https://ccc.inaoep.mx/~mdprl/documentos/17062015.pdf

Twenty five years of remote sensing in precision agriculture: Key advances and remaining knowledge gaps

with the Agricultural Research Service (ARS) and various government agencies and private institutions have provided a great deal of fundamental information relating spectral reflectance and thermal emittance properties of soils and crops to their agronomic and biophysical characteristics. This knowledge has facilitated the development and use of various remote sensing methods for non-destructive monitor- ing of plant growth and development and for the detection of many environmental stresses which limit plant productivity. Coupled with rapid advances in computing and position- locating technologies, remote sensing from ground-, air-, and space-based platforms is now capable of providing detailed spatial and temporal information on plant response to their local environment that is needed for site specific agricultural management approaches. This manuscript, which empha- sizes contributions by ARS researchers, reviews the biophysi- cal basis of remote sensing; examines approaches that have been developed, refined, and tested for management of water, nutrients, and pests in agricultural crops; and as- sesses the role of remote sensing in yield prediction. It con- cludes with a discussion of challenges facing remote sens- ing in the future.

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Remote Sensing for Crop Management

Remote sensing has provided valuable insights into agronomic management over the past 40 yr. The contributions of individuals to remote sensing methods have lead to understanding of how leaf reflectance and leaf emittance changes in response to leaf thickness, species, canopy shape, leaf age, nutrient status, and water status. Leaf chlorophyll and the preferential absorption at different wavelengths provides the basis for utilizing reflectance with either broad-band radiometers typical of current satellite platforms or hyperspectral sensors that measure reflectance at narrow wavebands. Understanding of leaf reflectance has lead to various vegetative indices for crop canopies to quantify various agronomic parameters, e.g., leaf area, crop cover, biomass, crop type, nutrient status, and yield. Emittance from crop canopies is a measure of leaf temperature and infrared thermometers have fostered crop stress indices currently used to quantify water requirements. These tools are being developed as we learn how to use the information provided in reflectance and emittance measurements with a range of sensors. Remote sensing continues to evolve as a valuable agronomic tool that provides information to scientists, consultants, and producers about the status of their crops. This area is still relatively new compared with other agronomic fields; however, the information content is providing valuable insights into improved management decisions. This article details the current status of our understanding of how reflectance and emittance have been used to quantitatively assess agronomic parameters and some of the challenges facing future generations of scientists seeking to further advance remote sensing for agronomic applications.

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Application of Spectral Remote Sensing for Agronomic Decisions

Crop monitoring is important in precision agriculture. Estimating above-ground biomass helps to monitor crop vitality and to predict yield. In this study, we estimated fresh and dry biomass on a summer barley test site with 18 cultivars and two nitrogen (N)-treatments using the plant height (PH) from crop surface models (CSMs). The super-high resolution, multi-temporal (1 cm/pixel) CSMs were derived from red, green, blue (RGB) images captured from a small unmanned aerial vehicle (UAV). Comparison with PH reference measurements yielded an R2 of 0.92. The test site with different cultivars and treatments was monitored during “Biologische Bundesanstalt, Bundessortenamt und CHemische Industrie” (BBCH) Stages 24–89. A high correlation was found between PH from CSMs and fresh biomass (R2 = 0.81) and dry biomass (R2 = 0.82). Five models for above-ground fresh and dry biomass estimation were tested by cross-validation. Modelling biomass between different N-treatments for fresh biomass produced the best results (R2 = 0.71). The main limitation was the influence of lodging cultivars in the later growth stages, producing irregular plant heights. The method has potential for future application by non-professionals, i.e., farmers.

Estimating Biomass of Barley Using Crop Surface Models (CSMs) Derived from UAV-Based RGB Imaging

https://pubag.nal.usda.gov/download/46603/PDF

Review: Sensing technologies for precision specialty crop production

ground-based booms, aircraft, or satellites—is a potentially important source of data for site-specific crop manRemote sensing—the process of acquiring information about obagement, providing spatial and temporal information jects from remote platforms such as ground-based booms, aircraft, or satellites—is a potentially important source of data for site-specific (NRC, 1997). Obtaining temporal information that is crop management, providing both spatial and temporal information. detailed and spatially distributed from other site-specific Our objective was to use remotely sensed imagery to compare differ- crop management methods is difficult and expensive ent vegetation indices as a means of assessing canopy variation and (NRC, 1997). Where available, remotely sensed images its resultant impact on corn (Zea mays L.) grain yield. Treatments show spatial and spectral variations resulting from soil consisted of five N rates and four hybrids, which were grown under and crop characteristics. One potential advantage of irrigation near Shelton, NE on a Hord silt loam in 1997 and 1998. remote-sensing imagery is that it is not limited by samImagery data with 0.5-m spatial resolution were collected from aircraft pling interval or geostatistical interpolation, as has been on several dates during both seasons using a multispectral, four-band implied for grid-sampled soil test data (Moran et al., [blue, green, red, and near-infrared reflectance] digital camera system. 1997). Imagery was imported into a geographical information system (GIS) and then georegistered, converted into reflectance, and used to com- For more than 30 yr, remote sensing has been envipute three vegetation indices. Grain yield for each plot was determined sioned as a valuable source of information for crop at maturity. Results showed that green normalized difference vegeta- management. The pioneering research of Colwell (1956) tion index (GNDVI) values derived from images acquired during showed that infrared aerial photography could be used midgrain filling were the most highly correlated with grain yield; to detect loss of vigor from disease in wheat (Triticum maximum correlations were 0.7 and 0.92 in 1997 and 1998, respectively. aestivum L.) and other small grains. One of the earliest Normalizing GNDVI and grain yield variability within hybrids im- digital remote-sensing analysis procedures developed to proved the correlations in both years, but more dramatic increases identify the vegetation contribution in an image was the were observed in 1997 (0.7 to 0.82) than in 1998 (0.92 to 0.95). This ratio vegetation index (RVI), created by dividing nearsuggested GNDVI acquired during midgrain filling could be used infrared reflectance (NIR) by red reflectance (Jordan, to produce relative yield maps depicting spatial variability in fields,

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James M. Tringe

Papers

Use of Remote-Sensing Imagery to Estimate Corn Grain Yield