A multi-temporal phenology based classification approach for Crop Monitoring in Kenya

The SBAM (Satellite Based Agricultural Monitoring) project, funded by the Italian Space Agency aims at: developing a validated satellite imagery based method for estimating and updating the agricultural areas in the region of Central-Africa; implementing an automated process chain capable of providing periodical agricultural land cover maps of the area of interest and, possibly, an estimate of the crop yield. The project aims at filling the gap existing in the availability of high spatial resolution maps of the agricultural areas of Kenya. A high spatial resolution land cover map of Central-Eastern Africa including Kenya was compiled in the year 2000 in the framework of the Africover project using Landsat images acquired, mostly, in 1995. We investigated the use of phenological information in supporting the use of remotely sensed images for crop classification and monitoring based on Landsat 8 and, in the near future, Sentinel 2 imagery. Phenological information on crop condition was collected using time series of NDVI (Normalized Difference Vegetation Index) based on Landsat 8 images. Kenyan countryside is mainly characterized by a high number of fragmented small and medium size farmlands that dramatically increase the difficulty in classification; 30 m spatial resolution images are not enough for a proper classification of such areas. So, a pan-sharpening FIHS (Fast Intensity Hue Saturation) technique was implemented to increase image resolution from 30 m to 15 m. Ground test sites were selected, searching for agricultural vegetated areas from which phenological information was extracted. Therefore, the classification of agricultural areas is based on crop phenology, vegetation index behaviour retrieved from a time series of satellite images and on AEZ (Agro Ecological Zones) information made available by FAO (FAO, 1996) for the area of interest. This paper presents the results of the proposed classification procedure in comparison with land cover maps produced in the past years by other projects. The results refer to the Nakuru County and they were validated using field campaigns data. It showed a satisfactory overall accuracy of 92.66 % which is a significant improvement with respect to previous land cover maps

Literature review of the remote sensing of natural resources

Abstracts of 596 documents related to remote sensors or the remote sensing of natural resources by satellite, aircraft, or ground-based stations are presented. Topics covered include general theory, geology and hydrology, agriculture and forestry, marine sciences, urban land use, and instrumentation. Recent documents not yet cited in any of the seven information sources used for the compilation are summarized. An author/key word index is provided

A Possible Role of Copernicus Sentinel-2 Data to Support Common Agricultural Policy Controls in Agriculture

Farmers that intend to access Common Agricultural Policy (CAP) contributions must submit an application to the territorially competent Paying Agencies (PA). Agencies are called to verify consistency of CAP contributions requirements through ground campaigns. Recently, EU regulation (N. 746/2018) proposed an alternative methodology to control CAP applications based on Earth Observation data. Accordingly, this work was aimed at designing and implementing a prototype of service based on Copernicus Sentinel-2 (S2) data for the classification of soybean, corn, wheat, rice, and meadow crops. The approach relies on the classification of S2 NDVI time-series (TS) by “user-friendly” supervised classification algorithms: Minimum Distance (MD) and Random Forest (RF). The study area was located in the Vercelli province (NW Italy), which represents a strategic agricultural area in the Piemonte region. Crop classes separability proved to be a key factor during the classification process. Confusion matrices were generated with respect to ground checks (GCs); they showed a high Overall Accuracy (>80%) for both MD and RF approaches. With respect to MD and RF, a new raster layer was generated (hereinafter called Controls Map layer), mapping four levels of classification occurrences, useful for administrative procedures required by PA. The Control Map layer highlighted that only the eight percent of CAP 2019 applications appeared to be critical in terms of consistency between farmers’ declarations and classification results. Only for these ones, a GC was warmly suggested, while the 12% must be desirable and the 80% was not required. This information alone suggested that the proposed methodology is able to optimize GCs, making possible to focus ground checks on a limited number of fields, thus determining an economic saving for PA and/or a more effective strategy of controls

Remote sensing image fusion on 3D scenarios: A review of applications for agriculture and forestry

Three-dimensional (3D) image mapping of real-world scenarios has a great potential to provide the user with a more accurate scene understanding. This will enable, among others, unsupervised automatic sampling of meaningful material classes from the target area for adaptive semi-supervised deep learning techniques. This path is already being taken by the recent and fast-developing research in computational fields, however, some issues related to computationally expensive processes in the integration of multi-source sensing data remain. Recent studies focused on Earth observation and characterization are enhanced by the proliferation of Unmanned Aerial Vehicles (UAV) and sensors able to capture massive datasets with a high spatial resolution. In this scope, many approaches have been presented for 3D modeling, remote sensing, image processing and mapping, and multi-source data fusion. This survey aims to present a summary of previous work according to the most relevant contributions for the reconstruction and analysis of 3D models of real scenarios using multispectral, thermal and hyperspectral imagery. Surveyed applications are focused on agriculture and forestry since these fields concentrate most applications and are widely studied. Many challenges are currently being overcome by recent methods based on the reconstruction of multi-sensorial 3D scenarios. In parallel, the processing of large image datasets has recently been accelerated by General-Purpose Graphics Processing Unit (GPGPU) approaches that are also summarized in this work. Finally, as a conclusion, some open issues and future research directions are presented.European Commission 1381202-GEU PYC20-RE-005-UJA IEG-2021Junta de Andalucia 1381202-GEU PYC20-RE-005-UJA IEG-2021Instituto de Estudios GiennesesEuropean CommissionSpanish Government UIDB/04033/2020DATI-Digital Agriculture TechnologiesPortuguese Foundation for Science and Technology 1381202-GEU FPU19/0010

Quarterly literature review of the remote sensing of natural resources

The Technology Application Center reviewed abstracted literature sources, and selected document data and data gathering techniques which were performed or obtained remotely from space, aircraft or groundbased stations. All of the documentation was related to remote sensing sensors or the remote sensing of the natural resources. Sensors were primarily those operating within the 10 to the minus 8 power to 1 meter wavelength band. Included are NASA Tech Briefs, ARAC Industrial Applications Reports, U.S. Navy Technical Reports, U.S. Patent reports, and other technical articles and reports

Feasibility study using remote sensing technologies to improve zonal vineyard management

The primary purpose of this research was to examine the feasibility of using remote sensing data to improve efficiency of zonal vineyard management. To achieve this goal, correlation analysis between the significant vineyard management variables and different remote sensing data analysis tools were undertaken. The variables included leaf water potential, soil moisture, canopy size, vine health, vineyard yield, and fruit composition, which further impacts wine quality. The remote sensing data analysis tools included normalized difference vegetation index (NDVI), and other indices extracted from electromagnetic reflectance data of grapevine leaves and canopies. In each site, sentinel vines (i.e., 72-81) were identified in a grid form. GPS-based geolocation was carried out for six Cabernet Franc vineyards in Ontario's Niagara wine country. Even though remote sensing data analysis tools were not associated with several other important variables for quality grape production, this research still confirmed that remote sensing data analysis has significant potential to differentiate specific zones of canopy size, water stress, yield, some superior fruit compositions, and the resulting wine sensory attributes within a single vineyard site. This study also confirmed that the mechanism of plant defense systems against biotic stress could have impacts on the spectral behaviour of grapevine leaves and hyperspectral remote sensing technologies could be applied as a tool to identify the spectral behaviour changes due to stress. Overall, this study verified the feasibility of remote sensing technologies to enhance the efficiency of vineyard management in the correlation of data from various remote sensing data-analysis techniques and viticulturally important variables for plant health and growth, and fruit and wine quality. As a first step to develop a site-specific crop management (SSCM) model for vineyard management, it also proposes future research opportunities to test and develop an efficient vineyard management decision making model

IDENTIFICATION OF AGRICULTURAL LAND USE IN CALIFORNIA THROUGH REMOTE SENSING

Ground truthing actual crop types in an area can be expensive and time-consuming. The California Department of Water Resources attempts to ground truth land use in each county in California every five years. However, this is limited by budgetary constraints and often results in infrequent (more than every ten years) surveying of many counties. An accurate accounting of crops growing in a region is important for a variety of purposes including farm production estimates, groundwater and surface water modeling, evapotranspiration estimation, water planning, research applications, etc. Agricultural land use is continually changing due to development and environmental factors. Currently, USDA NASS provides georeferenced land use maps of regions throughout the U.S. While these are beneficial, the accuracy is not very high for California due to the wide variety of crops grown throughout the state. California has an increasingly complex agricultural system which includes multi-crops changing on an annual and even semiannual basis, long growing seasons, and complex and flexible irrigation schedules. Remotely sensed data from available satellites are used to more accurately classify crop types within the Madera and Merced Counties of California’s Central Valley. An initial classification approach utilizing a simplified decision tree for a data subset of the area considered is presented. In order to accommodate the larger dataset at hand, a computer based approach is applied using the Nearest Neighbor classification algorithm in the computer program eCognition. Iterative analyses were performed to consider a range of scenarios with varying spectral inputs. The results show the methods presented can be beneficial in discriminating 24 of the major crop types from multi-temporal spectral data