Retrieval of biophysical vegetation parameters using simultaneous inversion of high resolution remote sensing imagery constrained by a vegetation index

This study proposes a new method for inverting radiative transfer models to retrieve canopy biophysical parameters using remote sensing imagery. The inversion procedure is improved with respect to standard inversion, and achieves simultaneous inversion of leaf area index (LAI), soil reflectance (ρ_soil), chlorophyll content (C_a+b) and average leaf angle (ALA). In this approach, LAI is used to constrain modelling conditions during the inversion process, providing information about the phenological state of each plot under study. Due to the small area of the vegetation plots used for the inversion procedure and in order to avoid redundant information and improve computation efficiency, existing plot segmentation was used. All retrieved biophysical parameters, except LAI, were assumed to be invariant within each plot. The proposed methodology, based on the combination of PROSPECT and SAILH models, was tested over 16 cereal fields and 51 plots, on two dates, which were chosen to ensure crop assessment at different phenological stages. Plots were selected to provide a wide range of LAI between 0 and 6. Field measurements of LAI, ALA and C_a+b were conducted and used as ground truth for validation of the proposed model-inversion methodology. The approach was applied to very high spatial resolution remote sensing data from the QuickBird 2 satellite. The inversion procedure was successfully applied to the imagery and retrieved LAI with R ² = 0.83 and RMSE = 0.63 when compared to LAI2000 ground measurements. Separate inversions for barley and wheat yielded R ² = 0.89 (RMSE = 0.64) and R ² = 0.56 (RMSE = 0.61), respectively.     

Monitoring nitrogen,phosphorus and sulphur in hybrid rice (<Emphasis Type="Italic">Oryza sativa</Emphasis> L.) using hyperspectral remote sensing

Till date, the remote sensing research on crop nutrient monitoring has focused mainly on biomass and nitrogen (N) estimation and only a few attempts had been made to characterize and monitor macronutrients other than N. Field experiments were undertaken to study the remote detection of macronutrient status of rice using hyperspectral remote sensing. The variability in soil available N, phosphorus (P) and sulphur (S) and their content in plants were created using artificial fertility gradient design. The leaf and canopy hyperspectral reflectance was captured from variable macronutrient status vegetation. Linear correlation analysis between the spectral reflectance and plant nutrient status revealed significantly (p < 0.05) higher correlation coefficient at 670, 700, 730, 1090, 1260, 1460 nm for the nutrient under study. Published and proposed vegetation indices (VIs) were tested for canopy N, P and S prediction. The results of the investigation revealed that, published VIs (NDVI hyper and NDVI broadbands) could retrieve canopy N with higher accuracy, but not P and S. The predictability of the visible and short wave infrared based VI NRI₁₅₁₀ ((R₁₅₁₀ ? R₆₆₀)/(R₁₅₁₀ + R₆₆₀)) was the highest (r = 0.81, p < 0.01) for predicting N. Based on the outcomes of linear correlation analysis new VIs were proposed for remote detection of P and S. Proposed VI P_670_1260 ((R₁₂₆₀ ? R₆₇₀)/(R₁₂₆₀ + R₆₇₀)) retrieved canopy P status with higher prediction accuracy (r = 0.67, p < 0.01), whereas significantly higher canopy S prediction (r = 0.58, p < 0.01) was obtained using VI S_670_1090 ((R₁₀₉₀ ? R₆₇₀)/(R₁₀₉₀ + R₆₇₀)). The proposed spectral algorithms could be used for real time and site-specific N, P and S management in rice. Nutrient specific wavelengths, identified in the present investigation, could be used for developing relatively low-cost sensors of hand-held instruments to monitor N, P and S status of rice plant.     

Active remote sensing and grain yield in irrigated maize

Advances in agricultural technology have led to the development of active remote sensing equipment that can potentially optimize N fertilizer inputs. The objective of this study was to evaluate a hand-held active remote sensing instrument to estimate yield potential in irrigated maize. This study was done over two consecutive years on two irrigated maize fields in eastern Colorado. At the six- to eight-leaf crop growth stage, the GreenSeeker? active remote sensing unit was used to measure red and NIR reflectance of the crop canopy. Soil samples were taken before side-dressing from the plots at the time of sensing to determine nitrate concentration. Normalized difference vegetation index (NDVI) was calculated from the reflectance data and then divided by the number of days from planting to sensing, where growing degrees were greater than zero. An NDVI-ratio was calculated as the ratio of the reflectance of an area of interest to that of an N-rich portion of the field. Regression analysis was used to model grain yield. Grain yields ranged from 5 to 24 Mg ha^?1. The coefficient of determination ranged from 0.10 to 0.76. The data for both fields in year 1 were modeled and cross-validated using data from both fields for year 2. The coefficient of determination of the best fitting model for year 1 was 0.54. The NDVI-ratio had a significant relationship with observed grain yield (r ² = 0.65). This study shows that the GreenSeeker? active sensor has the potential to estimate grain yield in irrigated maize; however, improvements need to be made.     

Spectral and spatial differences in response of vegetation indices to nitrogen treatments on apple

Handheld chlorophyll sensors and remote sensing are two nondestructive approaches for estimating plant nitrogen (N) status, which are now commercially available. In this paper we address three questions on the application of these technologies in perennial fruit trees: (1) can individual leaf meter measurements be used to predict N status for surrounding trees?, (2) are narrow band indices more sensitive than the normalized difference vegetation index (NDVI) to differences in plant N?, and (3) is NDVI from satellite remote sensing correlated to leaf level vegetation indices? We evaluated data from a N rate trial conducted in a commercial Fuji apple orchard (Malus domestica Borkh. cv. ‘Fuji’). SPAD and CM1000 handheld chlorophyll meters and reflectance measurements using a portable spectrometer were made on individual leaves three or four times during each growing season. The reflectance measurements were used to determine NDVI and three narrow band vegetation indices. Satellite imagery from the Quickbird sensor was acquired two or three times during each growing season and used to generate NDVI for individual trees. The leaf meter measurements and vegetation indices were compared with the N application rate and plant N status measured as total leaf tissue N.We evaluated how well single leaf meter measurements predict N status for surrounding trees by calculating the differences between actual and estimated N applications from individual measurements. On average, a sample of 12 leaves (from the same treatment and same measurement date) resulted in an estimation error of 30 kg ha⁻¹ for either the SPAD or the CM1000 sensor, representing almost half of the range in N treatment rates. To evaluate any improvement in prediction of applied N using narrow band indices, we used analysis of variance (ANOVA) to compare three narrow band indices with the leaf meters and NDVI measured at leaf and canopy levels. Two narrow band indices, red edge vegetation stress index (RVSI) and modified chlorophyll absorption in reflectance index (MCARI) had higher F-values (31 and 41, respectively) than did NDVI from leaf level measurements (26), from satellite NDVI (6), or the CM1000 chlorophyll meter (12). The ANOVA results support improvements in leaf sensors using index values other than NDVI. We found that NDVI from satellite imagery acquired close to the leaf level measurement dates were positively correlated to the chlorophyll sensors and vegetation indices. When the data was averaged to the experiment plot level (twelve leaves total), the correlation coefficients between the satellite NDVI and the other sensors ranged from 0.68 for NDVI from leaf level reflectance to 0.84 with the CM1000 chlorophyll meter. Given the level of correlations, remote sensing might be a useful tool to extrapolate handheld measurements spatially throughout an orchard.     

Effect of observation scale on remote sensing based estimates of evapotranspiration in a semi-arid row cropped orchard environment

Understanding in detail the spatial distribution of evapotranspiration (ET) in row cropped fruit production areas with diverse water requirements is vital for monitoring water use and efficient irrigation scheduling. Spatially distributed ET for these environments can be estimated using remote sensing (RS). However, the computation of RS based ET under such conditions is complicated because of the complex parameterizations that are required to derive ET for the mixed pixels comprising of bare soil and well-watered plants typical of row cropped areas. Also, the parameterization of these processes is not scale invariant, owing to change in the percentage of vegetation cover in the mixed pixels across remote sensing observation scales. In this study, our main objectives were (1) to isolate and evaluate the effect of varying spatial scales (comparable to canopy sizes and larger) of the remote sensing data on ET estimates; and (2) provide an operational method for estimating remote sensing based ET for row cropped conditions. ET was computed using an empirical technique (S-SEBI: Simplified-Surface Energy Balance Index Algorithm) for almond and pistachio orchards from remote sensing imagery collected at a scale comparable to the canopy sizes of the trees (5.8 and 7.2 m) and a scale that was much larger than the canopy size (120 m) using the MASTER and Landsat sensors, respectively. In order to account for the effect of mixed pixels, a Normalized Difference Vegetation Index based correction factor was applied to the derived ET values and the results averaged for different fields were validated with Penman–Monteith based ET estimates. It was found that the corrected mean ET estimates at 120 m were in agreement with the Penman–Monteith based ET estimates (RMSE_average = 0.12 mm/h), whereas they were underestimated at the finer resolutions. Our results indicated that a remote sensing pixel resolution comparable to the row spacing and smaller and comparable to the canopy size overestimated the land surface temperature and consequently, underestimated ET when using operational models that do not account for vegetation and soil temperature separately. The results of the application of the NDVI correction factor indicates that good spatial estimates of crop ET can be made for crops growing in orchards using simple ET models that require minimal data and freely available Landsat imagery. These findings are very encouraging for the regular monitoring of crop health and effective management of irrigation water in highly water stressed agricultural environments.     

Crop water status assessment in controlled environment using crop reflectance and temperature measurements

Crop water status is an important parameter for plant growth and yield performance in greenhouses. Thus, early detection of water stress is essential for efficient crop management. The dynamic response of plants to changes of their environment is called ‘speaking plant’ and multisensory platforms for remote sensing measurements offer the possibility to monitor in real-time the crop health status without affecting the crop and environmental conditions. Therefore, aim of this work was to use crop reflectance and temperature measurements acquired remotely for crop water status assessment. Two different irrigation treatments were imposed in tomato plants grown in slabs filed with perlite, namely tomato plants under no irrigation for a certain period; and well-watered plants. The plants were grown in a controlled growth chamber and measurements were carried out during August and September of 2014. Crop reflectance measurements were carried out by two types of sensors: (i) a multispectral camera measuring the radiation reflected in three spectral bands centred between 590–680, 690–830 and 830–1000 nm regions, and (ii) a spectroradiometer measuring the leaf reflected radiation from 350 to 2500 nm. Based on the above measurements several crop indices were calculated. The results showed that crop reflectance increased due to water deficit with the detected reflectance increase being significant about 8 h following irrigation withholding. The results of a first derivative analysis on the reflectance data showed that the spectral regions centred at 490–510, 530–560, 660–670 and 730–760 nm could be used for crop status monitoring. In addition, the results of the present study point out that sphotochemical reflectance index, modified red simple ratio index and modified ratio normalized difference vegetation index could be used as an indicator of plant water stress, since their values were correlated well with the substrate water content and the crop water stress index; the last being extensively used for crop water status assessment in greenhouses and open field. Thus, it could be concluded that reflectance and crop temperature measurements might be combined to provide alarm signals when crop water status reaches critical levels for optimal plant growth.     

Low altitude remote sensing technologies for crop stress monitoring: a case study on spatial and temporal monitoring of irrigated pinto bean

Site-specific crop management is a promising approach to maximize crop yield with optimal use of rapidly depleting natural resources. Availability of high resolution crop data at critical growth stages is a key for real-time data-driven decisions during the production season. The goal of this study was to evaluate the possibility of using small unmanned aerial system (UAS)-based remote sensing technologies to monitor the crop stress of irrigated pinto beans (Phaseolus vulgaris L.) with varied irrigation and tillage treatments. A small UAS with onboard multispectral and infrared thermal imaging sensors was used to collect data from bean field plots on three growth stages in 2015 and 2016, respectively. Indicators including green normalized vegetation index (GNDVI), canopy cover (CC, ratio of ground covered by crop canopy to the total plot area) and canopy temperature (CT, °C) of crops were extracted from imaging data and correlated with ground-reference crop yield and leaf area index (LAI) estimated with a handheld ceptometer. Results show that GNDVI, CC and CT were able to differentiate crops with full and deficit irrigation treatments at each of the three growth stages in both years. Developed indicators were strongly correlated with to the crop yield with Pearson correlation coefficients (r) of approximate 0.71 and 0.72 for GNDVI and CC, respectively, in the early growth stage (54 days after planting) in both years. Canopy temperature showed even stronger correlation (r > 0.8) with yield at early growth stage. Performance of small UAS-based imagery-based indicators in crop stress monitoring and crop yield estimation was better than or comparable to that of the ground-based LAI estimates, indicating the potential of such remote sensing tool in rapid crop stress monitoring and management.     

Mapping radiation interception in row-structured orchards using 3D simulation and high-resolution airborne imagery acquired from a UAV

This study was conducted to model the fraction of intercepted photosynthetically active radiation (fIPAR) in heterogeneous row-structured orchards, and to develop methodologies for accurate mapping of the instantaneous fIPAR at field scale using remote sensing imagery. The generation of high-resolution maps delineating the spatial variation of the radiation interception is critical for precision agriculture purposes such as adjusting management actions and harvesting in homogeneous within-field areas. Scaling-up and model inversion methods were investigated to estimate fIPAR using the 3D radiative transfer model, Forest Light Interaction Model (FLIGHT). The model was tested against airborne and field measurements of canopy reflectance and fIPAR acquired on two commercial peach and citrus orchards, where study plots showing a gradient in the canopy structure were selected. High-resolution airborne multi-spectral imagery was acquired at 10?nm bandwidth and 150?mm spatial resolution using a miniaturized multi-spectral camera on board an unmanned aerial vehicle (UAV). In addition, simulations of the land surface bidirectional reflectance were conducted to understand the relationships between canopy architecture and fIPAR. Input parameters used for the canopy model, such as the leaf and soil optical properties, canopy architecture, and sun geometry were studied in order to assess the effect of these inputs on canopy reflectance, vegetation indices and fIPAR. The 3D canopy model approach used to simulate the discontinuous row-tree canopies yielded spectral RMSE values below 0.03 (visible region) and below 0.05 (near-infrared) when compared against airborne canopy reflectance imagery acquired over the sites under study. The FLIGHT model assessment conducted for fIPAR estimation against field measurements yielded RMSE values below 0.08. The simulations conducted suggested the usefulness of these modeling methods in heterogeneous row-structured orchards, and the high sensitivity of the normalized difference vegetation index and fIPAR to background, row orientation, percentage cover and sun geometry. Mapping fIPAR from high-resolution airborne imagery through scaling-up and model inversion methods conducted with the 3D model yielded RMSE error values below 0.09 for the scaling-up approach, and below 0.10 for the model inversion conducted with a look-up table. The generation of intercepted radiation maps in row-structured tree orchards is demonstrated to be feasible using a miniaturized multi-spectral camera on board UAV platforms for precision agriculture purposes.     

Evaluation of red and red-edge reflectance-based vegetation indices for rice biomass and grain yield prediction models in paddy fields

Remote sensing-based nitrogen (N) management has been evaluated in many crops. The water background and wide range of varieties in rice (Oryza sativa), are unique features that require additional consideration when using sensor technology. The commonly calculated normalized difference vegetation index is of limited use when the crop has reached complete canopy closure. The objective of this research was to evaluate mid-season agronomic parameter and grain yield prediction models along with the effect of water background and of different varieties using a red- and red-edge-based vegetation index. Varieties × N trials were established at the LSU AgCenter Rice Research Station located in Crowley, Louisiana in 2011 and 2012. Canopy spectral reflectance under clear and turbid water, biomass yield, N content, plant coverage, and water depth were collected each week for three consecutive weeks beginning 2 weeks before panicle differentiation. Grain yield was also determined. Water turbidity had an influence on spectral reflectance when canopy coverage was less than 50 %. While water depth influenced red reflectance, this was not carried over when reflectance was transformed to vegetation indices. The red-edge-based vegetation indices, especially those computed by ratio, had stronger relationships with measured agronomic parameters as compared with red-based indices. Furthermore, the effect of variety on the yield prediction model was observed using derivative-based red-edge indices but not with other ratio-based indices. Future researches should focus on developing a generalized yield prediction model using ratio-based red-edge indices across different varieties to extend its applicability in production fields.     

Is data needed from every field to determine in-season precision nitrogen recommendations in winter wheat?

The research reported here seeks to determine whether it is necessary to obtain optical reflectance measurements with a GreenSeeker^® handheld sensor from each field to make accurate in-season nitrogen application recommendations for winter wheat, and how much precision—and profit—would be lost by moving from site-specific (or field-specific) optical reflectance sampling to region-level sampling. The approach used was to estimate a separate linear response-plateau regression every year using yield and optical reflectance data from randomized complete block experiments. Profits from region-level sampling and field-level sampling were statistically indistinguishable, but this result was mostly due to both being imprecise. Furthermore, the region- and field-based sampling systems were no better than break-even with the historical extension advice to apply preplant anhydrous ammonia at 90 kg ha^?1. The approach of estimating a new regression every year is too imprecise, whether at the field or region level. This research goes beyond past research by accounting for the uncertainty in the estimated relationships. The poor performance of the systems is directly related to the imprecise relationship between yield and optical reflectance responses to nitrogen.     

Mapping crop water stress index in a ‘Pinot-noir’ vineyard: comparing ground measurements with thermal remote sensing imagery from an unmanned aerial vehicle

Characterizing the spatial variability in water status across vineyards is a prerequisite for precision irrigation. The crop water stress index (CWSI) indicator was used to map the spatial variability in water deficits across an 11-ha ‘Pinot noir’ vineyard. CWSI was determined based on canopy temperatures measured with infrared temperature sensors placed on top of well-watered and water-stressed grapevines in 2009 and 2010. CWSI was correlated with leaf water potential (Ψ_L) (R ² = 0.83). This correlation was also tested with results from high resolution airborne thermal imagery. An unmanned aerial vehicle equipped with a thermal camera was flown over the vineyard at 07:30, 09:30, and 12:30 h (solar time) on 31 July 2009. At about the same time, Ψ_L was measured in 184 grapevines. The image obtained at 07:30 was not useful because it was not possible to separate soil from canopy temperatures. Using the airborne data, the correlation between CWSI and Ψ_L had an R ² value of 0.46 at 09:30 h and of 0.71 at 12:30 h, suggesting that the latter was the more favorable time for obtaining thermal images that were linked with Ψ_L values. A sensitivity analysis of varying pixel size showed that a 0.3 m pixel was needed for precise CWSI mapping. The CWSI maps thus obtained by airborne thermal imagery were effective in assessing the spatial variability of water stress across the vineyard.     

叶片的灰尘对高光谱遥感中植被冠层反射率的影响

灰尘作为气溶胶的一部分由于沉降现象而附着于植物叶子的表面,当遥感探测器扫描植被冠层时,所得反射率数据就具有了一定的相似性。在平时我们处理卫星数据时忽略了附着于叶子表面灰尘的影响,没有把植被表面的灰尘作为影响因子考虑进去。随着探测器的发展,光谱分辨率的提高,灰尘对于某些波段的影响越来越大。由于天气变化如降雨可以使植被叶片表面灰尘减少甚至消失,引起植被反射率发生变化,于是本文通过模拟降雨前后植被叶片反射率变化,来研究灰尘的影响程度大小。具体方法为采用ASD野外光谱仪分别测量小麦、冬青等植被采摘后表层附有灰尘的叶片和模拟雨水冲洗灰尘后的叶片。通过同一片叶子两次测得的不同数据处理、比较得出以下结论:1.附着于植被冠层的灰尘对其反射率有很大影响。2.没有去尘处理的叶片反射曲线不是灰尘反射曲线和经除尘处理的叶片反射曲线简单的线性叠加。3.灰尘并不会造成“红边”移动,不会发生“蓝移”和“红移”。     

Evaluating the performance of xanthophyll,chlorophyll and structure-sensitive spectral indices to detect water stress in five fruit tree species

This study assessed the capability of several xanthophyll, chlorophyll and structure-sensitive spectral indices to detect water stress in a commercial farm consisting of five fruit tree crop species with contrasting phenology and canopy architecture. Plots irrigated and non-irrigated for eight days of each species were used to promote a range of plant water status. Multi-spectral and thermal images were acquired from an unmanned aerial system while concomitant measurements of stomatal conductance (g_s), stem water potential (Ψ_s) and photosynthesis were taken. The Normalized Difference Vegetation Index (NDVI), red-edge ratio (R₇₀₀/R₆₇₀), Transformed Chlorophyll Absorption in Reflectance Index normalized by the Optimized Soil Adjusted Vegetation Index (TCARI/OSAVI), the Photochemical Reflectance Index using reflectance at 530 (PRI) and 515 nm [PRI_(570–515)] and the normalized PRI (PRI_norm) were obtained from the narrow-band multi-spectral images and the relationship with the in-field measurements explored. Results showed that within the Prunus species, Ψ_s yielded the best correlations with PRI and PRI_(570–515) (r² = 0.53) in almond trees, with TCARI/OSAVI (r² = 0.88) in apricot trees and with PRI_norm, R₇₀₀/R₆₇₀ and NDVI (r² from 0.72 to 0.88) in peach trees. Weak or no correlations were found for the Citrus species due to the low level of water stress reached by the trees. Results from the sensitivity analysis pointed out the canopy temperature (T_c) and PRI_(570–515) as the first and second most sensitive indicators to the imposed water conditions in all the crops with the exception of apricot trees, in which Ψ_s was the most sensitive indicator at midday. PRI_norm was the least sensitive index among all the water stress indicators studied. When all the crops were analyzed together, PRI_(570–515) and NDVI were the indices that better correlations yielded with Crop Water Stress Index, g_s and, particularly, Ψ_s (r² = 0.61 and 0.65, respectively). This work demonstrated the feasibility of using narrow-band multispectral-derived indices to retrieve water status for a variety of crop species with contrasting phenology and canopy architecture.     

Leaf nitrogen estimation in potato based on spectral data and on simulated bands of the VENμS satellite

Relationships between leaf spectral reflectance at 400–900 nm and nitrogen levels in potato petioles and leaves were studied. Five nitrogen (N) fertilizer treatments were applied to build up levels of nitrogen variation in potato fields in Israel in spring 2006 and 2007. Reflectance of leaves was measured in the field over a spectral range of 400–900 nm. The leaves were sampled and analyzed for petiole NO₃–N and leaf percentage N (leaf-%N). Prediction models of leaf nitrogen content were developed based on an optical index named transformed chlorophyll absorption reflectance index (TCARI) and on partial least squares regression (PLSR). Prediction models were also developed based on simulated bands of the future VENμS satellite (Vegetation and Environment monitoring on a New Micro-Satellite). Leaf spectral reflectance correlated better with leaf-%N than with petiole NO₃–N. The TCARI provided strong correlations with leaf-%N, but only at the tuber-bulking stage. The PLSR analysis resulted in a stronger correlation than TCARI with leaf-%N. An R ² of 0.95 (p < 0.01) and overall accuracy of 80.5% (Kappa = 74%) were determined for both vegetative and tuber-bulking periods. The simulated VENμS bands gave a similar correlation with leaf-%N to that of the spectrometer spectra. The satellite has significant potential for spatial analysis of nitrogen levels with inexpensive images that cover large areas every 2 days.     

Foliage temperature extraction from thermal imagery for crop water stress determination

Crop water stress determination methods from canopy temperatures, derived from the surface energy balance equations, treat the canopy temperature under the assumption that the canopy behaves as a virtual “big-leaf”, covering the ground surface. Introduction of very high-resolution thermal imagery, 0.01–0.3-m pixel size, acquired from low altitude platforms, enabled finely detailed observation of the whole canopy, raising the question how to select the relevant canopy temperatures. One approach is to select the sunlit leaves confirming to the “big leaf” energy balance paradigm. However, thermal imagery alone is incomplete and needs additional marking or synchronized visible imagery for interpretation, which makes the process complicated and expensive. The other approach, used in reference surface based water stress evaluation, is to use full frame pixel statistics without pattern recognition by selecting the mean temperature of the cold fraction from the pixel histogram. That greatly simplifies processing for large-scale aerial thermography. Here are presented the results of experiments conducted in cotton and vine grapes, where both approaches were evaluated simultaneously. Ground referenced thermal and visible images were overlapped, and sunlit, shaded and whole canopy leaves were selected for crop temperature evaluation. The pixel histograms of the same images were analyzed in a two-step method, after discarding soil pixels where their temperature was 7 °C higher than air temperature at step one, and calculation of the mean temperatures of the lowest 33 and 100 % of the remaining pixels for step two. Several crop water stress indices were compared with leaf and stem water potentials and stomatal conductance. Good agreement was found between both image segmentation and histogram analysis methods, demonstrating the suitability of both methods in canopy temperature evaluation for crop water stress evaluation.     

Evaluation of different approaches for estimating and mapping crop water status in cotton with thermal imaging

Canopy temperature has long been recognized as an indicator of plant water status, therefore, a high-resolution thermal imaging system was used to map crop water status. Potential approaches for estimating crop water status from digital infrared images of the canopy were evaluated. The effect of time of day on leaf temperature measurements was studied: midday was found to be the optimal time for thermal image acquisition. Comparison between theoretical and empirical approaches for estimating leaf water potential showed that empirical temperature baselines were better than those obtained from energy balance equations. Finally, the effects of angle of view and spatial resolution of the thermal images were evaluated: water status was mapped by using angular thermal images. In spite of the different viewing angles and spatial resolution, the map provided a good representation of the measured leaf water potential.     

Crop water stress index derived from multi-year ground and aerial thermal images as an indicator of potato water status

Potato yield and quality are highly dependent on an adequate supply of water. In this study, 3 years of information from thermal and RGB images were collected to evaluate water status in potato fields. Irrigation experiments were conducted in commercial potato fields (Desiree; drippers). Two water-deficit scenarios were tested: a short-term water deficit (by suppressing irrigation for a number of days before image acquisition), and a long-term cumulative water deficit. Ground and aerial images were acquired in various phenological stages along the potato growing season. Effects of irrigation treatments were recorded by thermal indices and biophysical measurements of stomatal conductance (SC), leaf water potential, leaf osmotic potential and gravimetric water potential in soil. Canopy temperature was delineated from the thermal images with and without fused information from the RGB image. Crop water stress index (CWSI) was calculated, using three forms of minimum baseline temperature: empirical, theoretical and statistical. An empirical evaluation of maximum baseline temperature of Tair + 7 °C was used in all CWSI forms examined. Statistical tests and comparison of CWSI with biophysical measurements were performed to evaluate the responses to irrigation treatments. The results indicated a high correlation of CWSI with SC from tuber initiation to maturity based on ground and aerial data (0.64 ≤ R² ≤ 0.99). Similar trends of increasing CWSI from well to deficit-irrigated treatments were found in all three growing seasons. The results also showed that CWSI may be calculated based merely on thermal imagery data.     

长白山阔叶红松林不同演替阶段冠层光谱特征及其与气温的关系

【目的】通过遥感数据分析长白山阔叶红松林不同演替阶段冠层光谱变化特征,为揭示长白山群落内部种间变化以及植被生产力对气候因子的响应机制提供理论依据。【方法】通过Google Earth Engine平台提取1984-2019年长白山原始阔叶红松林与次生白桦林Landsat和Sentinel多年冠层光谱数据并计算植被绿度参数,分析二者冠层光谱特征季节变化、植被绿度的季节与年际变化,计算植被年际绿度变化与同期月均温的Pearson相关系数。【结果】(1)原始林与次生林冠层可见光反射率在非生长季较高,生长季下降,而近红外光变化趋势则与此相反。在生长旺盛季节(5-10月底)原始林与次生林可见光波段冠层反射率相近,近红外波段差异明显,次生林冠层反射率更高。二者都具有明显的"红谷"、"绿峰"、"蓝谷"和"红边"现象,原始林冠层光谱反射率年变化幅度小于次生林。(2)原始林与次生林的绿度表现为相同的变化趋势,即春季展叶期间增长、秋季落叶期衰减。非生长季,原始林植被指数变化较为稳定且大于次生林,次生林林下透光度高。生长季,次生林增强植被指数(EVI)和哨兵二号红边位置(S2REP)均大于原始林,植被冠层生...