Correlation between vegetation indexes generated at Vitis Vinifera L. and soil, plant and production parameters for emergency application in decision making

ABSTRACT: Correlation between proximal sensing techniques and laboratory results of qualitative variables plus agronomic attributes was evaluated of a 3,0 ha vineyard in the county of Muitos Capões, Northeast of Rio Grande do Sul State, Brazil, in Vitis vinifera L. at 2017/2018 harvest, aiming to evaluate the replacement of conventional laboratory analysis in viticulture by Vegetation Indexes, at situations were laboratory access are unavailable. Based on bibliographic research, looking for vegetative indexes developed or used for canopy reflectance analysis on grapevines and whose working bands were within the spectral range provided by the equipment used, a total of 17 viable candidates were obtained. These chosen vegetation indices were correlated, through Pearson (5%), with agronomic soil attributes (apparent electrical conductivity, clay, pH in H2O, phosphorus, potassium, organic matter, aluminum, calcium, magnesium, effective CTC, CTC at pH 7.0, zinc, copper, sulfur and boron) for depths 0 -20 cm and 20-40 cm, and plant tissue (Nitrogen, phosphorus, potassium, calcium, magnesium, sulfur, copper, zinc, iron, manganese and boron) , in addition to some key oenological and phytotechnical parameters for the quantification of wine production and quality. One hundred and thirty ninesignificant correlations were obtained from this cross, with 36 moderate coefficients between 19 parameter variables versus 12 of the indexes. We concluded that in cases where access or availability of laboratory analyzes is difficult or impracticable, the use of vegetation indices is possible if the correlation coefficients reach, at least, the moderate magnitude, serving as a support to decision making until the lack analytical structure to be remedied.

Impact of Structural, Photochemical and Instrumental Effects on Leaf and Canopy Reflectance Variability in the 500–600 nm Range

Current rapid technological improvement in optical radiometric instrumentation provides an opportunity to develop innovative measurements protocols where the remote quantification of the plant physiological status can be determined with higher accuracy. In this study, the leaf and canopy reflectance variability in the PRI spectral region (i.e., 500–600 nm) is quantified using different laboratory protocols that consider both instrumental and experimental set-up aspects, as well as canopy structural effects and vegetation photoprotection dynamics. First, we studied how an incorrect characterization of the at-target incoming radiance translated into an erroneous vegetation reflectance spectrum and consequently in an incorrect quantification of reflectance indices such as PRI. The erroneous characterization of the at-target incoming radiance translated into a 2% overestimation and a 31% underestimation of estimated chlorophyll content and PRI-related vegetation indexes, respectively. Second, we investigated the dynamic xanthophyll pool and intrinsic Chl vs. Car long-term pool changes affecting the entire 500–600 nm spectral region. Consistent spectral behaviors were observed for leaf and canopy experiments. Sun-adapted plants showed a larger optical change in the PRI range and a higher capacity for photoprotection during the light transient time when compared to shade-adapted plants. Outcomes of this work highlight the importance of well-established spectroscopy sampling protocols to detect the subtle photochemical features which need to be disentangled from the structural and biological effects.

Estimation of Aboveground Biomass For Winter Wheat In The Later Growth Stage By Combining Digital Texture And Spectral Analysis

Background: Aboveground biomass (AGB) is an important indicator to predict crop yield. Traditional spectral features or image textures have been proposed to estimate the AGB of crops, but they perform poorly in estimation of AGB at high biomass levels. The present study thus evaluated the ability of spectral features, image textures, combinations thereof to estimate winter wheat AGB. Result: The spectral features were obtained from the wheat canopy reflectance spectra of 400–1000 nm including original wavelengths and seven vegetation indices (VIs), then we screened effective wavelengths (EWs) through successive projection algorithm (SPA) and the optimal vegetation index selected by correlation analysis. The image textures features were extracted by gray level co-occurrence matrix including texture features (TEX) and normalized difference texture index (NDTI), then we selected effective variables including the optimal texture subset (OTEXS) and the optimal normalized difference texture index subset (ONDTIS) through the ranking of feature importance of random forest (RF). Linear regression (LR), partial least squares regression (PLS) and random forest (RF) were established to evaluate the relationship between each calculated feature and AGB. The results demonstrate that the ONDTIS with PLS based on validation datasets exhibited better performance in estimating AGB for the post-seedling stage (R2 = 0.75, RMSE = 0.04). Moreover, the combinations of OTEXS and EWs with LR based on validation datasets exhibited the highest prediction accuracy for the post-seedling stage (R2 = 0.78, RMSE = 0.05). Conclusion: The findings show that the combined use of spectral features and image textures can effectively improve the accuracy for AGB estimation especially in post-seeding stage.

Field Studies on the Effect of Rye Cover Crop on Soybean Root Disease and Productivity

Cover crops improve soil and water quality in annual cropping systems, but knowledge of their impact on soybean (Glycine max L.) seedling and root diseases is limited. The effects of winter rye cover crops (Secale cereale L.) on soybean population, biomass, root morphology, seedling and root diseases, pathogen incidence, canopy reflectance, and yield were assessed over two years in Iowa and Missouri, USA. Plots without a rye cover crop were compared to plots with early-kill rye and late-kill rye cover crops, which were terminated 34 to 49 days or 5 to 17 days before soybean planting, respectively. Soybean shoot dry weight, root rot severity, and incidence of Fusarium spp. and Pythium spp. on roots were not influenced by the treatments. Soybean grain yield and plant population were reduced in the presence of rye in two site-years, increased in one site-year, and not changed in the remaining site-years. Soybean canopy reflectance was measured at 810 nm and measurements were first made at 70 to 80 days after planting (DAP). At least five measurements were obtained at 7- to 15- day intervals, ending at 120 to 125 DAP. Measurements at approximately 120 to 125 DAP differed by treatments but were not consistently associated with the presence or absence of a rye cover crop. Our field studies suggest that Iowa and Missouri soybean farmers can use winter rye as a cover crop in soybean fields with low seedling disease pressure without increasing the risk of seedling and root diseases or suppressing yield.

Assessing Non-Photosynthetic Cropland Biomass from Spaceborne Hyperspectral Imagery

Non-photosynthetic vegetation (NPV) biomass has been identified as a priority variable for upcoming spaceborne imaging spectroscopy missions, calling for a quantitative estimation of lignocellulosic plant material as opposed to the sole indication of surface coverage. Therefore, we propose a hybrid model for the retrieval of non-photosynthetic cropland biomass. The workflow included coupling the leaf optical model PROSPECT-PRO with the canopy reflectance model 4SAIL, which allowed us to simulate NPV biomass from carbon-based constituents (CBC) and leaf area index (LAI). PROSAIL-PRO provided a training database for a Gaussian process regression (GPR) algorithm, simulating a wide range of non-photosynthetic vegetation states. Active learning was employed to reduce and optimize the training data set. In addition, we applied spectral dimensionality reduction to condense essential information of non-photosynthetic signals. The resulting NPV-GPR model was successfully validated against soybean field data with normalized root mean square error (nRMSE) of 13.4% and a coefficient of determination (R2) of 0.85. To demonstrate mapping capability, the NPV-GPR model was tested on a PRISMA hyperspectral image acquired over agricultural areas in the North of Munich, Germany. Reliable estimates were mainly achieved over senescent vegetation areas as suggested by model uncertainties. The proposed workflow is the first step towards the quantification of non-photosynthetic cropland biomass as a next-generation product from near-term operational missions, such as CHIME.

Influence of light intensity distribution characteristics of light source on measurement results of canopy reflectance spectrometers

Background The characteristics of light source have an important influence on the measurement performance of canopy reflectance spectrometer. The size of the effective irradiation area and the uniformity of the light intensity distribution in the irradiation area determine the ability of the spectrometer to express the group characteristics of the measured objects. Methods In this paper, an evaluation method was proposed to theoretically analyze the influence of the light intensity distribution characteristics of the light source irradiation area on the measurement results. The light intensity distribution feature vector and the reflectance feature vector of the measured object were constructed to design reflectance difference coefficient, which could effectively evaluate the measurement performance of the canopy reflectance spectrometer. By using self-design light intensity distribution test system and GreenSeeker RT100, the evaluation method was applied to evaluate the measurement results. Results The evaluation results showed that the vegetation indices based on the arithmetic average reflectance of the measured object could be obtained theoretically only when the light intensity distribution of the light source detected by the spectrometer was uniform, which could fully express the group characteristics of the object. When the light intensity distribution of the active light source was not uniform, the measure value was difficult to fully express the group characteristics of the object. And the measured object reflectance was merely the weighted average value based on the light intensity distribution characteristics. Conclusions According to the research results of this paper, sunlight is the most ideal detection light source. If the passive light source spectrometer can improve the measurement method to adapt to the change of sunlight intensity, its measurement performance will be better than any active-light spectrometer.

​​Performance of Cereals under Need based Nitrogen Management Strategies: A Review

Cereals have large nitrogen requirement, but the demand for fertilizer is variable. Divergence between the supply and requirement of nitrogen can potentially hamper the crop growth as well as the environment, resulting in poor nitrogen use efficiency leads to economic losses. A balance between supply and utilization is required to optimize crop growth, economic returns and to maintain environmental sustainability which can be solved through need based nitrogen management which is nothing but application of inputs is according to the needs of the farm. Spatial variability is present in the fields but often they receive a same dose of fertilizers because they are treated by farmers as a homogenous unit. Through need based strategies, farmers will supply nitrogen fertilizers according to the demand of the crop which reduce the losses of N fertilizer. A precision agriculture approach to address the disparate spatial N requirements across a field is the use of a variable rate application guided by crop canopy reflectance sensors. Sensors like SPAD chlorophyll meter, greenseeker, rapid SCAN etc are used for determining the nitrogen need of the field crops. Many researchers across the globe are working on standardization of these sensors for different growth stages of the crop. Precision input management in cereals is lacking at present in most of the growing areas. A good amount of information on crop nutrition is available, but information regarding need based N management is lacking. This article reviews the work done on need based nitrogen management strategies in cereals.

Improving Soil Moisture Assessment of Turfgrass Systems Utilizing Field Radiometry

The need for water conservation continues to increase as global freshwater resources dwindle. Turfgrass mangers are adapting to these concerns by implementing new tools to reduce water consumption. Time-domain reflectometer (TDR) soil moisture sensors can decrease water usage when scheduling irrigation, but nonuniformity across unsampled locations creates irrigation inefficiencies. Remote sensing data have been used to estimate soil moisture stress in turfgrass systems through the normalized difference vegetation index (NDVI). However, numerous stressors other than moisture constraints impact NDVI values. The water band index (WBI) is an alternative index that uses narrowband, near-infrared light reflectance to estimate moisture limitations within the plant canopy. The green-to-red ratio index (GRI) is a vegetation index that has been proposed as a cheaper alternative to WBI as it can be measured using digital values of visible light instead of relying on more costly hyperspectral reflectance measurements. A replicated 2 × 3 factorial experimental design was used to repeatedly measure turf canopy reflectance and soil moisture over time as soils dried. Pots of ‘007’ creeping bentgrass (CBG) and ‘Latitude 36’ hybrid bermudagrass (HBG) were grown on three soil textures: United States Golf Association (USGA) 90:10 sand, loam, and clay. Reflectance data were collected hourly between 07:00 and 19:00 using a hyperspectral radiometer and volumetric water content (VWC) data were collected continuously using an embedded soil moisture sensor from soil saturation until complete turf necrosis by drought stress. The WBI had the strongest relationship to VWC (r = 0.62) compared to GRI (r = 0.56) and NDVI (r = 0.47). The WBI and GRI identified significant moisture stress approximately 28 h earlier than NDVI (p = 0.0010). Those metrics also predicted moisture stress prior to fifty percent visual estimation of wilt (p = 0.0317), with lead times of 12 h (WBI) and 9 h (GRI). By contrast, NDVI provided 2 h of prediction time. Nonlinear regression analysis showed that WBI and GRI can be useful for predicting moisture stress of CBG and HBG grown on three different soil textures in a controlled environment.