Normalized Difference Vegetation Index Sensor Research Articles

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.

Monitoring seasonal and diurnal changes in photosynthetic pigments with automated PRI and NDVI sensors

Abstract. The vegetation indices normalized difference vegetation index (NDVI) and photochemical reflectance index (PRI) provide indicators of pigmentation and photosynthetic activity that can be used to model photosynthesis from remote sensing with the light-use-efficiency model. To help develop and validate this approach, reliable proximal NDVI and PRI sensors have been needed. We tested new NDVI and PRI sensors, "spectral reflectance sensors" (SRS sensors; recently developed by Decagon Devices, during spring activation of photosynthetic activity in evergreen and deciduous stands. We also evaluated two methods of sensor cross-calibration – one that considered sky conditions (cloud cover) at midday only, and another that also considered diurnal sun angle effects. Cross-calibration clearly affected sensor agreement with independent measurements, with the best method dependent upon the study aim and time frame (seasonal vs. diurnal). The seasonal patterns of NDVI and PRI differed for evergreen and deciduous species, demonstrating the complementary nature of these two indices. Over the spring season, PRI was most strongly influenced by changing chlorophyll : carotenoid pool sizes, while over the diurnal timescale, PRI was most affected by the xanthophyll cycle epoxidation state. This finding demonstrates that the SRS PRI sensors can resolve different processes affecting PRI over different timescales. The advent of small, inexpensive, automated PRI and NDVI sensors offers new ways to explore environmental and physiological constraints on photosynthesis, and may be particularly well suited for use at flux tower sites. Wider application of automated sensors could lead to improved integration of flux and remote sensing approaches for studying photosynthetic carbon uptake, and could help define the concept of contrasting vegetation optical types.

Optimum Monitoring Time for the Normalized Difference Vegetation Index of Crops

<abstract> Influenced by such factors as air temperature, humidity, solar radiation, and soil moisture, the normalized difference vegetation index (NDVI) for remote sensing of vegetation always changes dynamically within a day. Therefore, choosing the proper time to monitor the index is very important so as to minimize the effects of these factors. In this study, the NDVIs of soybean, cotton, pasture, regular grass, and weeds were measured with a GreenSeeker handheld NDVI sensor for six consecutive days. Furthermore, SPSS statistical software was used to test the differences in the NDVI values between different times for the five types of vegetation. The optimum monitoring time was determined as the period with minimum NDVI change by investigating the NDVI changes within a day and during the days after rainfall. The results showed that the most suitable time within a day for monitoring the NDVI was from 10:00 to 12:00 in the morning, and the best time was from 10:00 to 11:00. The most suitable time for monitoring the NDVI after saturating rainfall was from the 2nd day to the 4th day, and the best time was from the 3rd day to the 4th day.

Evaluating temporal consistency of long-term global NDVI datasets for trend analysis

As a way to understand vegetation changes, trend analysis on NDVI (normalized difference vegetation index) time series data have been widely performed at regional to global scales. However, most long-term NDVI datasets are based upon multiple sensor systems and unsuccessful corrections related to sensor shifts potentially introduce substantial uncertainties and artifacts in the analysis of trends. The temporal consistency of NDVI datasets should therefore be evaluated before performing trend analysis to obtain reliable results. In this study we analyze the temporal consistency of multi-sensor NDVI time series by analyzing the co-occurrence between breaks in the NDVI time series and sensor shifts from GIMMS3g (Global Inventory Modeling and Mapping Studies 3rd generation), VIP3 (Vegetation Index and Phenology version 3), LTDR4 (Long Term Data Record version 4) and SPOT-VGT (Système Pour l'Observation de la Terre VEGETATION). Single sensor time series from MODIS (MODerate Resolution Imaging Spectroradiometer) Terra and Aqua are used as reference datasets. The global land surface is divided into six regions according to the world humidity zones and averaged NDVI time series in each region are analyzed separately using a multiple structural change detection approach. We find that artifacts exist in the VIP3 and LTDR4 NDVI datasets with an abrupt shift detected in all humidity zones coinciding with the shift from NOAA-9 to NOAA-11 in 1988 and that orbital drift effects are evident in arid regions, potentially introducing uncertainties in NDVI trend analysis. Platform/sensor change from VGT-1 to VGT-2 is found to cause a significant positive break in the SPOT-VGT NDVI time series. Potential artifacts exist in humid, dry-subhumid, semi-arid and hyper-arid regions of GIMMS3g NDVI, whereas no signs of artifacts are found in the arid region. Although temporal consistency throughout all examined datasets increases after 2000 due to the usage of advanced platforms and sensors, variations in NDVI values from 2010 to 2011 still result in different trends at global and regional scales.

Canopy‐Based Normalized Difference Vegetation Index Sensors for Monitoring Cotton Nitrogen Status

Crop reflectance using ground‐based sensors has the potential to provide information on crop N status in real‐time. However, the unique physiological and structural characteristics of cotton (Gossypium hirsutum, L.) complicate normalized difference vegetation index (NDVI)‐based inferences on cotton N status. Therefore, the objectives of this study were to (i) determine the sensitivity of selected sensors and resulting NDVI measurements to different levels of fertilizer N; (ii) determine relationships between sensors and biophysical measurements; and (iii) compare absolute NDVI readings reported by each sensor. Field trials were conducted during crop years conducted from 2008 through 2010 at Mississippi State, MS. Fertilizer N rates of 0, 45, 90, and 135 kg N ha–1 were applied and replicated four times to establish N‐induced growth differences. Sensors used included the Yara N Sensor (Yara International ASA, Oslo, Norway), GreenSeeker (GS) Model 505 Optical Sensor Unit (NTech Industries, Inc., Ukiah, CA) and Crop Circle (CC) Model ACS‐210 (Holland Scientific, Inc., Lincoln, NE). Sensor readings failed to consistently predict cotton leaf N status before early flowering; however, plant height relationships with NDVIs were strong. Comparison of sensor response curves to fertilizer N resulted in no significant differences in slope values, although consistent, significant differences in y intercepts were noted beginning at early flowering and continuing through peak flowering. Greater accuracy in the detection of cotton leaf N status may require the utilization of an index which is less responsive to changes in plant height or canopy architecture. Furthermore, the magnitude of canopy reflectance‐based NDVI values differed across sensors.

Generalized Algorithm for Variable‐Rate Nitrogen Application in Cereal Grains

Many different mathematical algorithms have been developed and used in conjunction with commercial sensors for sensor‐based nutrient management. Several of the N algorithms have led to the precise mid‐season prediction of yields and calculation of sidedress N rates. The original Oklahoma State University (OSU) algorithm identified several limitations that were addressed in this study. Based on data analyses from more than 390 winter wheat (Triticum aestivum L.) and 200 corn (Zea mays L.) experiments and analyses of more than 100 N‐rich strips, a generalized algorithm (for both corn and wheat) was developed to estimate the optimum N application rate based on spectral measurements. The generalized model adjusts the yield calibration curve for growth stages and better predicts corn and wheat yields. The coefficients of determination of the generalized model explained 5 to 6% less of the model error than the individual regressed data for both crops. Mean absolute error (MAE) was approximately 0.9 Mg/ha greater with the generalized model than with the individually regressed model. The larger MAE with the OSU generalized model was due to sensitivity to location of the inflection point; however, this sensitivity did not impact the calculated fertilizer rates. The generalized model reported here using normalized difference vegetation index sensor measurements collected midseason can be used to apply fertilizer N with changing growth stage for both corn and wheat.

Use of an Active Canopy Sensor and SPAD Chlorophyll Meter to Quantify Geranium Nitrogen Status

This research was conducted to investigate the potentials of normalized difference vegetation index (NDVI), a Soil-Plant Analyses Development (SPAD) chlorophyll meter, and leaf nitrogen (N) concentration [% dry matter (DM)] for rapid determination of N status in potted geraniums (Pelargonium ×hortorum). Two F1 cultivars were chosen to represent a dark-green leaf cultivar, Horizon Deep Red, and a light-green leaf cultivar, Horizon Tangerine, and were grown in a soilless culture system. All standard 6-inch (15.24-cm) pots filled with a medium received an initial top-dress application of 5 g controlled-release fertilizer (15N–9P–12K), then plants were supplemented with additional N in the form of urea at 0, 50, 100, or 200 mg·L−1 N every few days to produce plants ranging from N-deficient to N-sufficient. The NDVI readings of individual plants from a NDVI pocket sensor developed by Oklahoma State University were collected weekly until the flowering stage. Data on flower traits, including number of pedicels (NOP), number of full umbels per pot (NOFU), total number of flowers per pot (TNF), number of flowers per pedicel (NOF), and inflorescences diameter (IFD), were collected 3 months after initial fertilizer treatment. After measuring flower traits, pedicels were removed from each pot, and SPAD value, NDVI, and leaf N concentration (g·kg−1 DM) were measured simultaneously. Cultivar and N rate significantly affected all but two flower and one N status parameters studied. The coefficient of determination R2 showed that NOP, NOFU, and TNF traits were more related to the N rates and the status parameters studied for ‘Horizon Tangerine’ than for ‘Horizon Deep Red’. For the latter cultivar, NOP and TNF traits were highly related to NDVI and SPAD values than N rates and leaf N content parameters. Correlation analysis indicated that the NDVI readings (R2 = 0.848 and 0.917) and SPAD values (R2 = 0.861 and 0.950) were significantly related to leaf N content (g·kg−1 DM) between cultivars. However, sensitivity of the NDVI and chlorophyll values to N application rate in geranium was slightly less than leaf N content. Strong correlations (R2 = 0.974 and 0.979, respectively) between NDVI and SPAD values were found within cultivars. The results demonstrated NDVI and SPAD values can be used to estimate N status in geranium. Because the pocket NDVI sensor will be cheaper than the SPAD unit, it has an advantage in determining N content in potted ornamentals.

Real-Time NDVI Measurement Using a Low-Cost Panchromatic Sensor for a Mobile Robot Platform

The values of Normalized Difference Vegetation Index (NDVI) are directly related to the capacity of plant photosynthesis and important to crop growth. In this research, an attempt was performed to develop a real-time NDVI measurement device that eliminated the noise such as shadows and background soil. The developed NDVI sensor has the advantage of portability and the capability of real-time measurement of plant growth. The sensor has flexibility in operation, either manually or remotely. The system can be used with several interfaces to enable the communication using on-board Wi-Fi for image acquisition and NDVI measurement. Thus, the vision of this system is to develop an NDVI measurement sensor for using in field monitoring. The NDVI sensor could be manufactured commercially in a low-cost and would be convenient compare to other expensive similar commercial products.

NITROGEN FERTILIZATION OPTIMIZATION ALGORITHM BASED ON IN-SEASON ESTIMATES OF YIELD AND PLANT NITROGEN UPTAKE

Current methods of determining nitrogen (N) fertilization rates in winter wheat (Triticum aestivum L.) are based on farmer projected yield goals and fixed N removal rates per unit of grain produced. This work reports on an alternative method of determining fertilizer N rates using estimates of early-season plant N uptake and potential yield determined from in-season spectral measurements collected between January and April. Reflectance measurements under daytime lighting in the red and near infrared regions of the spectra were used to compute the normalized difference vegetation index (NDVI). Using a modified daytime lighting reflectance sensor, early-season plant N uptake between Feekes physiological growth stages 4 (leaf sheaths lengthen) through 6 (first node of stem visible) was found to be highly correlated with NDVI. Further analyses showed that dividing the NDVI sensor measurements between Feekes growth stages 4 and 6, by the days from planting to sensing date was highly correlated with final grain yield. This in-season estimate of yield (INSEY) was subsequently used to compute the potential N that could be removed in the grain. In-season N fertilization needs were then considered to be equal to the amount of predicted grain N uptake (potential yield times grain N) minus predicted early-season plant N uptake (at the time of sensing), divided by an efficiency factor of 0.70. This method of determining in-season fertilizer need has been shown to decrease large area N rates while also increasing wheat grain yields when each 1m2 area was sensed and treated independently.