Awifs data are acceptable for crop acreage estimation over large crop areas such as the midwest, the delta and the northern great plains. Remote sensing phenology is able to consistently generate estimates of the start, peak, duration, and end of the growing season over large areas. A crop phenology detection method using timeseries modis. Diagnostic phenology 5 satellite remote sensing vegetation indices exploiting. However, crop growth is not only driven by natural conditions, but also modified. Synchronous response analysis of features for remote.
The rs data can provide information of crop environment, crop distribution, leaf area index lai, and crop phenology. Ground remote sensing instruments are very useful for smallscale operational field monitoring of biotic and abiotic stress agents. A hidden markov models approach for crop classification. Analysis of crop phenology using timeseries modis data. Introduction precision agriculture pa recognizes and manages intrafield spatial variability with the desired outcome of increasing profitability and reduced environmental impact 1. The potential of satelliteobserved crop phenology to.
Monitoring crop phenology is required for understanding intra and interannual variations of agroecosystems, as well as for improving yield prediction models. Remote sensing data assimilation for a prognostic phenology model. Monitoring seasonal changes in vegetation activity and crop phenology over wide areas is essential for many applications, such as estimation of net primary production kimball et al. This study used remote sensing satellite data and climate data to.
Objectbased crop identification using multiple vegetation. In this study, an attempt has been made to derive the spatial patterns of temporal trends in phenology metrics and productivity of crops grown, at disaggregated level in indogangetic plains of india igp, which are helpful in understanding the impact of climatic, ecological and socioeconomic drivers. However, images must be obtained at the correct point in the growing season because, at early growth stages, soil background effects will dominate the spectral reflectance. Phenology characteristics are effective parameters for crop classification and can be. Landsat tm and awifs assessments 20042005 nebraska, 2004. Pdf monitoring crop phenology using a smartphone based. Analysis of crop phenology using timeseries modis data and. This information is integrated in csm, in a number of. Crop phenology study based on multispectral remote sensing. In addition, existing assessments of crop yields using satellite remote sensing gao et al. The use of lowcost ground remote sensing methods to schedule nitrogen fertilization may contribute to more sustainable agriculture.
Remote sensing technology allows monitoring the progress of vegetation and crop phenology in large regions. Opportunities and limitations for imagebased remote. Finally, conclusions and future improvements are presented in section 5. In the optical remote sensing domain, highresolution images can provide rich spatial and spectral information 2,3 and vegetation indices, such as normalized differential crop index ndvi timeseries data, to improve the efficiency of crop classification. Mar 28, 2018 estimating awc with remote sensing and crop models an inversion protocol conducted with 3 models. Combining crop models and remote sensing for yield prediction. Historical remote sensing phenology rsp image data and graphics for the conterminous u. Opportunities and limitations for imagebased remote sensing in precision crop management m. Subsequently, dedicated remote sensing products are generated which, in turn, are used within the crop growth simulation model to estimate yield. Generally, either diagnostic or prognostic parameterizations of vegetation phenology are employed in these studies. Efficient crop type mapping based on remote sensing in the. Modeling crop phenology using remotely sensed data by. The project on remote sensing technologies for ecosystem management treaties.
For example, estimates of when crops reach certain growth stages can help estimate the length of the growing season or the timing of cessation of crop transpiration. The use of satellite remote sensing in mapping of crop type, health, and phenology has evolved to include a variety of applications in agricultural assessment including crop classification vina. Phenology is the study of periodic plant and animal life cycle events and how these are influenced by seasonal and interannual variations in climate, as well as habitat factors such as elevation examples include the date of emergence of leaves and flowers, the first flight of butterflies, the first appearance of migratory birds, the date of leaf colouring and fall in deciduous trees, the. Conus 1 km avhrr rsp data, eastern conus 250 m emodis rsp data, and western conus 250 m emodis rsp data. Reedb, alfredo huetec adepartment of geography and center for remote sensing, boston university, 675 commonwealth avenue, boston, ma 02215, usa beros data center, sioux falls, sd. Use of remote sensing in pa is influenced by the type of platforms satellite, air or ground used for data collection. Many investigations have addressed the topic of crop identification via remote sensing congalton et al. Photogrammetric engineering and remote sensing, 7211, 12251234. Seasonal vegetation trends are commonly estimated from high temporal resolution but coarse spatial resolution satellite imagery, e. In the eagle master program at the department of remote sensing, the students get to know a wide range of topics and applications of earth observation. Pdf monitoring crop phenology using a smartphone based near. Dynamic processbased crop simulation models are useful tool in predicting crop growth and yield in response to environmental and cultural factors but are constrained by lack of availability of the required large number of inputs when applied for. Most methods to detect phenological events based on satellite data use thresholds to identify key events in the lifecycle of the crop.
Remote sensing is being used with global positioning systems, geographic information systems, and variable rate technology to ultimately help farmers maximize the economic and environmental benefits of crop pest management through precision agriculture. An overview of usa crop production monitoring and the role of. Final technical report the project on remote sensing. Introduction precision agriculture pa recognizes and manages intrafield spatial variability with the desired outcome of increasing. Pdf remote sensing based detection of crop phenology for. Conclusion overview of a processbased crop model estimating awc with remote sensing and crop models an inversion protocol conducted with 3 models. Monitoring crop phenology in mato grosso brazil using. Efficient crop type mapping based on remote sensing in the central valley, california by liheng zhong a dissertation submitted in partial satisfaction of the requirements of the degree of doctor of philosophy in environmental science, policy and management in the graduate division of the university of california, berkeley committee in charge. The use of remote sensing data in faos crop growth model aquacrop to estimate actual crop yields has been evaluated. This technique was tested using proximal sensing, 6 m above the top of canopy, in maize and soybean gitelson et al. Pdf crop phenology study based on multispectral remote sensing.
Landsat and advanced very high resolution radiometer. Remote sensing based detection of crop phenology for. Accurate phenology information detection is the basis for other remotesensing based agriculture applications. Due to the large amount of the remote sensing data and the time consuming processing, the sar processing is performed using a high performing cluster solution, where. Remote sensing technology has the potential of revolutionizing the detection and characterization of agricultural productivity based on biophysical attributes of crops andor soils liaghat and balasundram, 2010.
Further, this research can be applied to remote sensing applications in which ground truth are not available. Estimation of crop evapotranspiration using satellite. Identifying corn and soybeans based on phenological profiles. Basics of remote sensing for agricultural applications. Classification of multitemporal spectral indices for crop. Remote sensing technology allows monitoring the progress of.
Short communication monitoring vegetation phenology using modis xiaoyang zhanga, mark a. Hufkens k, melaas ek, mann ml, foster t, ceballos f, robles m, kramer b 2019 agricultural and forest meteorology monitoring crop phenology using a smartphone based nearsurface remote sensing approach. Eros maintains a set of nine annual phenological metrics for the conterminous united states, all curated from satellite data. Taken together, the metrics represent a powerful tool for documenting life cycle trends and the impacts of climate change on ecosystems. Ir 36 carreon phenology species variability rice phenology and temperature photoperiod. Monitoring crop phenology using a smartphone based near. Remote sensing yield third method for yield estimates premise there is a relationship between crop iomass, vigor, greenness, ndvi and land surface temperature and the resulting crop yield utilize modis data to obtain biomass and temperature variables national, state, asd, and county corn and soybeans only. Monitoring crop phenology and disturbances to crop growth is critical in. This suite of phenology metrics was derived from timeseries collection 6 aqua emodis normalized difference vegetation index ndvi data. Timely monitoring and prediction of the trajectory of crop development provides scientific information to agronomists and climate scientists. This study used remote sensing satellite data and climate data to determine key phenological states of corn and soybean and evaluated estimates of these. Phenology is the study of periodic lifecycle events for example, flowering, insect emergence, nesting, migration and how these stages are affected by climate and environment.
A study on phenology detection of corn in northeastern. In section 3, we outline the method for attributing a crop type to main crop seasons recognized on lsp, whereas section 4 presents the results of this process. Airborne remote sensing is flexible and versatile because fields can be flown at variable. The assessment of crop progress and condition requires early. A dissertation submitted to the graduate faculty in partial fulfillment of the. In this context, phenocamtype data using smartphone cameras on the ground provide high. Remote sensed imagery can be used for mapping soil properties, classification of crop species, detection of crop water stress, monitoring of weeds and crop diseases, and mapping of crop yield. The main purpose of this chapter is to apply a new phenology detection.
Rundquist, galina keydan, bryan leavitt, and james schepers abstract of fullyexpanded leaves,n, designatedby v n andrepromonitoring crop phenology is required for understanding intra ductive from silking to physiological maturity. Crop phenology profiling stacked ndvi images with phenology profiling pixels selected. Elgindy, estimation of evapotranspiration etc and crop coefficient kc of wheat, in south nile delta of egypt using integrated fao56 approach and remote sensing data, egyptian journal of remote sensing and space science, vol. Objectbased crop identification using multiple vegetation indices, textural features and crop phenology. Linking crop phenology to time series of multisensor remote sensing data article pdf available in. Peng remote sensing of environment 115 2011 309101. Pdf improved regional yield prediction by crop growth. Phenology and temperature cv ir 30 phenology species variability temperature rice temperature, c 0 5 10 15 20 25 30 35 sowing to panicle emergence, d 0 100 200 300 400 500 600 700 s. Phenology is the study of plant and animal life cycles in relation to the seasons. Near surface remote sensing offers granular visual field data, providing detailed.
In monsoon asia, optical satellite remote sensing for rice paddy phenology suffers from atmospheric contaminations mainly due to frequent cloud cover. Modeling crop phenology using remotely sensed data by colin. Within the course from field measurements to geoinformation, the students learn how to collect field. Deriving crop phenology metrics and their trends using. So far, there have been a lot of phenology estimation models based on remote sensing data, but little attention was paid to microscopic mechanism of crops and the environmental factors. So far, there have been a lot of phenology estimation models based on remotesensing data, but little attention was paid to microscopic mechanism of crops and the environmental factors. Monitoring crop phenology using a smartphone based nearsurface. Improvements in classification accuracy are achieved due to increased temporal frequency of the awifs sensor 5 day vs. In such systems, many or most agents responsible for crop production tend to perform crop management within a similar time window, so that imagery can later capture synoptic and diagnostic. Oct 08, 2003 monitoring crop phenology is required for understanding intra and interannual variations of agroecosystems, as well as for improving yield prediction models. Pdf in recent years, the use of high temporal resolution satellite data has been emerging as an important tool to study crop phenology. A study on phenology detection of corn in northeastern china.
Development of a national and subnational crop calendars. Basics of remote sensing for agricultural applications introduction when farmers or ranchers observe their fields or pastures to assess their condition without physically touching them, it is a form of remote sensing. Silleos, n, misopolinos, n and perakis, k 1992 relationships between remote sensing spectral indices and crop discrimination. Agronomy journal abstract remote sensing monitoring. Furthermore, the methods used for computation of remote sensing phenology are shortly described. Short communication monitoring vegetation phenology using. The more favorable weather conditions, in comparison to the characteristic conditions of temperate regions, permit higher flexibility in land use, planning, and management, which implies complex crop. Forecasting yield by coupling remote sensing and crop model during the. Accurate crop type identification and crop area estimation from remote sensing data in tropical regions are still considered challenging tasks. The elements of phenology that can be estimated from remote sensing are necessarily more coarse than direct observations of individual plant phenology, such as bud burst or first leaf, but are rather summaries of the constituents of pixels and do not normally represent any one vegetation type. Remote sensing, crop phenology, multitemporal images, ndvi, precision agriculture, spatiotemporal variability 1.
Laibased crop phenology dataset with 1 km spatial resolu tion across china chinacropphen1km will benefit. Due to noise, multitemporal remote sensing datasets exhibit local variation in spectral reflectance, and thus vivalues, punctuated by missing values evident in figure 1, whereas, over time, crop growth follows a smooth trajectory in normal conditions events such as pest attacks, moisture shortage, natural disasters can. International journal of remote sensing, 292060456049. In the continental united states, key phenological stages are strongly influenced by meteorological and climatological conditions. The main purpose of this chapter is to apply a new phenology detection model, which combined. Modis daily data appropriate spectral configuration for vegetation monitoring global coverage spatial resolution too coarse. The advantages of utilizing remote sensing for phenology applications are the ability to capture the continuous expression of phenology patterns across the landscape and the ability to retrospectively observe phenology from archived satellite data sets e. Singha, m, wu, b and zhang, m 2016 an objectbased paddy rice classification using multispectral data and crop phenology in assam, northeast india. Combining crop models and remote sensing for yield. Phenology describes how organisms are specifically adapted to the environmental cycles that surround them and it applies to.
The 2018 remote sensing phenology metrics have been released. The elements of phenology that can be estimated from. The objective of this paper is to remotely evaluate the phenological development of maize zea mays l. Tcs research and innovation, tata consultancy services, thane, maharashtra, india. Evaluation of temporal resolution effect in remote sensing. Forecasting yield by coupling remote sensing and crop model during the crop season. Understanding crop phenology is fundamental to agricultural production, management, planning and decisionmaking. In essence data assimilation is the technique whereby remote sensing data are used as inputs in crop models, to adjust or reset state variables in crop models. Evaluation of temporal resolution effect in remote sensing based crop phenology detection studies 5 difference between survival data point and its inverse distance weighted idw estimation should be small than 0. Canopy cover and biomass, derived from coarse resolution ndvi time series, have been. Remote sensing derived phenological metrics to assess the. We evaluated the quality of satellite remote sensing of paddy phenology.
Agronomy journal abstract remote sensing monitoring maize. Pdf the study identifies various growing stages of rice crop using multispectral data through red edge analysis. Accurate phenology information detection is the basis for other remote sensing based agriculture applications. Remote sensing based crop yield monitoring and forecasting. Assessing remote sensing techniques for measuring vegetation. Smallholder farmers play a critical role in supporting food security in developing countries. Dongremote sensing based detection of crop phenology for agricultural zones in china using a new threshold method remote sens. Observing the colors of leaves or the overall appearances of plants can determine the plants condition.
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