A new assembly for measuring surface reflectance in the field was developed and built to fill the gap between laboratory and field spectral measurements of the soil surface. This device, named SoilPRO (Soil field PRObe), can be connected to any field spectrometer fiber tip and used to measure representative and undisturbed surfaces of different soil types, low grass and artificial materials. Here, the SoilPRO is presented and compared to reflectance-measurement methods that are commonly used in the laboratory (dark box configuration and the ASD® contact probe) and the field (bare fiber handheld by pistol grip and ASD contact probe). The SoilPRO's performance was evaluated by conducting continuous tests in the laboratory and outdoors with soil samples, and in the field with five different surface types as test targets. The SoilPRO's reflectance products in the field were very similar to those of commonly used methods under optimal conditions, while eliminating most of their associated errors. The device provides a new approach to spectral data acquisition in the field that enables measuring undisturbed soil surfaces while maintaining constant and stable environmental and operational conditions. The SoilPRO measures the reflectance of a representative target's footprint across the spectrometer’s full range of sensitivity. The measurement is not dependent on the sun's radiation or atmospheric variations, or on operator stability or measurement geometry, and it does not disturb the surface being measured. Reflectance is an inherent property, and the assembly provides superb reflectance accuracy for the surface area in question. The SoilPRO assembly was examined under shadow orchard trees plots which suffer from hydrophobicity as a result of irrigation with secondary swage water. The SoiPRO assembly was able to derive a high quality spectral over this shaded area and together with exact measurements of the Water Drop Penetration Time (WDPT) values to proximate the hydrophobicity status of the soil. This measurement enable a better water management of the water, preventing runoff and by most of all- applying precaution treatments to the soil to reduce the HP values. This treatment increases the water penetration into the soil profile and effectively utilize the irrigation water for agriculture activity under arid climate region. The SoilPRO assembly is thus can be used as a tool to calibrate and validate hyperspectral data of undisturbed soils from field air and orbit.

High spatial and temporal soil information is crucial to analyse soil developments and for monitoring long term changes to avoid soil degradation. This information is essential to achieve sustainable food security, health and high productivity of soils. Naturally exposed soils in Europe occur rarely. Mainly agricultural regions can provide areas of exposed soil for short periods of time during a year. The Soil Composite Mapping Processor (SCMaP) is a fully automated approach to make use of per-pixel based bare-soil compositing to overcome the issue of limited soil exposure. It is based on the alternating behavior of a vegetation index and allows the automated generation of cloud-free multispectral soil reflectance composites that can support high temporal and large scale top soil analysis. Besides the multispectral soil reflectance composites two additional products are generated that include the distribution of exposed soils and statistical information related to soil use and intensity for time series (1984-2017) of five year periods based on multispectral Landsat (LT 4, 5, LE 7 and LC 8) imagery. A Validation of the distribution of exposed soils with statistical data from regional agricultural surveys showed a high plausibility for Germany.

SCMaP was developed and validated for the agriculture dominated country Germany. Different climatic conditions, land use and agricultural practices can have an influence on the extraction of the bare soil composites. Technically, the processor is applicable to all areas with agricultural activities. Thus, several European countries have been processed to test the transferability of the method to different climatic conditions and agricultural practices.

This work aims for 1) applying, investigating and adapting the required threshold definition of SCMaP for testing its applicability for different climatic regions in various European countries and 2) to present the resulting multitemporal products of the different time periods that have the potential to detect exposed soil changes on a wider spatial scale. First results show a realistic distribution of exposed soils in the different countries.

In future work, reflectance information from different European countries will be proved with validation data of field campaigns and airborne Earth Observation imagery with a higher spatial and spectral resolution. The composites undergo an in-depth validation routine to derive more detailed soil parameters for analysing retrospective soil developments with high spatial and temporal resolution to add information to existing soil maps.

So far the most state-of-the-art soil map (soil map 1:50’000) of the German federal state Baden-Wuerttemberg is based on conventional data sources as soil surveys, forest mapping, geological and topographical maps, and digital elevation models. However, the integration of remote sensing products as a complementary data source has attained little attention over the years due to various reasons (e.g., high costs for commercial products, low spatial and/ or temporal resolution). Nowadays with Sentinel-2 data, obtained within the Copernicus Mission, new multispectral images of the earth at a relatively high spatial resolution (up to 10 m) and a repetition rate of 5 days are made available free of charge. As such, Sentinel-2 data can be used as an additional database that delivers continuous information about the soil surface since 2015.

The aim is to elaborate the relationships of near-surface soil parameter (NSSP) and their spectral characteristics by fusing the multispectral Sentinel-2 data with analyzed soil samples (n=164) of arable lands in Baden-Wuerttemberg and hyperspectral field and laboratory spectrometer measurements. The soil sampling network is relatively dense with a mean distribution of 5.7 km x 5.7 km on arable land. Such NSSP are the contents of sand, silt, clay, organic carbon, and the presence of carbonates.
A correlation of measured NSSP and spectral characteristics in the spectrum of Sentinel-2 is to be expected. Based on the identified relationships of NSSP and spectral signatures, a proposed regression equation should be applicable to the total arable land within Sentinel-2 scenes to model the spatial distribution of NSSP. Mixed pixels first need to be unmixed to provide a clearer and unambiguous spectral signal. Additionally, the high repetition rate of Sentinel-2 enables to capture the influence of temporal dynamics as soil moisture on the reflectance and spectral signatures on NSSP. That information facilitates the interpretation of spectra regarding the overruling effect of water saturated NSSP that reduce the actual soil parameter signal.
Other soil data like the Land Use/Cover Area frame statistical Survey (LUCAS) can serve as an independent dataset to validate the derived NSSP of Sentinel 2. The objective is to integrate the obtained additional information about NSSP into the existing soil map of Baden-Wuerttemberg.

The results may demonstrate that the combination of traditional field surveys with remote sensing data can lead to a better understanding of soil properties and their interactions. Furthermore, the future integration of remote sensing data in soil maps enables to keep soil maps up-to-date and integrate additional information cost-effectively and time-saving.

One of the leading causes of land degradation has been attributed to soil erosion which has substantial impacts both on human and their surrounding environments including agricultural production, surface and ground water quality, human health to name a few. Thereby, soil erosion has been considered as a major threat to human stability. Estimation of the annual soil loss rate is critical as it helps facilitate land use planning and environmental protection policies. Soil erodibility, generally considered as soil sensitivity to erosion, is highly affected by different climatic, physical, hydrological, chemical, mineralogical and biological properties. Thereby, the estimation requires a multi-factor approach. Space-borne remote sensing shows promise to estimate rate of soil loss. In order to carry out such an analysis, the Revised Universal Soil Loss Equation (RUSLE) was promoted by Renard et al 1991. In a riverine country like Bangladesh, the quantifications of soil loss is very limited considering the complex nature of field techniques. In addition, to our knowledge there is no baseline study existing in Bangladesh with particular focus on soil erosion in any part of the country. Therefore, this study aims to quantify the soil loss rate in Jamuna River sub-basin area that accommodates about 3 million people (BBS, 2011). The study area is limited within Bangladesh country border of the river basin area only.

In this study we use the Revised Universal Soil Loss Equation (RUSLE) in a GIS environment to estimate the annual soil loss in the Jamuna Basin. The factors that have been taken into consideration includes:
Rainfall-runoff erosivity factor that has been determined by the amount of annual rainfall per month in the study area
Soil erodibility factor that has been determined by the percentage composition of the soil in the study area
Slope length factor and slope steepness factors have been determined from Digital Elevation Model
Cover-management factor is determined by using the Normalized Difference Vegetation Index (NDVI)
Support practice factor has been assumed as 1.

Landsat TM, OLI and MSS sensor images were used in the study and indices such as NDVI (Normalized Difference Vegetation Index) was applied on the images. Landsat images were pre-processed using various techniques namely atmospheric correction, cloud removal, geometric and radiometric corrections in order to ensure that there were no or less distortions in the image and also it was ensured that the image doesn’t have any missing data values. ASTER Digital Elevation Model was used in order to determine the slope properties and other hydrological characteristics of the basin. Soil properties data was obtained from Bangladesh Agriculture Research Council (BARC) and the daily rainfall data were obtained from Bangladesh Methodological Department (BMD).

Validation of the results was difficult as there are no such database for the rate of soil loss in Bangladesh. From the RUSLE analysis conducted in GIS environment, the average annual soil loss rate for the Jamuna sub-basin was calculated to be 61 tons/ha/year. As there are no micro- level datasets for the annual soil loss in the Jamuna basin, the study used national average rate of soil loss from global dataset. According to the Global Land Degradation Information System (Nachtergaele 2010), the rate of soil erosion is estimated to be 50-60 tons/ha/year. Therefore, our study results are coinciding with the aforementioned Analysis of the parameters showed that the areas with the lowest cover management factor also had high rates of erosion. It was observed that 4 out of 9 unions with erosion rates greater than 380 tons/ha/year also had lowest cover management factor. A Union based analysis was conducted for the study area and it was observed that Dhanua and Rajnagar unions are the two unions with highest rate of erosion present in areas with the high rainfall erossivity factor. Dhanua union was identified as the union with the highest Soil erodibility factor and thus it has a rate of erosion greater than 380 ton/ha/year.The study being the pioneer in estimation of annual soil loss in Bangladesh leaves a profound impact on the policy makers who are making plans for Jamuna Basin management. The study shows that the Jamuna Basin is very susceptible due to soil erosion and therefore, this has impacts on the amounts of sediments that is being added to the Jamuna River system directly and from adjoin creeks and streams. Therefore, we believe that the outcome of our study may be used for overall Jamuna Basin management strategies. This will help improve management practices to help reduce excessive soil loss.

Apuan Alps ecosystem, landscape and environment are affected by increasing marble mining activities from last decades: since 2016 the Tuscan Regional Government has launched a special project to enforce both sustainable development of new mining activities and a deeper control of existing ones.
The Tuscan Regional Environmental Agency (ARPAT) Information System is involved in implementing Remote Sensing and UAV surveys integration techniques for precise automatic land cover changes detection due to marble extraction: in this paper a new classifier is proposed to find both 2d and 3d changes based on high resolution optical, lidar and radar data.
Following a preliminary check of free Sentinel-2/1 data yearly change detection capabilities, which showed poor agreement with a priori knowledge originated by UAV survey and technical documentation between 2016 and 2017, both Pleiades satellite images and Cosmo Skymed SAR archived data have been chosen in order to detect small soil changes (<10m): other available data (lidar aerial acquisitions and aerial photos) have been acquired in order to check - and eventually increase - the precision of the automatic change detection procedure.
Processing of available data showed that supervised classification of Pleiades optical images are well suitable for discriminating vegetated areas from rocks and quarry areas, thus not allowing discrimination of quarry areas details - such as roads and production areas - which can be better isolated by using 3d data (slope <25% for street and production places, slope >75% for production edges): a precise knowledge of excavation zones changes is a key point to ensure an effective monitoring of the progress of authorized activities, while road changes may affect water management planes.
In addition to 3d data contribution, texture parameters can be used to distinguish roads from active quarry areas, allowing the exploitation of the high resolution pancreatic band (50cm) of Pleiades images: all the available informations - spectral response, textures parameters, lidar data - have been taken into account to improve classification quality.
Three Multiband images - taken in 2016, 2017 and 2018 - have been built by assembling RGBA Pleiades bands (pansharpened), Structured Feature Set extracted from Pleiades pancromatic bands (6 features bands), slope extracted by lidar surveys: all the three images have been classified by using traditional supervised methods, and extracted vector features have been compared in order to detect changes in production areas.
The classifier has been tested by using a 4 quarries subset in the southern zone of the whole area under investigation, and then applied to the more complex Carrara basin: the results showed good performances even in shadowed areas, where man-made visual recognition has !any limitations, overcoming precision issues showed by plain supervised classification of optical images.

Known for their polders - dewatered lands often used as pastures - the Netherlands has many parts situated below sea-level. To keep these parts habitable and safe the water-level has to be continuously monitored and controlled, a task performed by the Dutch regional water authorities. Deciding when and by how much to adjust to water level is a challenging task, because different, often conflicting, interests have to be kept in mind.

Around 30% of the dewatered pastures are situated on peat soils, of which many are located in the western part of the Netherlands, known as the ‘Green Heart’. Peat is composed of organic materials that oxidize and emit greenhouse gases when exposed to air as a consequence of the draining. Oxidation of peat soils results in volume reduction and subsequent subsidence. As a result, the groundwater level rises relative to the surface. Consequently, the soil needs to be dewatered to keep it sufficiently dry for farming, resulting in more oxidation, and therefore more subsidence. This process is bound to continue until the peat soils have disappeared completely. The societal cost of land subsidence due to peat soils are estimated to be 5200 million euro for urban areas and 200 million euro for peatland pastures, for a period until 2050.

Peat soils are dynamic surfaces with shrinkage and swelling of up to 10 centimeters in summer and winter, respectively. Especially swelling is very rapid process and can cause uplift of more than a cycle between acquisitions. This causes phase ambiguities in time which we cannot estimate without prior knowledge. To avoid making unwrapping errors we incorporate weather data from the KNMI, in particular the cumulative rainfall surplus (CRFS). The CRFS correlates strongly with the dynamics of the soils and it is expected that the CRFS is a good indicator for shrinkage and swell.

Here we processed and analyzed surface deformation over a small peat area near Delft using Sentinel-1 SAR images, which yields a time series of data with a typical revisit rate of 2 days. An improved time series InSAR algorithm was applied, which exploits all interferometric combinations and computes an equivalent single-master stack to estimate the vertical dynamics of the peat soils. Using terrain and land-use defined coherence estimation areas we optimize the phase estimation over areas severely affected by temporal decorrelation. We subtract a scaled estimate of the cumulative rainfall surplus to flatten the time-series and unwrap in time. Afterwards the estimate is restored, resulting in the time-series showing the full dynamics of the soils. Estimated yearly subsidence rates vary between 0.5 to 2.5 centimeter per year.

The methodology will prove invaluable for the Dutch regional water authorities. Conventional geodetic techniques are uncapable of measuring the dynamics of peat soils as soft soils make it impossible to install fixed benchmarks for repeated surveying. Providing weekly observations over large areas will lead to better water management to reduce emissions and move to a sustainable use of the Dutch peat soils.