Christopher Conrad

TiSeG: A Flexible Software Tool for Time-Series Generation of MODIS Data Utilizing the Quality Assessment Science Data Set

Time series generated from remotely sensed data are important for regional to global monitoring, estimating long-term trends, and analysis of variations due to droughts or other extreme events such as El Nintildeo. Temporal vegetation patterns including phenological states, photosynthetic activity, or biomass estimations are an essential input for climate modeling or the analysis of the carbon cycle. However, long-term analysis requires accurate calibration and error estimation, i.e., the quality of the time series determines its usefulness. Although previous attempts of quality assessment have been made with NOAA-AVHRR data, a first rigorous concept of data quality and validation was introduced with the MODIS sensors. This paper presents the time-series generator (TiSeG), which analyzes the pixel-level quality-assurance science data sets of all gridded MODIS land (MODLand) products suitable for time-series generation. According to user-defined settings, the tool visualizes the spatial and temporal data availability by generating two indices, the number of invalid pixels and the maximum gap length. Quality settings can be modified spatially and temporally to account for regional and seasonal variations of data quality. The user compares several quality settings and masks or interpolates the data gaps. This paper describes the functionality of TiSeG and shows an example of enhanced vegetation index time-series generation with numerous settings for Germany. The example indicates the improvements of time series when the quality information is employed with a critical weighting between data quality and the necessary quantity for meaningful interpolation.

Per-Field Irrigated Crop Classification in Arid Central Asia Using SPOT and ASTER Data

The overarching goal of this research was to explore accurate methods of mapping irrigated crops, where digital cadastre information is unavailable: (a) Boundary separation by object-oriented image segmentation using very high spatial resolution (2.5-5 m) data was followed by (b) identification of crops and crop rotations by means of phenology, tasselled cap, and rule-based classification using high resolution (15-30 m) bi-temporal data. The extensive irrigated cotton production system of the Khorezm province in Uzbekistan, Central Asia, was selected as a study region. Image segmentation was carried out on pan-sharpened SPOT data. Varying combinations of segmentation parameters (shape, compactness, and color) were tested for optimized boundary separation. The resulting geometry was validated against polygons digitized from the data and cadastre maps, analysing similarity (size, shape) and congruence. The parameters shape and compactness were decisive for segmentation accuracy. Differences between crop phenologies were analyzed at field level using bi-temporal ASTER data. A rule set based on the tasselled cap indices greenness and brightness allowed for classifying crop rotations of cotton, winter-wheat and rice, resulting in an overall accuracy of 80 %. The proposed field-based crop classification method can be an important tool for use in water demand estimations, crop yield simulations, or economic models in agricultural systems similar to Khorezm.

Integration of Optical and Synthetic Aperture Radar Imagery for Improving Crop Mapping in Northwestern Benin, West Africa

Abstract: Crop mapping in West Africa is challenging, due to the unavailability of adequate satellite images (as a result of excessive cloud cover), small agricultural fields and a heterogeneous landscape. To address this challenge, we integrated high spatial resolution multi-temporal optical (RapidEye) and dual polarized (VV/VH) SAR (TerraSAR-X) data to map crops and crop groups in northwestern Benin using the random forest classification algorithm. The overall goal was to ascertain the contribution of the SAR data to crop mapping in the region. A per-pixel classification result was overlaid with vector field boundaries derived from image segmentation, and a crop type was determined for each field based on the modal class within the field. A per-field accuracy assessment was conducted by comparing the final classification result with reference data derived from a field campaign. Results indicate that the integration of RapidEye and TerraSAR-X data improved classification accuracy by 10%–15% over the use of RapidEye only. The VV polarization was found to better discriminate crop types than the VH polarization. The research has shown that if optical and SAR data are available for the whole cropping season, classification accuracies of up to 75% are achievable.

Temporal segmentation of MODIS time series for improving crop classification in Central Asian irrigation systems

Crop cover and crop rotation mapping is an important and still evolving field in remote sensing science for which robust and highly automated processing chains are required. This study presents an improved mapping procedure for crop rotations of irrigated areas in Central Asia by using classification and regression trees (CARTs) applied to transformations of 250 m Moderate Resolution Imaging Spectroradiometer (MODIS) Normalized Difference Vegetation Index (NDVI) time series. The time series were divided into several temporal segments, from which metrics were derived as input features for classification. This temporal aggregation was applied to suppress within-class temporal variability. Various lengths of temporal segments were tested for their potential to increase classification accuracy. In addition, tests of enhancing the classification accuracy were done by combining different classification results using the majority rule for voting. These different processing strategies were applied to four annual time series (2004–2007) of the Khorezm region, where 270 000 ha of irrigated land is dominated by rotations of cotton, wheat and rice. Improved classification results were obtained for CARTs applied to metrics derived from a mixture of different segment lengths. The sole use of either long or short temporal segments was inferior. CART prioritized segments representing active phases of the phenological development. The best result, the optimized segment-based approach, achieved an overall accuracy between 83 and 85% for classifications between 2004 and 2007; in particular, the small range demonstrated the robustness regarding inter-annual variations. These accuracies exceeded those of the original time series without temporal segmentation by 6–7%. With some adjustments to other crops and field heterogeneity influencing the usefulness of a respective sensor, the approach can be applied to other irrigation systems in Central Asia.

Grassland habitat mapping by intra-annual time series analysis – Comparison of RapidEye and TerraSAR-X satellite data

Abstract Remote sensing concepts are needed to monitor open landscape habitats for environmental change and biodiversity loss. However, existing operational approaches are limited to the monitoring of European dry heaths only. They need to be extended to further habitats. Thus far, reported studies lack the exploitation of intra-annual time series of high spatial resolution data to take advantage of the vegetations’ phenological differences. In this study, we investigated the usefulness of such data to classify grassland habitats in a nature reserve area in northeastern Germany. Intra-annual time series of 21 observations were used, acquired by a multi-spectral (RapidEye) and a synthetic aperture radar (TerraSAR-X) satellite system, to differentiate seven grassland classes using a Support Vector Machine classifier. The classification accuracy was evaluated and compared with respect to the sensor type – multi-spectral or radar – and the number of acquisitions needed. Our results showed that very dense time series allowed for very high accuracy classifications (>90%) of small scale vegetation types. The classification for TerraSAR-X obtained similar accuracy as compared to RapidEye although distinctly more acquisitions were needed. This study introduces a new approach to enable the monitoring of small-scale grassland habitats and gives an estimate of the amount of data required for operational surveys.

Snow-cover variability in central Asia between 2000 and 2011 derived from improved MODIS daily snow-cover products

In this study, the daily snow-cover time series has been analysed for the whole of central Asia after cloud coverage was removed. Snow-cover duration (SCD), snow-cover start (SCS), and snow-cover melt (SCM) have been derived for each hydrological year from 2000/2001 to 2010/2011 and mean conditions were extracted that identify a distinct north–south gradient of these parameters. The snow-cover index (SCI), which depicts a moderate variability with maximum deviations of ∼20%, has been included for major hydrological catchments. The hydrological year 2001/2002 stands out due to minimum SCD caused by late SCS and early SCM while 2002/2003 constitutes maximum SCD initiated by late SCM. Although the time series of 11 years of data is too short to calculate possible trends of snow-cover characteristics, the results can be used to describe the average snow-cover conditions and compare single years against these values. Large divergences can indicate deficits or excesses of snow, which may lead to abnormal run-off situations, including natural disasters such as floods, landslides, or droughts. The latter, especially, can have severe negative economic impacts in a region.

Identifying Changing Snow Cover Characteristics in Central Asia between 1986 and 2014 from Remote Sensing Data

Central Asia consists of the five former Soviet States Kazakhstan, Kyrgyzstan, Tajikistan, Turkmenistan, and Uzbekistan, therefore comprising an area of ~4 Mio km2. The continental climate is characterized by hot and dry summer months and cold winter seasons with most precipitation occurring as snowfall. Accordingly, freshwater supply is strongly depending on the amount of accumulated snow as well as the moment of its release after snowmelt. The aim of the presented study is to identify possible changes in snow cover characteristics, consisting of snow cover duration, onset and offset of snow cover season within the last 28 years. Relying on remotely sensed data originating from medium resolution imagers, these snow cover characteristics are extracted on a daily basis. The resolution of 500–1000 m allows for a subsequent analysis of changes on the scale of hydrological sub-catchments. Long-term changes are identified from this unique dataset, revealing an ongoing shift towards earlier snowmelt within the Central Asian Mountains. This shift can be observed in most upstream hydro catchments within Pamir and Tian Shan Mountains and it leads to a potential change of freshwater availability in the downstream regions, exerting additional pressure on the already tensed situation.

Generation and Assessment of MODIS Time Series using Quality Information

Monitoring and modeling extensive Earth surface processes for regional to global applications such as carbon budgeting or biomass estimation requires time series derived from remotely sensed imagery. Time series are also needed for discrimination of long-term land cover change from short-term variations, mapping of vegetation dynamics and improved land cover mapping and update. The results of these applications, however, clearly depend on the quality of the time series. Cloud coverage, high aerosol content, adverse view and illumination angles, or sensor defects affect and corrupt the data and may lead to false conclusions. Value-added MODIS data contain detailed pixel level quality information. This source of meta-data highly suits for data analysis or generation of time series. A software package, called Time Series Generator (TiSeG), has been developed to analyze data quality and estimate the quality of time series to be generated. TiSeG meets the challenge to weight the data quality against the quantity of available data for meaningful time series construction.

Validation of the collection 5 MODIS FPAR product in a heterogeneous agricultural landscape in arid Uzbekistan using multitemporal RapidEye imagery

The fraction of photosynthetically active radiation (FPAR) absorbed by a vegetation canopy is an important variable for global vegetation modelling and is operationally available from data of the Terra Moderate Resolution Imaging Spectroradiometer (MODIS) satellite sensor starting from the year 2000. Product validation is ongoing and important for constant product improvement, but few studies have investigated the specific accuracy of MODIS FPAR using in situ measurements and none have focused on agricultural areas. This study therefore presents a validation of the collection 5 MODIS FPAR product in a heterogeneous agricultural landscape in western Uzbekistan. High-resolution FPAR maps were compiled via linear regression between in situ FPAR measurements and the RapidEye normalized difference vegetation index (NDVI) for the 2009 season. The data were aggregated to the MODIS scale for comparison. Data on the percentage cover of agricultural crops per MODIS pixel allowed investigation of the impact of spatial heterogeneity on MODIS FPAR accuracy. Overall, the collection 5 MODIS FPAR overestimated RapidEye FPAR between approximately 6% and 15%. MODIS quality flags, the underlying biome classification and spatial heterogeneity were investigated as potential sources of error. MODIS data quality was very good in all cases. A comparison of the MODIS land-cover product with high-resolution land-use classification revealed a significant misclassification by MODIS. Yet, we found that the overestimation of MODIS FPAR is independent of classification accuracy. The results indicate that the amount of background information, present even in the most homogeneous pixels (∼70% crop cover), is most likely the reason for the overestimation. The behaviour of pure pixels could not be investigated due to a lack of appropriate pixels.

Evaluation of the CropSyst model for simulating the potential yield of cotton

Cotton produced in Uzbekistan has a low water and fertilizer use efficiency and yield is below its potential. To introduce improved production methods, knowledge is required on how the agro-ecosystem would respond to these alternatives. For this assessment, dynamic simulation models such as the crop-soil simulation model CropSyst are useful tools. CropSyst had never been applied to cotton, so it first was calibrated to the cotton variety Khorezm-127 grown under researcher-managed optimal conditions in the Khorezm region of Uzbekistan in 2005. The model performance was evaluated with a data set obtained in 2004 on two farmer-managed sites. Both data sets comprised in-situ measurements of leaf area index and aboveground biomass. In addition, the 2004 data set included the normalized difference vegetation index derived from satellite imagery of the two cotton fields, which provided estimations of leaf area index with a high temporal resolution. The calibrated optimum mean daily temperature for cotton growth was 25 °C., the specific leaf area 13.0 m2 kg−1, the leaf/stem partition coefficient 3.0, the biomass/transpiration coefficient 8.1 kg m−2 kPa m−2 and the radiation use efficiency 2.0 g MJ−1. Simulations matched 2005 data, achieving a root mean square error between simulated and observed leaf area index and aboveground biomass of 0.36 m2 m−2 and 0.97 Mg ha−1, respectively. The evaluation showed that early cotton growth and leaf area index development could be simulated with sufficient accuracy using CropSyst. However, final aboveground biomass was slightly overestimated by CropSyst, because some unaccounted plant stress at the sites diminished actual aboveground biomass, leading to a root means square error of around 2 Mg ha−1. Some characteristics of cotton, such as the indeterminate growth habit, could not be incorporated in detail in the model. However, these simplifications were compensated by various other advantages of CropSyst, such as the option to simulate crop-rotation or its generic crop growth routine that allows modelling of additional, undocumented crops. The availability of normalized difference vegetation index data with a high temporal and acceptable spatial resolution opened possibilities for a precise, in-expensive and resource-efficient way of model evaluation.