Suitable remote sensing method and data for mapping and measuring active crop fields (original) (raw)
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Remote Sensing of Irrigated Agriculture: Opportunities and Challenges
2010
Over the last several decades, remote sensing has emerged as an effective tool to monitor irrigated lands over a variety of climatic conditions and locations. The objective of this review, which summarizes the methods and the results of existing remote sensing studies, is to synthesize principle findings and assess the state of the art. We take a taxonomic approach to group studies based on location, scale, inputs, and methods, in an effort to categorize different approaches within a logical framework. We seek to evaluate the ability of remote sensing to provide synoptic and timely coverage of irrigated lands in several spectral regions. We also investigate the value of archived data that enable comparison of images through time. This overview of the studies to date indicates that remote sensing-based monitoring of irrigation is at an intermediate stage of development at local scales. For instance, there is overwhelming consensus on the efficacy of vegetation indices in identifying irrigated fields. Also, single date imagery, acquired at peak growing season, may suffice to identify irrigated lands, although to multi-date image data are necessary for improved classification and to distinguish different crop types. At local scales, the mapping of irrigated lands with remote sensing is also strongly affected by the timing of image acquisition and the number of images used. At the regional and global scales, on the other hand, remote sensing has not been fully operational, as methods that work in one place and time are not necessarily transferable to other locations and periods. Thus, at larger scales, more work is required to indentify the best spectral indices, best time periods, and best classification methods under different climatological and cultural environments. Existing studies at regional scales also establish the fact that both remote sensing and national statistical approaches require further refinement with a substantial
Using remote sensing to assess crop water productivity
SPIE Newsroom, 2010
The overarching goal of this research was to map crop water productivity using satellite sensor data at various spectral, spatial, radiometric, and temporal resolutions involving: (a) Moderate Resolution Imaging Spectroradiometer (MODIS) 500m, (b) MODIS 250m, (c) Landsat enhanced thematic mapper plus (ETM+) 60m thermal, (d) Indian Remote Sensing Satellite (IRS) 23.5 m, and (e) Quickbird 2.44 m data. The spectro-biophysical models were developed using IRS and Quickbird satellite data for wet biomass, dry biomass, leaf area index, and grain yield for 5 crops: (a) cotton, (b) maize, (c) winter wheat, (d) rice, and (e) alfalfa in the Sry Darya basin, Central Asia. Crop-specific productivity maps were developed by applying the best spectro-biophysical models for the respective delineated crop types. Water use maps were produced using simplified surface energy balance (SSEB) model by multiplying evaporative fraction derived from Landsat ETM+ thermal data by potential ET. The water productivity (WP) maps were then derived by dividing the crop productivity maps by water use maps. The results of cotton crop, an overwhelmingly predominant crop in Central Asian Study area, showed that about 55% area had low WP of < 0.3 kg/m 3 , 34% had moderate WP of 0.3-0.4 kg/m 3 , and only 11% area had high WP > 0.4 kg/m 3 . The trends were similar for other crops. These results indicated that there is highly significant scope to increase WP (to grow "more crop per drop") through better water and cropland management practices in the low WP areas, which will substantially enhance food security of the ballooning populations without having to increase: (a) cropland areas, and\or (b) irrigation water allocations. Use: http://spiedl.org/terms agriculture: thermal and hyper-spectral imagery for monitoring and mapping water status and nitrogen level in various crops and orchards; Spatio-temporal analysis of pests and diseases: Medfly, Olive fly, Pear Psylla, and soil-borne diseases in potato and nuts; Development of spatial knowledge-based decision support systems for pest control; Remote sensing for recognition and mapping of crop types and delineation of Green corridors.
Using Remote Sensing to Improve Crop Water Allocation in a Scarce Water Resources Environment
International Journal of Science and Research (IJSR), 2016
To understand the cropped areas and assess seasonal water supply for irrigation, remote sensing-based crop classification was conducted on satellite imagery data for a pilot area in the Bekaa Valley, Lebanon, during the 2011-2012 growing years. The crop classification was achieved using three sets of RapidEye and Landsat7 ETM+ (Enhanced Thematic Mapper Plus) images acquired in early (May), mid (July) and late (September) of 2011 and 2012 growing years, respectively. Field crop data were obtained throughout the growing seasons in well-defined farmers' plots before the images acquisitions using a hand-held GPS (Global Positioning System) Unit. Ten crop classification profiles and three non-crop profiles were derived for each year from the different class signatures in the preselected bands of the two satellite data. Then, image-derived results were checked for accuracy and used to produce cropping maps within GIS (Geographic Information System).These maps enabled us to define different cropping calendars and determine seasonal irrigation water requirements (IWRs) at the pilot area level. IWRs were calculated for the surveyed crops as the product of the produced cropping maps and net irrigation requirements (NIR)calculated by means of MOPECO(Economic Optimization Model for Irrigation Water Management). The results were compared with the Litani River Authority Database (LRAD) and found a good agreement. The classification results of RapidEye images (2011) compared quite well in the whole test area with Landsat derived crop maps (2012). The overall accuracy of the classification against the field data ranges from 84% to 95%. In addition, crop classification profiles appeared consistent with field crop observations, even though a slight variation was noted. The examination of the crop maps showed decreases of as much as 7%, 30% and 5%inbareland, woodland and fallow areas, respectively, in 2012 when compared to 2011. Data showed that these decreases were reported as increases in wheat (15%), fruit trees (11%), olive (6%), and vineyard (3%). The increased cropland that was observed in 2012 was accompanied by an increase in the amount of water allocated from the Canal 900 irrigation conveyor in comparison with that of 2011. This study presented an example of remote sensing application for water allocation in agriculture. It was concluded that satellite imagery was essential for the definition of the existing cropping patterns in the pilot area and helped better estimate seasonal irrigation needs at the scheme level. The proposed methodology may help irrigation deciders to better assess water resources with respect to the surveyed cropped areas.
Precise and economic water use is very important issue for irrigated farms. With the complexity in scientific approaches, the irrigation scheduling has moved towards a high-tech process with the precision application of water. The methods for irrigation scheduling are classified into several approaches based on soil water measurements (gravimetric method), soil water balance estimates (sophisticated methods) and plant stress indicators (crop water stress index), in combination with very sophisticated methods (remote sensing). The aim of this review paper is to critically discuss different methods and approaches of irrigation scheduling. The soil water measurement methods are found to be simple and with fair range of applicability. These methods are used for real time irrigation scheduling at smaller scale i.e., farmers field level. The other class of methods is soil water balance which requires a number of factors to be determined with a good accuracy. The problem of its popularity is huge calculations needed throughout its implementation. Furthermore, plant stress indicators can be used only if weather conditions are not rapidly changing (wind and radiation) and only for fully developed crops (in order to avoid soil surface temperature influence on measurements). However this approach can be made productive and easy to use for farmers if guidelines are established for different crops in different climatic conditions. The use of this technique however, usually is limited to small scale. In the modern age, there are several algorithms developed to assess the actual and potential evapotranspiration using the satellite remote sensing techniques. The difference of actual and potential evapotranspiration is being used to assess the water deficit and hence the amount of water require. Spatial and temporal resolution of the data attained from the satellite however still remains the main concern of these techniques. Dependencies of these techniques on satellite overpass time and fix spatial resolution of the individual satellites does not allow to develop a flexible irrigation schedule. After a critical review of all these methods, implementation of irrigation schedule for enhancing the crop water productivity in mechanized agriculture depends upon soil type, availability of modern tools, human capacity to implement these tools.
A Global map of irrigated area has been produced for the end of the last millennium using multiple satellite sensor data and secondary data. Multiple resolution time series data used in the study were: (a) AVHRR 4-band and NDVI 10-km monthly time series for 1981-1999, (b) SPOT vegetation NDVI 1-km monthly time series for 1999, (c) GTOPO-30 1-km elevation, (d) JERS SAR data for the rainforests during two seasons of 1996, (d) Tree cover 1-km data of 1992-93, and (e) Rainfall 50-km monthly time series for 1961-2000. A 34-class irrigated area map of the world (Figure 1) includes 20 irrigated area classes, 8 supplemental irrigation classes, and 6 other LULC classes with significant irrigation. A unique sub-pixel decomposition techniques (SP-DCT) enabled computation of irrigated areas for different seasons. So the area irrigated at the end of the last millennium for the entire world was estimated as 637 million hectares of which the seasonal distribution of the areas were: (a) 317,960,831 hectares during June-September, (b) 194,255,688 hectares during October-February, and (c) 125,220,366 hectares during March-May. There are suite of products that consists of maps, images, class characteristics, area calculations, snapshots and photos, animations, and accuracies. For example, the product-line provides month-by-month spectral characteristics of each of the 34 classes within and between seasons and provides a 20-year animation of irrigated area change dynamics. The data and the products are made available as a global public good through a dedicated web site at: http://www.iwmigmia.org.
Precise information on irrigation performance and water consumption at field or canal command level is important to irrigation managers and policymakers to make appropriate decisions on water management. Use of irrigation performance indices cum water consumption are the tools make such decisions. Calculate those figures are a challenging task for the past cropping years, that essential to make correct decisions especially in data-scarce regions of Asia and Africa. Similarly, calculation of these above at field or canal command level over large irrigation schemes are laborious, costly, timely inefficient and less accurate. The necessity for these figures is rapidly increasing due to the need to make the correct decisions with the continually rising population and food demand cum declining water availability parallel to climatic change issues. Accordingly, well established surface energy balance algorithm (SEBAL) technique has been employed in large irrigated areas in Punjab, Pakistan, as a tool to estimate actual evapotranspiration (ET a ), i.e., water consumption for the cropping years of 2004/05 and 2006/07. Freely available medium resolute daily MODIS images and hydrometeorological data were used as inputs for the ET a calculation. Under this study irrigation performance indices of equity and adequacy were calculated using actual evapotranspiration and evaporative fraction. Results show that annual ET a varies from less than 100 mm/year in desert/barren areas to 1,650 mm/year over large water bodies. For cropped areas, the variation ranges from 400 to 1,200 mm per year for both cropping years. In rice-wheat area of Punjab, average ET a of the cropping year 2004/05 is 896 mm, and 971 mm for cropping year 2006/07. In lower and southern Punjab, ET a is low and varies from 805 to 870 mm during 2004/05 and 2006/07, respectively. ET a was further analyzed in depth on a seasonal and canal command basis for a better understanding and shows that an average of 9% more water has been consumed by crops during the Kharif 2007 season while 10% higher consumption was observed for the Rabi 2006/07 season than in the previous 2004/05 cropping year. ET a of the Thal Canal has increased by 44% in the Kharif 2007 season followed by Lower Jhelum Canal with 28%. ET a of Upper Jhelum Canal has also increased by about 20% while that of Panjnad Canal has increased by 22% in the Rabi 2006/07 season. Equity of water consumption in 2006/07 has improved considerably compared to 2004/05 in many canals, especially Central Bari Doab, Bahawal, Thal, Chashma Right Bank, Muzaffargarh and Panjnad, which figured as 2, 12, 11, 7, 8 and 8%, respectively. Similarly, adequacy has also improved in many canal commands in the 2006/07 cropping year when compared to 2004/05. This study demonstrates how a remote sensing based estimation of water consumption and water stress can be combined to provide a better estimation of system and irrigation performance at a variety of spatial and temporal scales that would assist water managers and policymakers.
Potential utilization of satellite remote sensing for field-based agricultural studies
Chemical and Biological Technologies in Agriculture
Using satellite remote-sensing is a useful approach for agriculture to monitor plant and soil conditions and provide decision-making support to farmers. Recently, several types of tools and indices by the satellite remote-sensing have been developed for monitoring drought stress, changes in land uses, and crop–soil water relations. Although these techniques are powerful tools, especially in developing countries and regions where precise data of crop evaluation and yield statistics are not accessible, it is quite difficult for beginners to select the most suitable tool or index for their objectives. Major difficulties are in the specificity of the terminology, differences among the sensors (e.g., active vs. passive sensors), interpretation of imaginary data, and multidisciplinary topics. This work offers non-expert readers basic knowledge of remote-sensing use in agriculture, presenting advancements in the field and future insights. We review different sensors that are frequently use...
Mapping Irrigated Lands at 250-m Scale by Merging MODIS Data and National Agricultural Statistics
2010
Accurate geospatial information on the extent of irrigated land improves our understanding of agricultural water use, local land surface processes, conservation or depletion of water resources, and components of the hydrologic budget. We have developed a method in a geospatial modeling framework that assimilates irrigation statistics with remotely sensed parameters describing vegetation growth conditions in areas with agricultural land cover to spatially identify irrigated lands at 250-m cell size across the conterminous United States for 2002. The geospatial model result, known as the Moderate Resolution Imaging Spectroradiometer (MODIS) Irrigated Agriculture Dataset (MIrAD-US), identified irrigated lands with reasonable accuracy in California and semiarid Great Plains states with overall accuracies of 92% and 75% and kappa statistics of 0.75 and 0.51, respectively. A quantitative accuracy assessment of MIrAD-US for the eastern region has not yet been conducted, and qualitative assessment shows that model improvements are needed for the humid eastern regions where the distinction in annual peak NDVI between irrigated and non-irrigated crops is minimal and county sizes are relatively small. This modeling approach enables consistent mapping of irrigated lands based upon USDA irrigation statistics and should lead to better understanding of spatial trends in irrigated lands across the conterminous United States. An improved version of the model with revised datasets is planned and will employ 2007 USDA irrigation statistics.