IntroductionAgriculture is one of the key socio-economic activities substantially affected by climate variability and change globally [1]. The impact of weather and climate variability and change is more remarkable in the Arid and Semi Arid Lands (ASALs).In Kenya , maize is the most widely grown crop and maize farmers in small-scale farms grow maize under rain-fed condition. Maize production is expected to decrease by 3-10 % until 2050 due to climate change effects (Thornton and Cramer 2012). Increasing risk of invasion by vectors of crops virus diseases in eastern Africa is one of the evidences for the climate change effect.This study investigates the use of two different optical sensors, the multispectral imager (MSI) onboard the RapidEye satellites and the operational land imager (OLI) onboard the Landsat-8 for mapping within-field variability of crop growth conditions and tracking the seasonal growth dynamics.

1. Adamgbe EM, Ujoh F. Effect of variability in rainfall characteristics on maize yield in Gboko. Nigeria J Environ Prot. 2013; 4:881-7. PublisherFullText

With increasing population pressure throughout the world and the need for increased agricultural production there is a definite need for improved management of the world's agricultural resources. To make this happen it is first necessary to obtain reliable data on not only the types, but also the cropping system.

To address this challenge, we exploited use of different high spatial resolution multi-temporal opticalit is believed that the red edge band (690- 730 nm) of the RapidEye sensor allows better estimates of chlorophyll content for crop monitoring.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)

In recent years, agricultural land use has experienced high expansion rates in many parts of the world [1]. This expansion is mainly due to high population growth (especially in developing countries) and the need to grow more food to meet the rising food demand. Accurate and up-to-date information on agricultural land use is essential to appropriately monitor these changes and assess their impacts on water and soil quality, biodiversity and other environmental factors at various scales [24]. This is particularly important considering the looming effects of climate change and variability. Updated information on agricultural land use can help in monitoring changes in cropping systems and gauge farmers reaction to the changing climate.

Additionally, a wide range of biophysical and economic models can benefit from this information and improve decision-making based on their results. Remotely sensed (RS) data provide useful information for agricultural land use mapping. Periodic acquisition of RS data enables analysis to be conducted at regular intervals, which aids in identifying changes. Optical systems, which have largely been relied upon for agricultural land use mapping [5,6], measure reflectance from objects in the visible and infrared portions of the electromagnetic spectrum. The amount of reflectance is a function of the bio-physical characteristics of the reflecting feature (e.g., canopy moisture, leaf area and level of greenness of vegetation). Since different crops at varying vegetative stages exhibit different bio-physical characteristics, optical images have been useful in previous crop mapping studies [79]. However, the reliance of optical systems on the Suns energy limits image acquisition in cloudy or hazy conditions. Images acquired during these periods are normally of little use in mapping due to high cloud/haze cover. Whereas on irrigated land under arid conditions, the entire growing period can be easily covered by optical data [10,11], agricultural land use mapping efforts in rainfed dominated agricultural regions, like West Africa (WA), are hampered, because the rainfall season coincides with the cropping season. Consequently, little or no in-season images are available for agricultural land use mapping, leading to challenges in discriminating between different crop types or crop groups [1214]. For example, a number of land use studies [1517] in WA have had to lump all crop classes into one thematic class (cropland), due to a poor image temporal sequence. Synthetic aperture radar (SAR) systems are nearly independent of weather conditions. Unlike optical sensors, active radar systems have their own source of energy, transmitting radio waves and receiving the reflected echoes from objects on the Earths surface. The longer wavelengths of radio waves enable transmitted signals to penetrate clouds and other atmospheric conditions [18], which make radar systems highly reliable in terms of data provision, especially during periods in which optical sensors fail [1921]. Moreover, the information content of radar imagery differs from that of optical data owing to differences in how transmitted signals from the two systems interact with features on the ground. A radar sensor transmits an electromagnetic signal to an object and receives/records a reflected echo (backscatter) from the object. Backscatter intensities recorded by radar systems are largely a function Remote Sens. 2014, 6 6474 of the size, shape, orientation and dielectric constant of the scatterer [22]. Thus, in vegetation studies, radar backscatter intensities will differ based on the size, shape and orientation of the canopy components (e.g., leaves, stalks, fruit, etc.). Crops with different canopy architecture and cropping characteristics (e.g., planting in mounds) can be distinguished based on their backscatter intensities [2325]. The recent introduction of dual and quad-polarization acquisition modes in many radar satellites (e.g., Radarsat-2, PALSAR, TerraSAR-X) further increases the information content in radar data. Owing to the differences in imaging and information content, data from optical and radar systems have been found to be complementary [26]. Several studies have shown that integrating data from the two sources improves classification accuracies over the use of either of them [27]. The authors of [23] tested the integration of Landsat TM and SAR data (Radarsat, ENVISAT ASAR) for five regions in Canada. They concluded that in the absence of a good time series of optical imagery, the integration of two SAR images and a single optical image is sufficient to deliver operational accuracies (>85% overall accuracy). The authors of [28,29] noted an increase of 20% and 25%, respectively, in overall accuracy when radar and optical imagery were integrated in crop mapping. Other studies found percentage increases between 5% and 8% when the two data sources were merged [13,3034]. In this study, high resolution multi-temporal optical (RapidEye) and dual polarimetric (VV and VH) radar data (TerraSAR-X) have been combined to map crops and crop groups in northwestern Benin, West Africa. Excessive cloud cover during the main cropping season in West Africa has, for many years, hindered crop mapping efforts in the sub-region due to the unavailability of satellite images. A recent study [12] conducted in the sub-region with multi-temporal RapidEye images identified poor image temporal coverage as the limiting factor in accurately discrimination between certain crop types. A further limiting factor is the heterogeneity (small patches of different land use and land cover types) of the landscape [35], which leads to spectral confusion between classes, especially when per-pixel approaches are employed [36]. In order to reduce this confusion, a field-based classification approach was employed [37,38]. Vector field boundaries were derived through image segmentation. A per-pixel classification result was then overlaid and the modal class within each field assigned to it. The aim of this study was to combine optical and radar data to ascertain the contribution of radar data to crop mapping in WA. The specific research question addressed is: can dual polarized radar images acquired during peak cropping season months complement optical data to improve classification accuracies in crop mapping?

High-spatial resolution optical remote sensing observations can provide crop information at a spatial scale suitable for field to subfield level studies. The capability for simultaneous acquisition over a large area allows for capturing spatial variability due to underlying soil properties and management practices. It can greatly alleviate the workload for conducting crop surveys or field measurements. The time series observation is especially useful for tracking the seasonal trend of crop growth and improving our understanding of canopy functioning. Multiple optical remote sensing products over a growing season have been used for crop biomass and yield estimation with a radiation use efficiency model (RUE)1 and have proven to be useful in reducing the uncertainty of several input descriptors of crop models using the data assimilation approach.2,3 Unlike the moderate-resolution satellite sensors such as the MODIS and AVHRR, the relatively longer revisiting cycle of a high-resolution satellite sensor is largely affected by cloud contamination and hence leads to missed acquisitions during part of the key growth stages. For continuous monitoring of crop seasonal development trends, it is advantageous to be able to use data available from different sensors to shorten the revisit cycle.

The automatic cropland classification based on the data from spaceborne imagery is one of the most important sources of valuable information about the composition and the development of a variety of crops grown in different agricultural regions around the world. A general goal is to be able to estimate the area of specific crops,1,2monitor their health, and predict their yield.3,4A fundamental task in such applications is to determine the type of crop that is grown on a specific stretch of land.57The result is that the cropland classification has a significant role in the proper monitoring and management of land use on a local as well as a global level and represents an important factor in the overall agricultural policy making.8Therefore, it is essential to make such tools more accessible to different parties involved in the agricultural market and offer them the expertise acquired from the case studies of their deployment and development.The recent advances in the satellite imaging technology provide researchers and practitioners with ever more data in both quantitative and qualitative ways. This opens new opportunities for the extraction of meaningful and useful information, but it also creates new challenges regarding the choice and the development of appropriate methods for their processing. While satellite imagery for crop identification and crop-covered area estimation is a practice with more than 30 years of tradition,9,10the sensors have only recently gained resolution that allows for precision monitoring in the case of agriculture practices based on small land parcels.6,11,12The fact that most of these satellite imagery systems are commercial represents one of the obstacles for their widespread use and produces the need for the effective and efficient use of a vast amount of freely available data, like the high-quality data already available through the Landsat program13or the data that are planned to be published under similar terms by the upcoming Sentinel missions.14In line with these efforts, this paper presents a pixel-based cropland classification study that utilizes a time series of multispectral images with different properties which were acquired at different resolutions by different imaging instrumentsLandsat-815and RapidEye.16It also explores the capabilities of the proposed data fusion method for their combination with the aim of overcoming the shortcomings of different instruments in the particular cropland classification scenario characterized by the specific size of crop fields over the chosen agricultural region situated in the plains of Vojvodina in northern Serbia (southeastern Europe). It can be said that this scenario, where agriculture is based on very small areas dedicated to specific crops, is quite common in different parts of the world. One of the problems that arises is the presence of crop fields with very small areas and small dimensions compared to the spatial resolution of the freely available multispectral imagery. Additionally, if there is no available information about the crop field boundaries or cadastral data (such as in this study) which would enable the application of an object-based classification approach, it is of great significance to develop methods that effectively exploit available data and provide an improvement over the existing pixel-based classification approaches through the combination of different data sources. Therefore, this paper proposes a data fusion method that is successfully utilized in combination with a robust random forest classifier17in improving overall classification performance, as well as in enabling application of satellite imagery with a coarser spatial resolution in the given specific cropland classification task. A random forest classifier was chosen due to its recent use in remote sensing and because it has been accepted as an efficient tool in crop classification.1820The proposed method exploits different spectral and spatial resolutions of two different data sources in order to mitigate the described problem. Through feature-level fusion where composite features are extracted from all available multisensor data,21a data integration, which is considered as a low-level data fusion, is employed. It addresses the problem by using one mosaic multispectral image of the observed area, which is formed by mosaicking images acquired in the short time interval by the constellation of commercial satellites with a higher spatial resolution as an addition to the freely available Landsat-8 multispectral time series with a coarser spatial resolution which is acquired over a longer time interval that covers the phenological development of all observed crop types. This approach is related to a recent study,22which also tried to exploit the advantages of different data sources, but with application in the detection of vegetation changes. Through data fusion, it extends the applicability of the pixel-based classification using freely available satellite imagery with a coarser spatial resolution to the classification scenarios that demand finer spatial resolution due to the agricultural practices that are characterized by the growth of different crop types on the very small parcels of farmland.

In line with these efforts, this paper presents a pixel-based cropland classification study that utilizes a time series of multispectral images with different properties which were acquired at different resolutions by different sensors i.e. Landsat-8 and RapidEye The fact that most of these satellite imagery systems are commercial represents one of the obstacles for their widespread use and produces the need for the effective and efficient use of a vast amount of freely available data, like the high-quality data already available through the Landsat program 13 o

13 C. Reynold, Utilizing Landsat sidelaps to monitor global agriculture by a multi-platform system, in Proc. of the Global Geospatial Conf. Africa GIS, Addis Ababa, Ethiopia, Global Spatial Data Infrastructure (GSDI) Association, Needham, Massachusetts (2013).

It also explores the capabilities of the proposed data fusion method for their combination with the aim of overcoming the shortcomings of different instruments in the particular cropland classification scenario characterized by the specific size of crop fields over the chosen agricultural region situated in the plains of Vojvodina in northern Serbia (southeastern Europe). It can be said that this scenario, where agriculture is based on very small areas dedicated to specific crops, is quite common in different parts of the world. One of the problems that arises is the presence of crop fields with very small areas and small dimensions compared to the spatial resolution of the freely available multispectral imagery. Additionally, if there is no available information about the crop field boundaries or cadastral data (such as in this study) which would enable the application of an object-based classification approach, it is of great significance to develop methods that effectively exploit available data and provide an improvement over the existing pixel-based classification approaches through the combination of different data sources. Therefore, this paper proposes a data fusion method that is successfully utilized in combination with a robust random forest classifier17 in improving overall classification performance, as well as in enabling application of satellite imagery with a coarser spatial resolution in the given specific cropland classification task. A random forest classifier was chosen due to its recent use in remote sensing and because it has been accepted as an efficient tool in crop classification.1820