Normalized Difference Vegetation Index Sensor Research Articles

Evaluation of Physiological Changes in Flowering Dogwood under Drought Conditions in a Container Production System

Flowering dogwoods (Cornus florida L.) are drought-sensitive ornamental trees. Two trials (in 2021 and 2022) were conducted to evaluate the physiological changes induced as a result of drought conditions. In an outdoor setting, trees were organized in a randomized complete block design. Three different irrigation treatments were applied at 125%, 25%, and 10% (control, moderate, and severe drought, respectively) of their daily water usage (evapotranspiration). The two physiological parameters normalized difference vegetation index (NDVI) and leaf moisture potential were collected every week for 1 month. Plant growth data (height and width) were collected at the beginning and the end of the study. Normalized difference vegetation index data collected with a handheld NDVI meter and a Sentera NDVI sensor mounted on an unmanned aerial vehicle (UAV) were correlated for ground truthing. In 2021, control plants had a greater plant width increase and shoot biomass, whereas no significant differences in growth were observed among the treatments in 2022. In both trials, the NDVI was the greatest for control plants compared with the other treatments on days 7, 14, 21, and 27. In both studies, no differences were observed for leaf moisture potential on day 7, but was greatest for controls on days 14, 21, and 27. The correlation between the handheld NDVI and the UAV NDVI was found to be strong and positive, ranging from 0.84 to 0.93 (trial 1: P ≤ 0.0001, P ≤ 0.0001, P = 0.0002, and P ≤ 0.0001; trial 2: P = 0.0002, P ≤ 0.0001, P ≤ 0.0001, and P ≤ 0.0001 for weeks 1–4, respectively). This information will be applicable to understanding the physiology of the crop and the inclusion of emerging technology in crop production and monitoring.

Optical sensor-based nitrogen management: an environmentally friendly and cost-effective approach for sustainable wheat (Triticum aestivum L.) production on Eastern plains of India

An optical sensor like Green Seeker (GS) is an emerging tool for site-specific in-season fertilizer nitrogen management strategy. The objective of this study was to establish an in-season estimate of yield (INSEY)–grain yield (GY) relation in wheat grown under Eastern plains of India using normalized difference vegetation index (NDVI) at 45 and 65 days after sowing (DAS). Data revealed lower NDVI values at 65 DAS over 45 DAS in no-nitrogen (N), phosphorus (P), and potassium (K) applied control plots as well as in N-rich plots (225 kg ha−1 N); on the contrary, the values were higher at 65 DAS over 45 DAS in treatments where some N fertilizers were added based on NDVI readings at 45 DAS. Response index (RI) showed higher chances of response to external application of N in NDVI-based treatments. The INSEY–GY relation for wheat at 45 and 65 DAS was worked out as a power function of y = 64265x1.171 and y = 46949x1.036 (y is the attainable yield in kg ha−1 and x is INSEY), respectively. The yields could fairly be predicted through this relation even at 45 DAS, though the relationship was more robust at 65 DAS (R2 = 0.94). A prescriptive dose of 60 kg N ha−1 as basal + 60 kg N ha−1 at crown root initiation (CRI) stage followed by NDVI sensor-guided N application (at 45 and 65 DAS) brought about a significant improvement in yield performances, N use efficiencies with higher net returns, and benefit-to-cost ratio. The results proved the reliability of the NDVI sensor as an important tool for the optimization of fertilizer nitrogen in wheat grown under the Eastern plains of India. The new INSEY–GY relation developed through this trial could successfully be used for yield prediction in the Eastern plains of India under changing climate.

Normalized difference vegetation index sensor-based nitrogen management in bread wheat (Triticum aestivum L.): Nutrient uptake, use efficiency, and partial nutrient balance.

The present experiment was conducted to assess the impact of fixed and variable doses (using a normalized difference vegetation index-sensor) of nitrogen (N) on wheat yields, nutrient uptake, nitrogen use efficiency, and soil nitrogen balance through the optimization of nitrogen dose. There were 10 treatments based on fixed and variable doses with different splits, and each treatment was replicated three times under a randomized complete block design. The treatments comprised fixed doses of 120 and 150kgNha-1 with different splits; variable doses based on sensor readings after application of 60, 90, and 120kgNha-1; 225kgNha-1 as a nitrogen-rich control; and no application of nitrogen as the absolute control. It was revealed that the application of a basal dose of 60kgNha-1 and another 60kgNha-1 at the crown root initiation stage followed by a sensor-guided N application significantly improved wheat grain yields and grain nitrogen uptake. However, straw nitrogen uptake was highest in N-rich plots where 225kgNha-1was applied. It was found that any curtailment in these doses at basal and crown root initiation stages followed by nitrogen application using a normalized difference vegetation index sensor later could not bring about higher crop yields. On average, wheat crops responded to 152-155kgNha-1 in both years of the study. Partial factor productivity along with agronomic and economic nitrogen use efficiency showed a declining trend with an increased rate of N application. Apparent N recovery values were comparable between normalized difference vegetation index sensor-based N application treatments and treatments receiving lesser N doses. Soil N status decreased in all the treatments except the nitrogen-rich strip, where there was a marginal increase in soil N status after the wheat crop harvest in the rotation. Partial nitrogen balance was negative for all the treatments except the control. From these 2-year field trials, it can be concluded that applying a normalized difference vegetation index sensor could be an essential tool for the rational management of fertilizer nitrogen in wheat grown in eastern sub-Himalayan plains.

Aerial and ground-based phenotyping of an alfalfa diversity panel to assess adaptation to a prolonged drought period in a Mediterranean environment of central Chile

Climate change is one of the biggest challenges facing food production worldwide, particularly in Mediterranean climate regions where rainfall has been decreasing and temperature increasing. Alfalfa is a perennial forage crop that could play an important role in increasing the resilience and sustainability of rainfed production systems in these environments. In this study, we tested how a prolonged drought period of 5–6 months in a Mediterranean environment of central Chile affects the plant persistence and productivity of an alfalfa diversity panel of 69 landraces, cultivars, and diverse pre-breeding populations (hereafter accessions) from Kazakhstan, Azerbaijan, Spain, Australia, USA, and Chile, with putative drought tolerance. The objectives were i) to evaluate the phenotypic variability in plant survival and productivity among the alfalfa diversity panel, under rainfed and with supplemental irrigation, in a Mediterranean environment; and ii) to study the use of an NDVI sensor, and RGB and thermal cameras for field phenotyping and evaluation of canopy traits, such as leaf area index (LAI) and forage yield (FY). Results showed large phenotypic variability for plant survival after the three drought periods, ranging from 30 % to 76 % under rainfed conditions and from 48 % to 90 % with supplementary irrigation. The FY during winter was significantly different between cultivars, with more than 4 Mg ha-1 per year in some accessions. The average FY for three growing seasons (2018–2020) ranged between 2.26 and 10.80 Mg ha-1 under rainfed and 3.92–11.14 Mg ha-1 per year with supplementary irrigation. Among the most productive accessions under both water regimes were two populations of the Medicago arborea x sativa hybrid (AF3448 and AF3347), cultivars Genesis and Venus, and the landraces Aragon from Spain and Alta Sierra 2 from Chile. The normalized difference vegetation index (NDVI) showed exponential and positive relationships with the leaf area index (LAI) and FY. RGB images were obtained at 60 cm above the top of the canopy with a digital camera and at 30 m using a drone. The RGB indices “intensity”, a* and greener area obtained from images taken at 60 cm above the top of the canopy, were highly correlated (r = 0.76 – 0.98; P < 0.0001) with the NDVI and FY, under rainfed and with supplementary irrigation in 2019. The RGB indices “saturation”, a* and u* obtained from a drone at 30 m, were also highly correlated (r > 0.73 – 0.94; P < 0.0001) with the NDVI and FY of accessions growing under both water regimens in 2020. The canopy temperature (CT) obtained from thermal images from a drone was higher under the rainfed regime. The stress degree day (SDD), defined as the difference between canopy temperature (Tc) and air temperature (Ta) was negatively related to FY. Under the rainfed regimen, the population AF3348 and cultivars Genesis and Aragon exhibited the lowest SDD and the highest FY.

Development of an Automated Linear Move Fertigation System for Cotton Using Active Remote Sensing

Optimum nitrogen (N) application is essential to the economic and environmental sustainability of cotton production. Variable-rate N fertigation could potentially help farmers optimize N applications, but current overhead irrigation systems normally lack automated site-specific variable-rate fertigation capabilities. The objective of this study was to develop an automated variable-rate N fertigation based on real-time Normalized Difference Vegetation Index (NDVI) measurements from crop sensors integrated with a lateral move irrigation system. For this purpose, NDVI crop sensors and a flow meter integrated with Arduino microcontrollers were constructed on a linear move fertigation system at the Edisto Research and Education Center in Blackville, South Carolina. A computer program was developed to automatically apply site-specific variable N rates based on real-time NDVI sensor data. The system’s ability to use the NDVI data to prescribe N rates, the flow meter to monitor the flow of N, and a rotary encoder to establish the lateral’s position were evaluated. Results from this study showed that the system could accurately use NDVI data to calculate N rates when compared to hand calculated N rates using a two-sample t-test (p &gt; 0.05). Linear regression analysis showed a strong relationship between flow rates measured using the flow meter and hand calculations (R2 = 0.95), as well as the measured distance travelled using the encoder and the actual distance travelled (R2 = 0.99). This study concludes that N management decisions can be automated using NDVI data from on-the-go handheld GreenSeeker crop sensors. The developed system can provide an alternative N application solution for farmers and researchers.

SURFACE MOISTURE INDEX BY RADIOMETRIC MEASUREMENTS AND ORBITAL DATA

As an indicator of surface moisture, TVDI has great potential for application in agriculture in different types of climates, but the best results have been obtained in arid climates, given the presence of areas with contrasting water conditions. Some studies under subtropical climate conditions have shown good results in the use of TVDI as an indicator of surface moisture but some uncertainties still need to be better understood. This study aimed to evaluate the coherence of temperature-vegetation dryness index (TVDI) values, obtained from sensors installed on the surface, compared to data obtained using orbital images in an agricultural area in southern Brazil under humid subtropical climate conditions. The analyses employed normalized difference vegetation index (NDVI) and surface temperature (TS) data obtained through sensors installed on the surface, with continuous monitoring, as well as Landsat OLI/TIRS images, covering the period of the soybean cycle. The TVDI values, obtained from NDVI and TS radiometric sensors installed in the field, indicated consistency of using Landsat-OLI/TIRS orbital images to map the surface moisture condition, representing the spatial and temporal variations of the water condition of soybean cultivations under subtropical climate conditions.

Estimating Leaf Chlorophyll Content of Buffaloberry Using Normalized Difference Vegetation Index Sensors

Commercial optical chlorophyll meters estimate relative chlorophyll content using the ratio of transmitted red light and near-infrared (NIR) light emitted from a red light-emitting diode (LED) and an NIR LED. Normalized difference vegetation index (NDVI) sensors have red and NIR light detectors and may be used to estimate chlorophyll content by detecting the transmitted red and NIR light through leaves. In this study, leaf chlorophyll content of ‘Torrey’ buffaloberry (Shepherdia ×utahensis) plants treated with 0 mm [zero nitrogen (N)], 2 mm (medium N), or 4 mm (ample N) ammonium nitrate for 3 weeks were evaluated using two commercial chlorophyll meters and NDVI sensors. The absolute chlorophyll content was determined using chlorophyll extraction. Our results showed that plants receiving ample N and medium N had decreased transmitted red light (i.e., greater absorption in red light). Measurements of optical chlorophyll meters, NDVI sensors, and chlorophyll extraction similarly showed that plants receiving medium N and ample N had greater leaf chlorophyll content than those receiving zero N. Relative leaf chlorophyll content estimated using NDVI sensors correlated positively with those from the chlorophyll meters (P &lt; 0.0001; r2 range, 0.56–0.82). Therefore, our results indicate that NDVI measurements are sensitive to leaf chlorophyll content. These NDVI sensors, or specialized sensors developed using similar principles, can be used to estimate the relative chlorophyll content of nursery crops and help growers adjust fertilization to improve plant growth and nutrient status.

COMPARING A SINGLE-SENSOR CAMERA WITH A MULTISENSOR CAMERA FOR MONITORING COFFEE CROP USING UNMANNED AERIAL VEHICLES

There exist two options for digital cameras that can capture the near-infrared (NIR) band. Conventional red–green–blue (RGB, visible bands) cameras with a single sensor provide NIR band visibility based on the removal of the internal NIR-blocking filter. Alternatively, multisensor cameras exist that have a specific sensor for each band. The modified RGB cameras are of a lower price. In this context, the objective of this study was to compare the performance of a modified RGB camera with that of a multisensor camera for obtaining the normalized difference vegetation index (NDVI) in an area with coffee cultivations. A multispectral camera with five sensors and another camera with only one sensor were used. The NDVI of the coffee field was also measured using the GreenSeeker handheld NDVI sensor manufactured by Trimble. The images were calibrated radiometrically based on the targets in shades of gray made of napa, and the NDVI was calculated after image calibration. The calibration curves showed a high coefficient of determination. The NDVI value obtained with the calibrated images from the cameras showed a significant correlation with the values obtained by the GreenSeeker NDVI sensor, making it possible to obtain the variability pattern of the vegetation index. However, the NDVI obtained using the multisensor camera was closer to the NDVI obtained by the GreenSeeker NDVI sensor.

Impact of Varying Light and Dew on Ground Cover Estimates from Active NDVI, RGB, and LiDAR.

Canopy ground cover (GC) is an important agronomic measure for evaluating crop establishment and early growth. This study evaluates the reliability of GC estimates, in the presence of varying light and dew on leaves, from three different ground-based sensors: (1) normalized difference vegetation index (NDVI) from the commercially available GreenSeeker®; (2) RGB images from a digital camera, where GC was determined as the portion of pixels from each image meeting a greenness criterion (i.e., (Green − Red)/(Green + Red) > 0); and (3) LiDAR using two separate approaches: (a) GC from LiDAR red reflectance (whereby red reflectance less than five was classified as vegetation) and (b) GC from LiDAR height (whereby height greater than 10 cm was classified as vegetation). Hourly measurements were made early in the season at two different growth stages (tillering and stem elongation), among wheat genotypes highly diverse for canopy characteristics. The active NDVI showed the least variation through time and was particularly stable, regardless of the available light or the presence of dew. In addition, between-sample-time Pearson correlations for NDVI were consistently high and significant (P < 0.0001), ranging from 0.89 to 0.98. In comparison, GC from LiDAR and RGB showed greater variation across sampling times, and LiDAR red reflectance was strongly influenced by the presence of dew. Excluding times when the light was exceedingly low, correlations between GC from RGB and NDVI were consistently high (ranging from 0.79 to 0.92). The high reliability of the active NDVI sensor potentially affords a high degree of flexibility for users by enabling sampling across a broad range of acceptable light conditions.

Remote Sensing Analysis of Agricultural Drone

Farmers have more requirements for the completion of cultivations. Remote sensing is a big technology for reducing this requirement. Now, we need an organic spraying system at a low cost. We have two methods, first one neural network algorithm of quantum geographic information system (QGIS) and another one global positioning system (GPS) with drone. This paper describes the analysis of drone remote sensing using the normalized difference vegetation index (NDVI)/Near-infrared band (NIR) sensor in a multispectral view of agricultural land. NIR and NDVI images had water content values and precision values which is mixed in managing water resources. NDVI sensors are loaded to produce high-density images. Real-time monitoring coupled in NIR imaging geometrically and radiometrically adjusted to measure temperature. Multispectral and hyperspectral views had used for analyzing the tested data. Standard irrigation level is 60% to produce the plant growing. Irrigation techniques followed the treatment of the plant within continuous data per second. The implemented view focused only on growth controlling of plant in-depth irrigation between 30 and 90 cm in 60% deviation. NDVI, green normalized difference vegetation index (GNDVI), soil brightness index (SBI), green vegetation index (GVI), degree of yellow vegetation index (YVI), nitrogen sufficiency index (NSI), perpendicular vegetation index (PVI), transformed vegetation index (TVI), soil adjusted vegetation index (SAVI) and vegetation condition index (VCI) vegetation indices are used to the correlation of plant growth control with managing leaf strength and import python packages display the Vegetation various Real-time value in QGIS. Correlation of plant growth p ≤0.01, r = 0.77 and − 0.77 with conductance. It measured degree and demonstrated GPS view using irrigation techniques to control water stress. It had used to estimate the leaf conductance rate with the variation of atmospherically changing. It can calculate real-time leaf stress analysis. This report provided a drone survey analysis of compost percentage and vegetation indices of agricultural land.

Estimation of nitrogen uptake and tiller number of winter wheat using a handheld optical sensor in Hokkaido, Japan

ABSTRACT It is essential to manage nitrogen (N) fertilization to obtain the appropriate protein contents in wheat grains. In this study, N uptake from the booting stage to flag leaf appearance, and the tiller number at the booting and panicle formation stages was estimated using a handheld active optical sensor of the normalized difference vegetation index (NDVI) under different measurement conditions in Hokkaido, Japan. A stable NDVI was obtained if measured horizontally at 60–120 cm height from the canopy regardless of ambient radiation at different local times . However, the relationship between N uptake and NDVI differed significantly if the NDVI was measured at a 20 cm height (P < 0.01) or at 45° angle to the soil surface (P < 0.001). Regression analysis revealed that N uptake could be fitted by a quadratic curve of NDVI (N uptake = 0.39 + 17.4 × NDVI2; P < 0.001) across the different regions, years, and growth stages if the NDVI was less than 0.75. However, the accuracy was poor if the NDVI was higher than 0.75 probably because the emitted and reflected light was scattered in the canopy owing to increased leaf area and causing saturation of the NDVI. Significant positive correlations were observed between tiller number and NDVI at booting (P < 0.001) and panicle formation (P < 0.001). However, the relationship at the booting stage differed among regions and years. The significant positive correlation between the residuals of the relationship and NDVI of bare soil (P < 0.001) suggested that the NDVI at booting was influenced by soil conditions owing to low green coverage. In conclusion, it is expected the rapid monitoring of N uptake and tiller number of winter wheat using an active optical NDVI sensor will be helpful for simple and rapid decision-making for N fertilization management.

Changes in stay-green and nitrogen use efficiency traits in historical set of winter barley cultivars

The objective of this study was to compare grain yield, nitrogen use efficiency (NUE) and stay-green traits in winter barley under low and high nitrogen (N) fertilization treatments. Field trials were conducted during two growing seasons in a southern Pannonian location (Novi Sad, Serbia) with fifteen six-rowed barley cultivars released between 1972 and 2013. The rate of genetic gain in grain yield was higher under high N level, indicating that modern barley cultivars use the applied N fertilizer more efficiently than older cultivars. The improvement in NUE and N uptake efficiency were positively related with year of cultivar release under both fertilization levels, while association between N utilization efficiency and year of cultivar release was significant at high N fertilization. Modern cultivars had higher normalized difference vegetation index (NDVI) values at the anthesis, start and mid senescence. Moreover, time to mid senescence was prolonged in modern cultivars, indicating slower N remobilization during early grain filling period. Values of NDVI at different senescence stages were more related to N uptake efficiency, while timing of senescence stages were in association with N utilization. Close association between NDVI values at different senescence stages and N uptake indicate that application of NDVI sensors could be used for identification of cultivars with improved N productivity.

Using Unmanned Aerial Systems (UAS) to assay mangrove estuaries on the Pacific coast of Costa Rica.

Mangrove forests, one of the world’s most endangered ecosystems, are also some of the most difficult to access. This is especially true along the Pacific coast of Costa Rica, where 99% of the country’s mangroves occur. Unmanned Aerial Systems (UAS), or drones, have become a convenient tool for natural area assessment, and offer a solution to the problems of remote mangrove monitoring. This study is the first to use UAS to analyze the structure of a mangrove forests within Central America. Our goals were to (1) determine the forest structure of two estuaries in northwestern Costa Rica through traditional ground measurements, (2) assess the accuracy of UAS measurements of canopy height and percent coverage and (3) determine whether the normalized difference vegetation index (NDVI) could discriminate between the most abundant mangrove species. We flew a UAS equipped with a single NDVI sensor during the peak wet (Sept–Nov) and dry (Jan–Feb) seasons. The structure and species composition of the estuaries showed a possible transition between the wet mangroves of southern Costa Rica and the drier northern mangroves. UAS-derived measurements at 100 cm/pixel resolution of percent canopy coverage and maximum and mean canopy height were not statistically different from ground measurements (p > 0.05). However, there were differences in mean canopy height at 10 cm/pixel resolution (p = 0.043), indicating diminished returns in accuracy as resolution becomes extremely fine. Mean NDVI values of Avicennia germinans (most abundant species) changed significantly between seasons (p < 0.001). Mean NDVI of Rhizophora racemosa (second most abundant species) was significantly different from A. germinans and dry forest dominant plots during the dry season (p < 0.001), demonstrating NDVI’s capability of discriminating mangrove species. This study provides the first structural assessment of the studied estuaries and a framework for future studies of mangroves using UAS.

Canada & Vietnam: Argex & Hung Thinh – TiO2 pigment

The objective of this study was to compare grain yield, nitrogen use efficiency (NUE) and stay-green traits in winter barley under low and high nitrogen (N) fertilization treatments. Field trials were conducted during two growing seasons in a southern Pannonian location (Novi Sad, Serbia) with fifteen six-rowed barley cultivars released between 1972 and 2013. The rate of genetic gain in grain yield was higher under high N level, indicating that modern barley cultivars use the applied N fertilizer more efficiently than older cultivars. The improvement in NUE and N uptake efficiency were positively related with year of cultivar release under both fertilization levels, while association between N utilization efficiency and year of cultivar release was significant at high N fertilization. Modern cultivars had higher normalized difference vegetation index (NDVI) values at the anthesis, start and mid senescence. Moreover, time to mid senescence was prolonged in modern cultivars, indicating slower N remobilization during early grain filling period. Values of NDVI at different senescence stages were more related to N uptake efficiency, while timing of senescence stages were in association with N utilization. Close association between NDVI values at different senescence stages and N uptake indicate that application of NDVI sensors could be used for identification of cultivars with improved N productivity.

Radiometric calibration assessments for UAS-borne multispectral cameras: Laboratory and field protocols

The main objective of this study was to develop and test a framework that can be used by Unmanned Aerial Systems (UAS) operators with varying technical backgrounds to estimate the accuracy and reliability of multispectral (visible and Near-Infrared or NIR) sensor measurements. We evaluated the performance of two multispectral sensors – the MicaSense RedEdge and the Airinov MultiSpec 4C – in both a laboratory and field setting. In the laboratory, we measured the reflectance of a number of reference target materials using each UAS sensor, and compared the values to those measured using a calibrated spectrometer. We found a strong linear relationship between the measurements made by the MicaSense RedEdge and the spectrometer, while the relationship was much weaker for the Airinov MultiSpec 4C, particularly in the longer wavelength bands (red-edge and NIR). A sub-set of the target materials were selected as ground reference targets for three field calibration exercises. In field calibration assessment No. 1, imagery was collected using each UAS sensor and reflectance values were extracted from pixels covering the ground reference targets. The extracted values were compared to the reflectance values acquired in the laboratory, and both UAS sensors were found to over-estimate reflectance, with lower accuracy in red-edge and NIR bands. Field calibration assessment No. 2 involved a calculation of Normalized Difference Vegetation Index (NDVI) values at field control points using both UAS sensors, and we found a strong linear relationship between the NDVI values and measurements made by a hand-held NDVI sensor, suggesting that the calculation of a normalized band ratio (i.e., NDVI) effectively reduces the reflectance measurement inaccuracy that we observed previously. Field calibration assessment No. 3 included image acquisition of ground reference targets using the MicaSense RedEdge sensor over seventeen sequential field surveys. Results revealed measurement variability over time, suggesting that daily differences in solar illumination and atmospheric conditions may influence derived reflectance values. In light of these results, we propose simplified procedures that can be adopted by UAS operators to periodically assess the radiometric fidelity of their multispectral sensors.

Evaluation of Five Methods to Measure Normalized Difference Vegetation Index (NDVI) in Apple and Citrus

ABSTRACTNormalized Difference Vegetation Index (NDVI) is a common remote sensing calculation used to assess green biomass in addition to nutrient, pest, and water stress. The approach used in NDVI has potential for a broader use of remote sensing of plant physiological status. The purpose of the study was to evaluate techniques to that utilize combined near-infrared (NIR) and visible detection by low cost web cameras in comparison with a dedicated NDVI sensor and direct measurement of red-green-blue (RGB) and NIR bands to calculate NDVI and identify water stress. Established techniques as well as new techniques for NDVI computation from cameras were evaluated and compared to soil plant analysis development (SPAD) chlorophyll meter values to assess the accuracy of NDVI for predicting plant stress in apple and citrus. NDVI is a useful tool when evaluating long-term crop changes such as pest damage, chronic water shortage, and nutrient deficiencies that affect chlorophyll but NDVI is not useful for acute stresses such as an irrigation pump failure or plugged irrigation lines that have an effect within days.

A ground based platform for high throughput phenotyping

The objective of this effort was to evaluate current commercially-available sensor technology (three sonic ranging and two NDVI sensors) for use in a ground-based platform for plant phenotyping and crop management decisions. The Global Positioning System (GPS) receiver from Trimble provided a high level of accuracy during our tests. Normalized Difference Vegetation Index (NDVI) data collected using the GreenSeeker sensors were more consistent and presented less variability when compared to the Decagon SRS sensor. The consistency could be due to the GreenSeeker system averaging readings of more rows. The tests also indicated that although sonic ranging sensor technology may be employed to obtain average plant height estimates, the technology is still a limiting factor for high-accuracy measurements at the plant level.

The role of directional LAI in determining the fAPAR–NDVI relationship when using active optical sensors in tall fescue (Festuca arundinacea) pasture

ABSTRACTRemote sensing based spectral indices, such as the normalized difference vegetation index (NDVI), are often used to estimate the fraction of absorbed photosynthetically active radiation (fAPAR) in plant canopies. Owing to similar changes in both the NDVI and fAPAR as functions of varying solar illumination angle when using entirely passive sensors, the fAPAR–NDVI relationships are often stable, appearing insensitive to solar illumination angle. Active optical sensors (AOS) on the other hand, which have their own illuminating light source and are increasingly being used to measure NDVI (NDVIAOS), do not respond to solar illumination geometry. Yet, the passive sensor-derived fAPAR component of the fAPAR–NDVIAOS relationship remains affected by solar illumination angle. In this paper, a simple two-stream canopy model has been used to predict the fAPAR–NDVIAOS relationships of a pasture canopy (tall fescue; Festuca arundinacea) for a nadir-viewing active optical NDVI sensor under conditions of varying solar elevation angle. Both the model derived and subsequent experimental measurements of the fAPAR–NDVIAOS relationship in this pasture confirmed a strong dependence of the linear fAPAR–NDVIAOS relationships on solar illumination angle. The modelled fAPAR–NDVIAOS relationship only agreed with the field measurements when the ‘solar angle–dependent directional leaf area index’ (LAIθs) of the canopy, as presented to the incoming solar photons, was used as opposed to the traditionally used ‘nadir version’ of the leaf area index (LAI). Users of AOS to measure indices such as NDVI must account for the solar illumination angle dependent LAIθs when considering any fAPAR–NDVIAOS relationship.

Algorithms for In‐Season Nutrient Management in Cereals

The demand for improved decision‐making products for cereal production systems has placed added emphasis on using plant sensors in‐season, and that incorporate real‐time, site specific, growing environments. The objectives of this work were to describe validated in‐season sensor‐based algorithms presently being used in cereal grain production systems for improving nitrogen use efficiency (NUE) and cereal grain yields. A review of research programs in the central Great Plains that have developed sensor‐based N recommendations for cereal crops was performed. Algorithms included multiple land‐grant university, government, and industry programs. A common thread in this review is the use of active sensors, particularly those using the normalized difference vegetation index (NDVI) for quantifying differences in fertilized and non‐fertilized areas, within a specific cropping season. In‐season prediction of yield potential over different sites and years is possible using NDVI, planting date, sensing date, cumulative growing degree days (GDD), and rainfall. Other in‐season environment‐specific inputs have also been used. Early passive sensors have advanced to by‐plant N fertilization using active NDVI and by‐plant statistical properties. Most recently, sensor‐based algorithm research has focused on the development of generalized mathematical models for determining optimal crop N application. The development and promotion of fee‐based modeling approaches for nutrient management continues. Nonetheless, several algorithms using active sensors for in‐season N management are available from state and government sources at no cost and that have been extensively field tested and can be modified by producers.Core Ideas Normalized difference vegetation index algorithms can improve fertilizer N efficiency. Normalized difference vegetation index sensors currently sold employ these algorithms. Algorithms rely on knowledge that increased yields increase fertilizer N demand. Yield potential and N response are independent. Nitrogen‐rich strips help to predict in‐season grain yields.

The Study of Applicability to Fixed-field Sensor for Normalized Difference Vegetation Index (NDVI) Monitoring in Cultivation Area

The NDVI (Normalized difference vegetation index) is used as indicators of crop growth situation in remote sensing. To measure or validate the NDVI, reliable NDVI sensors have been needed. We tested new fixed-field NDVI sensor, “SRS (Spectral Reflectance Sensor)” developed by Decagon Devices, during Kimchi cabbage growing season at the cultivation area located in Gochang, Gangneung and Taebaek in Korea from 2014 to 2015. The diurnal variation of NDVI measured by SRS (SRS NDVI) showed a slight ∩-profile shape and was affected by water on the sensor surface. This means that SRS NDVI around noontime is resonable, except rainy day. Comparisons were made between the SRS NDVI and NDVI of used widely mobile sensor (Cropcircle NDVI). The comparisons indicate that SRS NDVI are close to Cropcircle NDVI (R=0.99). SRS NDVI time series displayed change of the plant height and leaf width of Kimchi cabbage. An obvious exponential relationship is found between SRS NDVI and the plant height (R 2 ≥0.92) and leaf width(R 2 ≥0.92) of Kimchi cabbage. Thus, SRS NDVI will be used as indicator of crop growth situation and a very powerful tool for evaluation of remote sensing NDVI estimates and associated corrections.