and the developed regional prediction model to a multi-field, multitemporal, bare soil image mosaic of all agrarian fields of the Demmin study area in Northeast Germany. Results modeled at the landscape scale were validated at an independent test site with a resulting prediction error of 1.4 OM-% for the main OM value range of the Demmin study area

Multitemporale Bodenmusteranalyse zur Schätzung der organischen Substanz auf Ackerflächen mittels multispektraler Satellitendaten

Background: The increasing impact of population development and climate change on soils and their functions leads to a growing importance of soil information. Due to the high socioeconomic and ecological relevance of soils, qualitative and quantitative soil data (e.g. on organic matter content) at multiple scales are urgently required for land and resource management, soil protection strategies, and more specific tasks in precision agriculture. Therefore, remote sensing data analysis can provide innovative, inexpensive and rapid tools for soil property prediction. To avoid the disturbance of temporal patterns (caused by vegetation, land management), a multitemporal remote sensing approach with the advantage of potentially higher pattern stability is obligatory. For multitemporal pattern analysis of soils, high resolution multispectral remote sensing data (e.g., RapidEye satellite imagery) are the best option to obtain a suitably large data series of bare soil images. Objective: This research proposes an innovative, transferable and operational model – the Multitemporal Soil Pattern Analysis (MSPA) method – for the generation of user-friendly soil information maps for precision agriculture based on multitemporal remote sensing data analysis and GIS spatial data modelling. The primary research objective is to evaluate the usefulness of spatiotemporal static soil reflectance patterns derived from high resolution multispectral satellite imagery using the reflection signal from soils for precise functional organic matter soil maps at croplands. Data: For model generation and validation, as well as data interpretation, in total 82 RapidEye scenes were obtained. In addition comprehensive soil sampling and analysis (1017 soil surface mixed samples) were conducted at agrarian fields, located in the young morainic soil-landscape of north-eastern German lowlands. Methods: At a demonstration field (study area Demmin), the field-specific MSPA method based on static soil reflectance pattern and soil sampling data was developed, consisting of following steps: (1) Selection of best suitable datasets (bare soil images) out of satellite time-series using automated classification based on NDVI thresholds and phenology data; (2) Soil reflectance pattern detection using standardised principal component analysis; (3) Evaluation of spatiotemporal soil pattern stability using statistical per-pixel analysis; (4) Functional soil mapping based on statistical analysis and stepwise exclusion of temporal effects. For a multi-field landscape-scale MSPA version, the field-scale based MSPA method was testes at single fields and field composites with diverse physical-geographical location characteristics in the study area Demmin. On this basis, both a representative regional organic matter prediction model and multitemporal bare soil mosaics (covering the study area) were created. To evaluate the transferability of the MSPA method and the application potential of the regional organic matter prediction model “Demmin” to other agrarian fields of same soil-landscape, the multi-field landscape-scale MSPA version was applied to croplands of the Quillow catchment area. To cover a larger area of the young morainic soil-landscape, a transregional prediction model was developed. Results: The main findings of this research are i) the highly operational and transferable MSPA method based on spatiotemporal static soil reflectance pattern derived from RapidEye time series and ii) the applicable regional prediction model “Demmin” and the transregional model for precise organic matter estimation at croplands. Prediction models are based on the significant relationship between organic matter values and the soil pattern detecting first standardised principal components. The prediction model “Demmin” (R² = 0.69) and the transregional model (R² = 0.65) show a prediction accuracy of 1.3 OM-% (absolute RMSE). High-quality functional soil maps with low prediction errors to laboratory-analysed data could be produced after excluding temporal effects. The MSPA method meets several requirements of innovative soil property prediction methods, such as “cost-efficiency” (0.95 €/ha for farm sizes of 10,000 ha). Conclusions: In this study, the MSPA method combined with RapidEye data provide a high prediction accuracy of organic matter values, independent of the study area, its value range of organic matter, and applied local, regional or transregional prediction model type.Hintergrund: Die zunehmenden Auswirkungen der Bevölkerungsentwicklung und des Klimawandels auf Böden und ihre Funktionen führen zu einer wachsenden Bedeutung an Bodeninformationen. Aufgrund der hohen sozioökonomischen und ökologischen Relevanz von Böden sind qualitative und quantitative Bodendaten (z.B. organische Substanz) auf mehreren Maßstäben für die Landschafts- und Ressourcenplanung, Bodenschutzstrategien und für exaktere Aufgaben in der Präzisionslandwirtschaft dringend erforderlich. Hierfür kann die fernerkundliche Datenanalyse innovative, preiswerte und schnelle Instrumente zur Vorhersage von Bodeneigenschaften liefern. Um die Störung durch temporäre Muster (verursacht durch Vegetation und Landmanagement) zu vermeiden, ist ein multitemporaler Fernerkundungsansatz mit dem Vorteil potenziell höherer Musterstabilität obligatorisch. Für die mulitemporale Musteranalyse von Böden sind hochauflösende, multispektrale Fernerkundungsdaten (z.B. RapidEye Satellitenbilder) die beste Option, um eine genügend große Datenreihe von Bildern mit vegetationslosen Böden zu erhalten. Ziel: Diese Forschungsarbeit empfiehlt ein innovatives, übertragbares und operatives Model – die multitemporale Bodenmusteranalyse-Methode (engl. Multitemporal Soil Pattern Analysis; MSPA) – zur Erstellung von benutzerfreundlichen Bodeninformationskarten für die Präzisionslandwirtschaft basierend auf der Analyse von Fernerkundungsdaten und der GIS-Modellierung von raumbezogenen Daten. Das Hauptforschungsziel ist die Nutzenevaluierung von raumzeitlich stabilen Bodenreflektionsmustern, die von hochauflösenden multispektralen Satellitenbildern über das Reflexionssignal von Böden abgeleitet wurden, zur Erstellung von präzisen Bodenfunktionskarten zur organischen Bodensubstanz auf Ackerflächen. Daten: Zur Modellerstellung und -validierung sowie zur Dateninterpretation wurden insgesamt 82 RapidEye-Satellitenbildszenen erhalten. Darüber hinaus wurde eine umfangreiche Bodenprobenentnahme und -analyse (1017 Oberbodenmischproben) auf Ackerflächen durchgeführt, die sich in der Jungmoränenlandschaft der nordostdeutschen Tiefebene befinden. Methoden: Auf einem Demonstrationsfeld (Untersuchungsgebiet Demmin) wurde die feldspezifische MSPA-Methode basierend auf stabilen Bodenreflektionsmustern und Bodendaten aus folgenden Schritten entwickelt: (1) Auswahl der am besten geeigneten Datensätze (Bilder mit vegetationslosen Böden) aus der Satellitenbildzeitreihe mittels einer automatischen Klassifizierung anhand von NDVI-Schwellenwerte und Phänologiedaten; (2) Bodenmustererkennung unter Einsatz der standardisierten Hauptkomponentenanalyse; (3) Bewertung der raumzeitlichen Bodenmusterstabilität mittels statistischer Per-Pixel-Analyse; (4) Erstellung von Bodenfunktionskarten basierend auf statistischer Analyse und schrittweiser Exklusion von temporären Effekten. Für die Schaffung einer Mehrfeld-Landschaftsskala-MSPA-Version wurde die Feldskalen basierte MSPA-Methode auf Einzelfeldern und Feldverbunden unter Berücksichtigung der verschiedenen physisch-geographischen Standorteigenschaften im Untersuchungsgebiet Demmin getestet. Auf dieser Grundlage wurden sowohl ein repräsentatives regionales Vorhersagemodell für die organische Substanz als auch multitemporale Bild-Mosaike von vegetationslosen Böden (die das gesamte Untersuchungsgebiet abdecken) entwickelt. Um die Übertragbarkeit der MSPA-Methode und das Anwendungspotential des regionalen Vorhersagemodells „Demmin“ auf andere Ackerflächen der gleichen Bodenlandschaft zu bewerten, wurde die Mehrfeld-Landschaftsskala-MSPA-Version auf Ackerflächen im Quillow-Einzugsgebiet angewendet. Zur größeren Flächenabdeckung der Jungmoränenlandschaft wurde ein überregionales Vorhersagemodell erstellt. Ergebnisse: Die Hauptergebnisse dieser Forschungsarbeit sind i) die hoch operative und übertragbare MSPA-Methode basierend auf raumzeitlich stabilen Bodenreflektionsmustern, abgeleitet aus der RapidEye-Zeitreihe, und ii) das anwendbaren regionale Vorhersagemodell „Demmin“ sowie das überregionale Modell zur präzisen Schätzung der organischen Substanz auf Ackerflächen. Die Vorhersagemodelle stützen sich auf dem signifikanten Zusammenhang zwischen Werten der organischen Bodensubstanz und den Bodenmuster detektierenden ersten standardisierten Hauptkomponenten. Das Vorhersagemodell „Demmin“ (R² = 0.69) und das überregionale Model (R² = 0.65) zeigen eine Vorhersagegenauigkeit von 1.3 OM-% (absolute RMSE). Hochwertige Bodenfunktionskarten mit niedrigen Vorhersagefehlern gegenüber den im Labor gemessenen Daten konnten nach dem Ausgrenzen von temporären Effekten erzeugt werden. Die MSPA-Methode erfüllt mehrere Anforderungen an eine innovative Vorhersagemethode von Bodeneigenschaften, wie zum Beispiel „Kosteneffizienz“ (0.95 €/ha für Betriebsgrößen von 10,000 ha). Fazit: Die MSPA-Methode kombiniert mit RapidEye-Daten bietet in dieser Studie eine hohe Vorhersagegenauigkeit der organischen Substanz, unabhängig von dem Untersuchungsgebiet, seinem Wertebereich der organischen Substanz und dem angewendeten lokalen, regionalen oder überregionalen Vorhersagemodelltyp

Multi-temporal yield pattern analysis method for deriving yield zones in crop production systems

Easy-to-use tools using modern data analysis techniques are needed to handle spatio-temporal agri-data. This research proposes a novel pattern recognition-based method, Multi-temporal Yield Pattern Analysis (MYPA), to reveal long-term (> 10 years) spatio-temporal variations in multi-temporal yield data. The specific objectives are: i) synthesis of information within multiple yield maps into a single understandable and interpretable layer that is indicative of the variability and stability in yield over a 10 + years period, and ii) evaluation of the hypothesis that the MYPA enhances multi-temporal yield interpretation compared to commonly-used statistical approaches. The MYPA method automatically identifies potential erroneous yield maps; detects yield patterns using principal component analysis; evaluates temporal yield pattern stability using a per-pixel analysis; and generates productivity-stability units based on k-means clustering and zonal statistics. The MYPA method was applied to two commercial cereal fields in Australian dryland systems and two commercial fields in a UK cool-climate system. To evaluate the MYPA, its output was compared to results from a classic, statistical yield analysis on the same data sets. The MYPA explained more of the variance in the yield data and generated larger and more coherent yield zones that are more amenable to site-specific management. Detected yield patterns were associated with varying production conditions, such as soil properties, precipitation patterns and management decisions. The MYPA was demonstrated as a robust approach that can be encoded into an easy-to-use tool to produce information layers from a time-series of yield data to support management