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

Detecting Phenological Development of Winter Wheat and Winter Barley Using Time Series of Sentinel-1 and Sentinel-2

Monitoring the phenological development of agricultural plants is of high importance for farmers to adapt their management strategies and estimate yields. The aim of this study is to analyze the sensitivity of remote sensing features to phenological development of winter wheat and winter barley and to test their transferability in two test sites in Northeast Germany and in two years. Local minima, local maxima and breakpoints of smoothed time series of synthetic aperture radar (SAR) data of the Sentinel-1 VH (vertical-horizontal) and VV (vertical-vertical) intensities and their ratio VH/VV; of the polarimetric features entropy, anisotropy and alpha derived from polarimetric decomposition; as well as of the vegetation index NDVI (Normalized Difference Vegetation Index) calculated using optical data of Sentinel-2 are compared with entry dates of phenological stages. The beginning of stem elongation produces a breakpoint in the time series of most parameters for wheat and barley. Furthermore, the beginning of heading could be detected by all parameters, whereas particularly a local minimum of VH and VV backscatter is observed less then 5 days before the entry date. The medium milk stage can not be detected reliably, whereas the hard dough stage of barley takes place approximately 6–8 days around a local maximum of VH backscatter in 2018. Harvest is detected for barley using the fourth breakpoint of most parameters. The study shows that backscatter and polarimetric parameters as well as the NDVI are sensitive to specific phenological developments. The transferability of the approach is demonstrated, whereas differences between test sites and years are mainly caused by meteorological differences.BMEL, 2815710715, Verbundprojekt: Erzeugung von landwirtschaftlichen Ertragspotenzialkarten durch Fusion von Ertragskartierungen, Fernerkundungsdaten, digitaler Reliefauswer-tung und Bewirtschaftungsdaten (AgriFusion) - Teilprojekt

Suitability of satellite remote sensing data for yield estimation in northeast Germany

Information provided by satellite data is becoming increasingly important in the field of agriculture. Estimating biomass, nitrogen content or crop yield can improve farm management and optimize precision agriculture applications. A vast amount of data is made available both as map material and from space. However, it is up to the user to select the appropriate data for a particular problem. Without the appropriate knowledge, this may even entail an economic risk. This study therefore investigates the direct relationship between satellite data from six different optical sensors as well as different soil and relief parameters and yield data from cereal and canola recorded by the thresher in the field. A time series of 13 years is considered, with 947 yield data sets consisting of dense point data sets and 755 satellite images. To answer the question of how well the relationship between remote sensing data and yield is, the correlation coefficient r per field is calculated and interpreted in terms of crop type, phenology, and sensor characteristics. The correlation value r is particularly high when a field and its crop are spatially heterogeneous and when the correct phenological time of the crop is reached at the time of satellite imaging. Satellite images with higher resolution, such as RapidEye and Sentinel-2 performed better in comparison with lower resolution sensors of the Landsat series. The additional Red Edge spectral band also has advantage, especially for cereal yield estimation. The study concludes that there are high correlation values between yield data and satellite data, but several conditions must be met which are presented and discussed here

The Diversity of Heath Flowering Phenology– Revealing Fine Scale Patterns of Heterogeneity by High Resolution Drone Cameras

Unmanned aerial vehicles (UAV) open up new perspectives for the repetitive spatial monitoring of vegetation stands and possibly even for fine scale analyses of individual plants. The potentials of recent UAV camera systems for integrating high spatial resolution (< 10 cm per image pixel) and area wide mapping are undergoing research. In particular, robust algorithms are needed for a spatially explicit characterization of individual plants, their structural composition and related functional traits. Such information can be used to examine ecological interdependencies that determine habitat establishment, and in general, to evaluate the habitat quality for nature conservation purposes [1]. In our study we developed a UAV-based methodological procedure to analyse open heathland areas that are protected in the European Natura 2000 network, and therefore, need to be managed to preserve a favourable conservation status [2]. We mapped the flowering phase of regenerated dwarf shrub heath (Calluna vulgaris) stands 2 years after burning and of unmanaged old stands using a standard RGB drone camera. From the UAV imaging point cloud a digital orthophoto and a digital surface model was generated with 2 cm pixel resolution. A method was developed that extracts every individual plant on the basis of RGB-colour value classification, texture filtering and local maxima estimation on a normalized digital surface model. For every plant the maximum plant height as well as the RGB colour distribution could be extracted and related to field surveys of plant traits. On that basis, a colour model was constructed that statistically predicts the phenological heath plant status with regard to fractional cover of flowers, fruits, vegetative shoots and senescent plant components. Our study reveals that the flowering phase of Calluna vulgaris is spatially much more divers than expected. Colour models from simple RGB imagery show that phases of completely vegetative, withered, fruit and full flowering plants spatially coexist in heterogeneous alternations of individual plants, in fact, independent of growth age after burning (height 40 cm) exhibit significantly lower fractions of fruit and flowers while senescence is increased. However, the high phenological diversity after fire management shows that driving factors of shoot extension and flower productions, thus, are triggered by additional mechanisms that are not affected by habitat management practice. UAV-based imagery can help to observe such effects for a more detailed evaluation of management effects. REFERENCES: 1. Neumann C, Weiss G, Schmidtlein S, Itzerott S, Lausch A, Doktor D, Brell M., 2015. Gradient-based assessment of habitat quality for spectral ecosystem monitoring. Remote Sensing. 7(3), 2871-98. 2. Habitat Directive, 1992. Council Directive 92/43/EEC of 21 May 1992 on the conservation of natural habitats and of wild fauna and flora. Official Journal of the European Union. 206, 7-50

Durable Environmental Multidisciplinary Monitoring Information Network DEMMIN - Agricultural Research at the TERENO German Northeastern Lowlands Observatory

The Durable Environmental Multidisciplinary Monitoring Information Network (DEMMIN) is a calibration and Validation test site for remote sensing Technologies and derived Information products. The test site is located within the TERENO German Northeastern Lowlands Observatory. DEMMIN Objectives are: i) combination of in-situ data and remote sensing data analysis for: Crop parameter estimation (crop type, crop status, crop pattern) Soil parameter retrieval (soil moisture, organic matter) Evapotranspiration modelling, ii) high resolution data analysis (automatic data processing and analysis of multi sensor data (e.g. TSX, Sentinel-1 & 2, Landsat-8 + in-situ + modelling), and iii) Cal/Val site for new sensors and missions

Gradient-based assessment of habitat quality for spectral ecosystem monitoring

The monitoring of ecosystems alterations has become a crucial task in order to develop valuable habitats for rare and threatened species. The information extracted from hyperspectral remote sensing data enables the generation of highly spatially resolved analyses of such species’ habitats. In our study we combine information from a species ordination with hyperspectral reflectance signatures to predict occurrence probabilities for Natura 2000 habitat types and their conservation status. We examine how accurate habitat types and habitat threat, expressed by pressure indicators, can be described in an ordination space using spatial correlation functions from the geostatistic approach. We modeled habitat quality assessment parameters using floristic gradients derived by non-metric multidimensional scaling on the basis of 58 field plots. In the resulting ordination space, the variance structure of habitat types and pressure indicators could be explained by 69% up to 95% with fitted variogram models with a correlation to terrestrial mapping of >0.8. Models could be used to predict habitat type probability, habitat transition, and pressure indicators continuously over the whole ordination space. Finally, partial least squares regression (PLSR) was used to relate spectral information from AISA DUAL imagery to floristic pattern and related habitat quality. In general, spectral transferability is supported by strong correlation to ordination axes scores (R$^{2}$ = 0.79–0.85), whereas second axis of dry heaths (R$^{2}$ = 0.13) and first axis for pioneer grasslands (R$^{2}$ = 0.49) are more difficult to describe

Classification and monitoring of reed belts using dual-polarimetric TerraSAR-X time series

Synthetic aperture radar polarimetry (PolSAR) and polarimetric decomposition techniques have proven to be useful tools for wetland mapping. In this study we classify reed belts and monitor their phenological changes at a natural lake in northeastern Germany using dual-co-polarized (HH, VV) TerraSAR-X time series. The time series comprises 19 images, acquired between August 2014 and May 2015, in ascending and descending orbit. We calculated different polarimetric indices using the HH and VV intensities, the dual-polarimetric coherency matrix including dominant and mean alpha scattering angles, and entropy and anisotropy (normalized eigenvalue difference) as well as combinations of entropy and anisotropy for the analysis of the scattering scenarios. The image classifications were performed with the random forest classifier and validated with high-resolution digital orthophotos. The time series analysis of the reed belts revealed significant seasonal changes for the double-bounce–sensitive parameters (intensity ratio HH/VV and intensity difference HH-VV, the co-polarimetric coherence phase and the dominant and mean alpha scattering angles) and in the dual-polarimetric coherence (amplitude), anisotropy, entropy, and anisotropy-entropy combinations; whereas in summer dense leaves cause volume scattering, in winter, after leaves have fallen, the reed stems cause predominately double-bounce scattering. Our study showed that the five most important parameters for the classification of reed are the intensity difference HH-VV, the mean alpha scattering angle, intensity ratio HH/VV, and the coherence (phase). Due to the better separation of reed and other vegetation (deciduous forest, coniferous forest, meadow), winter acquisitions are preferred for the mapping of reed. Multi-temporal stacks of winter images performed better than summer ones. The combination of ascending and descending images also improved the result as it reduces the influence of the sensor look direction. However, in this study, only an accuracy of ~50% correct classified reed areas was reached. Whereas the shorelines with reed areas (&gt;10 m broad) could be detected correctly, the actual reed areas were significantly overestimated. The main source of error is probably the challenging data geocoding causing geolocation inaccuracies, which need to be solved in future studies

Classification and monitoring of reed belts using dual-polarimetric TerraSAR-X time series

Synthetic aperture radar polarimetry (PolSAR) and polarimetric decomposition techniques have proven to be useful tools for wetland mapping. In this study we classify reed belts and monitor their phenological changes at a natural lake in northeastern Germany using dual-co-polarized (HH, VV) TerraSAR-X time series. The time series comprises 19 images, acquired between August 2014 and May 2015, in ascending and descending orbit. We calculated different polarimetric indices using the HH and VV intensities, the dual-polarimetric coherency matrix including dominant and mean alpha scattering angles, and entropy and anisotropy (normalized eigenvalue difference) as well as combinations of entropy and anisotropy for the analysis of the scattering scenarios. The image classifications were performed with the random forest classifier and validated with high-resolution digital orthophotos. The time series analysis of the reed belts revealed significant seasonal changes for the double-bounce–sensitive parameters (intensity ratio HH/VV and intensity difference HH-VV, the co-polarimetric coherence phase and the dominant and mean alpha scattering angles) and in the dual-polarimetric coherence (amplitude), anisotropy, entropy, and anisotropy-entropy combinations; whereas in summer dense leaves cause volume scattering, in winter, after leaves have fallen, the reed stems cause predominately double-bounce scattering. Our study showed that the five most important parameters for the classification of reed are the intensity difference HH-VV, the mean alpha scattering angle, intensity ratio HH/VV, and the coherence (phase). Due to the better separation of reed and other vegetation (deciduous forest, coniferous forest, meadow), winter acquisitions are preferred for the mapping of reed. Multi-temporal stacks of winter images performed better than summer ones. The combination of ascending and descending images also improved the result as it reduces the influence of the sensor look direction. However, in this study, only an accuracy of ~50% correct classified reed areas was reached. Whereas the shorelines with reed areas (&gt;10 m broad) could be detected correctly, the actual reed areas were significantly overestimated. The main source of error is probably the challenging data geocoding causing geolocation inaccuracies, which need to be solved in future studies

Automatic delineation algorithm for site-specific management zones based on satellite remote sensing data

In light of the increasing demand for food production, climate change challenges for agriculture, and economic pressure, precision farming is an ever-growing market. The development and distribution of remote sensing applications is also growing. The availability of extensive spatial and temporal data—enhanced by satellite remote sensing and open-source policies—provides an attractive opportunity to collect, analyze and use agricultural data at the farm scale and beyond. The division of individual fields into zones of differing yield potential (management zones (MZ)) is the basis of most offline and map-overlay precision farming applications. In the process of delineation, manual labor is often required for the acquisition of suitable images and additional information on crop type. The authors therefore developed an automatic segmentation algorithm using multi-spectral satellite data, which is able to map stable crop growing patterns, reflecting areas of relative yield expectations within a field. The algorithm, using RapidEye data, is a quick and probably low-cost opportunity to divide agricultural fields into MZ, especially when yield data is insufficient or non-existent. With the increasing availability of satellite images, this method can address numerous users in agriculture and lower the threshold of implementing precision farming practices by providing a preliminary spatial field assessment