L. Kooistra

Using spectral information from the NIR water absorption features for the retrieval of canopy water content

Canopy water content (CWC) is important for mapping and monitoring the condition of the terrestrial ecosystem. Spectral information related to the water absorption features at 970 nm and 1200 nm offers possibilities for deriving information on CWC. In this study, we compare the use of derivative spectra, spectral indices and continuum removal techniques for these regions. Hyperspectral reflectance data representing a range of canopies were simulated using the combined PROSPECT + SAILH model. Best results in estimating CWC were obtained by using spectral derivatives at the slopes of the 970 nm and 1200 nm water absorption features. Real data from two different test sites were analysed. Spectral information at both test sites was obtained with an ASD FieldSpec spectrometer, whereas at the second site HyMap airborne imaging spectrometer data were also acquired. Best results were obtained for the derivative spectra. In order to avoid the potential influence of atmospheric water vapour absorption bands the derivative of the reflectance on the right slope of the canopy water absorption feature at 970 nm can best be used for estimating CWC.

Soil Organic Carbon mapping of partially vegetated agricultural fields with imaging spectroscopy

Soil Organic Carbon (SOC) is one of the key soil properties, but the large spatial variation makes continuous mapping a complex task. Imaging spectroscopy has proven to be an useful technique for mapping of soil properties, but the applicability decreases rapidly when fields are partially covered with vegetation. In this paper we show that with only a few percent fractional maize cover the accuracy of a Partial Least Square Regression (PLSR) based SOC prediction model drops dramatically. However, this problem can be solved with the use of spectral unmixing techniques. First, the fractional maize cover is determined with linear spectral unmixing, taking the illumination and observation angles into account. In a next step the influence of maize is filtered out from the spectral signal by a new procedure termed Residual Spectral Unmixing (RSU). The residual soil spectra resulting from this procedure are used for mapping of SOC using PLSR, which could be done with accuracies comparable to studies performed on bare soil surfaces (Root Mean Standard Error of Calibration = 1.34 g/kg and Root Mean Standard Error of Prediction = 1.65 g/kg). With the presented RSU approach it is possible to filter out the influence of maize from the mixed spectra, and the residual soil spectra contain enough information for mapping of the SOC distribution within agricultural fields. This can improve the applicability of airborne imaging spectroscopy for soil studies in temperate climates, since the use of the RSU approach can extend the flight-window which is often constrained by the presence of vegetation.

A Lightweight Hyperspectral Mapping System and Photogrammetric Processing Chain for Unmanned Aerial Vehicles

During the last years commercial hyperspectral imaging sensors have been miniaturized and their performance has been demonstrated on Unmanned Aerial Vehicles (UAV). However currently the commercial hyperspectral systems still require minimum payload capacity of approximately 3 kg, forcing usage of rather large UAVs. In this article we present a lightweight hyperspectral mapping system (HYMSY) for rotor-based UAVs, the novel processing chain for the system, and its potential for agricultural mapping and monitoring applications. The HYMSY consists of a custom-made pushbroom spectrometer (400–950 nm, 9 nm FWHM, 25 lines/s, 328 px/line), a photogrammetric camera, and a miniature GPS-Inertial Navigation System. The weight of HYMSY in ready-to-fly configuration is only 2.0 kg and it has been constructed mostly from off-the-shelf components. The processing chain uses a photogrammetric algorithm to produce a Digital Surface Model (DSM) and provides high accuracy orientation of the system over the DSM. The pushbroom data is georectified by projecting it onto the DSM with the support of photogrammetric orientations and the GPS-INS data. Since an up-to-date DSM is produced internally, no external data are required and the processing chain is capable to georectify pushbroom data fully automatically. The system has been adopted for several experimental flights related to agricultural and habitat monitoring applications. For a typical flight, an area of 2–10 ha was mapped, producing a RGB orthomosaic at 1–5 cm resolution, a DSM at 5–10 cm resolution, and a hyperspectral datacube at 10–50 cm resolution.

Identification of soil heavy metal sources and improvement in spatial mapping based on soil spectral information: A case study in northwest China

In a sewage irrigation area of northwest China, 52 topsoil samples were collected to measure the contents of arsenic (As), chromium (Cr), copper (Cu), mercury (Hg), manganese (Mn), nickel (Ni), lead (Pb) and zinc (Zn). To identify their sources, multivariate statistics and geostatistics were applied to separate pedogenic elements (As and Mn) from anthropogenic elements (Cr, Cu, Hg, Ni, Pb and Zn). The accumulation of soil Hg was mainly attributed to long-term sewage irrigation, whereas Cr, Ni and Zn were mainly from industrial activities and dust deposition. In addition to the impacts of industry and dust, traffic-related factors were the main sources of Pb and Cu contamination. Based on the relationships of heavy metals with various soil properties and reflectance spectra, co-kriging (CK) was used to improve the interpolation of heavy metals. Comparatively, soil spectra were more suitable as covariates due to their ease and low-cost of collecting as features.

Generation of Spectral–Temporal Response Surfaces by Combining Multispectral Satellite and Hyperspectral UAV Imagery for Precision Agriculture Applications

Precision agriculture requires detailed crop status information at high spatial and temporal resolutions. Remote sensing can provide such information, but single sensor observations are often incapable of meeting all data requirements. Spectral-temporal response surfaces (STRSs) provide continuous reflectance spectra at high temporal intervals. This is the first study to combine multispectral satellite imagery (from Formosat-2) with hyperspectral imagery acquired with an unmanned aerial vehicle (UAV) to construct STRS. This study presents a novel STRS methodology which uses Bayesian theory to impute missing spectral information in the multispectral imagery and introduces observation uncertainties into the interpolations. This new method is compared to two earlier published methods for constructing STRS: a direct interpolation of the original data and a direct interpolation along the temporal dimension after imputation along the spectral dimension. The STRS derived through all three methods are compared to field measured reflectance spectra, leaf area index (LAI), and canopy chlorophyll of potato plants. The results indicate that the proposed Bayesian approach has the highest correlation (r = 0.953) and lowest RMSE (0.032) to field spectral reflectance measurements. Although the optimized soil-adjusted vegetation index (OSAVI) obtained from all methods have similar correlations to field data, the modified chlorophyll absorption in reflectance index (MCARI) obtained from the Bayesian STRS outperform the other two methods. A correlation of 0.83 with LAI and 0.77 with canopy chlorophyll measurements are obtained, compared to correlations of 0.27 and 0.09, respectively, for the directly interpolated STRS.

Mapping vegetation density in a heterogeneous river floodplain ecosystem using pointable CHRIS/PROBA data

River floodplains in the Netherlands serve as water storage areas, while they also have the function of nature rehabilitation areas. Floodplain vegetation is therefore subject to natural processes of vegetation succession. At the same time, vegetation encroachment obstructs the water flow into the floodplains and increases the flood risk for the hinterland. Spaceborne pointable imaging spectroscopy has the potential to quantify vegetation density on the basis of leaf area index (LAI) from a desired view zenith angle. In this respect, hyperspectral pointable CHRIS data were linked to the ray tracing canopy reflectance model FLIGHT to retrieve vegetation density estimates over a heterogeneous river floodplain. FLIGHT enables simulating top-of-canopy reflectance of vegetated surfaces either in turbid (e.g., grasslands) or in 3D (e.g., forests) mode. By inverting FLIGHT against CHRIS data, LAI was computed for three main classified vegetation types, ‘herbaceous’, ‘shrubs’ and ‘forest’, and for the CHRIS view zenith angles in nadir, backward (−36°) and forward (+36°) scatter direction. The −36° direction showed most LAI variability within the vegetation types and was best validated, closely followed by the nadir direction. The +36° direction led to poorest LAI retrievals. The class-based inversion process has been implemented into a GUI toolbox which would enable the river manager to generate LAI maps in a semiautomatic way.

Spatial variation in biodiversity, soil degradation and productivity in agricultural landscapes in the highlands of Tigray, northern Ethiopia

There is a growing concern about food security and sustainability of agricultural production in developing countries. However, there are limited attempts to quantify agro-biodiversity losses and relate these losses to soil degradation and crop productivity, particularly in Tigray, Ethiopia. In this study, spatial variation in agro-biodiversity and soil degradation was assessed in 2000 and 2005 at 151 farms in relation to farm, productivity, wealth, social, developmental and topographic characteristics in Tigray, northern Ethiopia. A significant decrease in agro-biodiversity was documented between 2000 and 2005, mainly associated with inorganic fertilizer use, number of credit sources and proximity to towns and major roads. Agro-biodiversity was higher at farms with higher soil fertility (available P and total N) and higher productivity (total caloric crop yield). Low soil organic matter, few crop selection criteria and steep slopes contributed to soil erosion. Sparsely and intensively cultivated land use types, as determined from satellite images, were associated with high and low agro-biodiversity classes, respectively, as determined during on-farm surveys in 2005. This study gives insight into the recent changes in and current status of agro-biodiversity and soil degradation at different spatial scales, which can help to improve food security through the maintenance of agro-biodiversity resources.

Characterizing Forest Change Using Community-Based Monitoring Data and Landsat Time Series

Increasing awareness of the issue of deforestation and degradation in the tropics has resulted in efforts to monitor forest resources in tropical countries. Advances in satellite-based remote sensing and ground-based technologies have allowed for monitoring of forests with high spatial, temporal and thematic detail. Despite these advances, there is a need to engage communities in monitoring activities and include these stakeholders in national forest monitoring systems. In this study, we analyzed activity data (deforestation and forest degradation) collected by local forest experts over a 3-year period in an Afro-montane forest area in southwestern Ethiopia and corresponding Landsat Time Series (LTS). Local expert data included forest change attributes, geo-location and photo evidence recorded using mobile phones with integrated GPS and photo capabilities. We also assembled LTS using all available data from all spectral bands and a suite of additional indices and temporal metrics based on time series trajectory analysis. We predicted deforestation, degradation or stable forests using random forest models trained with data from local experts and LTS spectral-temporal metrics as model covariates. Resulting models predicted deforestation and degradation with an out of bag (OOB) error estimate of 29% overall, and 26% and 31% for the deforestation and degradation classes, respectively. By dividing the local expert data into training and operational phases corresponding to local monitoring activities, we found that forest change models improved as more local expert data were used. Finally, we produced maps of deforestation and degradation using the most important spectral bands. The results in this study represent some of the first to combine local expert based forest change data and dense LTS, demonstrating the complementary value of both continuous data streams. Our results underpin the utility of both datasets and provide a useful foundation for integrated forest monitoring systems relying on data streams from diverse sources.

Using Sentinel-2 Data for Retrieving LAI and Leaf and Canopy Chlorophyll Content of a Potato Crop

Leaf area index (LAI) and chlorophyll content, at leaf and canopy level, are important variables for agricultural applications because of their crucial role in photosynthesis and in plant functioning. The goal of this study was to test the hypothesis that LAI, leaf chlorophyll content (LCC), and canopy chlorophyll content (CCC) of a potato crop can be estimated by vegetation indices for the first time using Sentinel-2 satellite images. In 2016 ten plots of 30 × 30 m were designed in a potato field with different fertilization levels. During the growing season approximately 10 daily radiometric field measurements were used to determine LAI, LCC, and CCC. These radiometric determinations were extensively calibrated against LAI2000 and chlorophyll meter (SPAD, soil plant analysis development) measurements for potato crops grown in the years 2010–2014. Results for Sentinel-2 showed that the weighted difference vegetation index (WDVI) using bands at 10 m spatial resolution can be used for estimating the LAI (R2 of 0.809; root mean square error of prediction (RMSEP) of 0.36). The ratio of the transformed chlorophyll in reflectance index and the optimized soil-adjusted vegetation index (TCARI/OSAVI) showed to be a good linear estimator of LCC at 20 m (R2 of 0.696; RMSEP of 0.062 g·m−2). The performance of the chlorophyll vegetation index (CVI) at 10 m spatial resolution was slightly worse (R2 of 0.656; RMSEP of 0.066 g·m−2) compared to TCARI/OSAVI. Finally, results showed that the green chlorophyll index (CIgreen) was an accurate and linear estimator of CCC at 10 m (R2 of 0.818; RMSEP of 0.29 g·m−2). Results for CIgreen were better than for the red-edge chlorophyll index (CIred-edge, R2 of 0.576, RMSE of 0.43 g·m−2). Our results show that Sentinel-2 bands at 10 m spatial resolution are suitable for estimating LAI, LCC, and CCC, avoiding the need for red-edge bands that are only available at 20 m. This is an important finding for applying Sentinel-2 data in precision agriculture.

Development of a Dynamic Web Mapping Service for Vegetation Productivity Using Earth Observation and in situ Sensors in a Sensor Web Based Approach

This paper describes the development of a sensor web based approach which combines earth observation and in situ sensor data to derive typical information offered by a dynamic web mapping service (WMS). A prototype has been developed which provides daily maps of vegetation productivity for the Netherlands with a spatial resolution of 250 m. Daily available MODIS surface reflectance products and meteorological parameters obtained through a Sensor Observation Service (SOS) were used as input for a vegetation productivity model. This paper presents the vegetation productivity model, the sensor data sources and the implementation of the automated processing facility. Finally, an evaluation is made of the opportunities and limitations of sensor web based approaches for the development of web services which combine both satellite and in situ sensor sources.