scholarly journals Data-Driven Methods for the Estimation of Leaf Water and Dry Matter Content: Performances, Potential and Limitations

Sensors ◽  
2020 ◽  
Vol 20 (18) ◽  
pp. 5394
Author(s):  
Bin Yang ◽  
Hui Lin ◽  
Yuhao He
Keyword(s):  
Remote Sensing ◽  
Dry Matter ◽  
Vegetation Indices ◽  
Matter Content ◽  
Data Driven ◽  
Support Vector ◽  
Dry Matter Content ◽  
Biochemical Constituents ◽  
Equivalent Water Thickness ◽  
Leaf Level

Leaf equivalent water thickness (EWT) and dry matter content (expressed as leaf mass per area (LMA)) are two critical traits for vegetation function monitoring, crop yield estimation, and precise agriculture management. Data-driven methods are widely used for remote sensing of leaf EWT and LMA because of their simplicity, satisfactory accuracy, and computation efficiency, such as the vegetation indices (VI)-based and machine learning (ML)-based methods. However, most of the data-driven methods are utilized at the canopy level, comparison of the performances of the data-driven methods at the leaf level has not been well documented. Moreover, the ML-based data-driven methods generally adopt leaf optical properties directly as their inputs, which may subsequently decrease their ability in remote sensing of leaf biochemical constituents. Performances of the ML-based methods cooperating with VI are rarely evaluated. Using the independent LOPEX and ANGERS datasets, we compared the performances of three data-driven methods: VI-based, ML-reflectance-based, and ML-VI-based methods, for the estimation of leaf EWT and LMA. Three sampling strategies were also utilized for evaluation of the generalization of these data-driven methods. Our results evidenced that ML-VI-based methods were the most accurate among these data-driven methods. Compared to the ML-reflectance-based and VI-based methods, the ML-VI-based model with support vector regression overall reduced errors by 5.7% (41.5%) and 1.8% (12.4%) for the estimation of leaf EWT (LMA), respectively. The ML-VI-based model inherits advantages of vegetation indices and ML techniques, which made it sensitive to changes of leaf biochemical constituents and capable of solving nonlinear tasks. It is thus recommended for the estimation of EWT and LMA at the leaf level. Moreover, its performance can further be enhanced by improving its generalization ability, such as adopting techniques on the selection of better wavelengths and definition of new vegetation indices. These results thus provided a prior knowledge of the data-driven methods and can be helpful for future studies on the remote sensing of leaf biochemical constituents.

scholarly journals Validating the Predictive Power of Statistical Models in Retrieving Leaf Dry Matter Content of a Coastal Wetland from a Sentinel-2 Image

2019 ◽  
Vol 11 (16) ◽  
pp. 1936 ◽  
Author(s):  
Abebe Mohammed Ali ◽  
Roshanak Darvishzadeh ◽  
Kasra Rafiezadeh Shahi ◽  
Andrew Skidmore
Keyword(s):  
Remote Sensing ◽  
Dry Matter ◽  
Vegetation Index ◽  
Coastal Wetland ◽  
Vegetation Indices ◽  
Matter Content ◽  
Process Models ◽  
Dry Matter Content ◽  
Leaf Dry Matter Content ◽  
Sentinel 2

Leaf dry matter content (LDMC), the ratio of leaf dry mass to its fresh mass, is a key plant trait, which is an indicator for many critical aspects of plant growth and survival. Accurate and fast detection of the spatiotemporal dynamics of LDMC would help understanding plants’ carbon assimilation and relative growth rate, and may then be used as an input for vegetation process models to monitor ecosystems. Satellite remote sensing is an effective tool for predicting such plant traits non-destructively. However, studies on the applicability of remote sensing for LDMC retrieval are scarce. Only a few studies have looked into the practicality of using remotely sensed data for the prediction of LDMC in a forest ecosystem. In this study, we assessed the performance of partial least squares regression (PLSR) plus 11 widely used vegetation indices (VIs), calculated based on different combinations of Sentinel-2 bands, in predicting LDMC in a coastal wetland. The accuracy of the selected methods was validated using LDMC, destructively measured in 50 randomly distributed sample plots at the study site in Schiermonnikoog, the Netherlands. The PLSR applied to canopy reflectance of Sentinel-2 bands resulted in accurate prediction of LDMC (coefficient of determination (R2) = 0.71, RMSE = 0.033). PLSR applied to the studied VIs provided an R2 of 0.70 and RMSE of 0.033. Four vegetation indices (enhanced vegetation index(EVI), specific leaf area vegetation index (SLAVI), simple ratio vegetation index (SRVI), and visible atmospherically resistant index (VARI)) computed using band 3 (green) and band 11 of the Sentinel-2 performed equally well and achieved a good measure of accuracy (R2 = 0.67, RMSE = 0.034). Our findings demonstrate the feasibility of using Sentinel-2 surface reflectance data to map LDMC in a coastal wetland.

scholarly journals Performance of some potato accessions of USA and Bangladesh in relation to dry matter yield and biochemical constituent

2016 ◽  
Vol 13 (2) ◽  
pp. 215-220 ◽  
Author(s):  
MA Mahamud ◽  
MAH Chowdhury ◽  
MA Rahim ◽  
PR Sheel
Keyword(s):  
Vitamin C ◽  
Biochemical Parameters ◽  
Dry Matter ◽  
Starch Content ◽  
Significant Negative Correlation ◽  
Matter Content ◽  
Dry Matter Content ◽  
Dry Matter Yield ◽  
Biochemical Constituents ◽  
Biochemical Constituent

Present research was designed to evaluate the dry matter yield and biochemical constituents of twenty two potato accession of USA in comparison with two Bangladeshi varieties. Studied biochemical constituents were starch, protein and vitamin C contents. Analyses were performed from November 2014 to April 2015 in different laboratories of Bangladesh Agricultural University, Mymensingh. Potato accessions differed significantly with respect to dry matter content and biochemical parameters. Dry mater content was found highest in accession AC 10110. Considering biochemical constituent, the accession AC 10062 showed highest protein and vitamin C contents. On the other hand, accession AC 10081 had the highest starch content. Significant positive correlation was observed between dry matter and starch content while significant negative correlation was observed between starch and protein contents. Considering both dry matter yield and biochemical parameters AC 10081 was found as the best USA potato accession for cultivation in Bangladesh.J. Bangladesh Agril. Univ. 13(2): 215-220, December 2015

Optimum harvest date of maize for biogas and silage purposes

2009 ◽  
Vol 57 (2) ◽  
pp. 119-125
Author(s):  
G. Hadi
Keyword(s):  
Dry Matter ◽  
Biogas Production ◽  
Weather Conditions ◽  
Vegetation Period ◽  
Dry Mass ◽  
Matter Content ◽  
Harvest Date ◽  
Dry Matter Content ◽  
Dry Matter Yield ◽  
Vegetative Organs

The dry matter and moisture contents of the aboveground vegetative organs and kernels of four maize hybrids were studied in Martonvásár at five harvest dates, with four replications per hybrid. The dry matter yield per hectare of the kernels and other plant organs were investigated in order to obtain data on the optimum date of harvest for the purposes of biogas and silage production.It was found that the dry mass of the aboveground vegetative organs, both individually and in total, did not increase after silking. During the last third of the ripening period, however, a significant reduction in the dry matter content was sometimes observed as a function of the length of the vegetation period. The data suggest that, with the exception of extreme weather conditions or an extremely long vegetation period, the maximum dry matter yield could be expected to range from 22–42%, depending on the vegetation period of the variety. The harvest date should be chosen to give a kernel moisture content of above 35% for biogas production and below 35% for silage production. In this phenophase most varieties mature when the stalks are still green, so it is unlikely that transport costs can be reduced by waiting for the vegetative mass to dry.

Persistence of modern varieties and hybrids of carrots and its dependence on the biochemical composition

2020 ◽  
pp. 16-19
Author(s):  
Е.В. Янченко
Keyword(s):  
Botrytis Cinerea ◽  
Sclerotinia Sclerotiorum ◽  
Dry Matter ◽  
Inverse Correlation ◽  
Dry Weight ◽  
Matter Content ◽  
White Rot ◽  
Dry Matter Content ◽  
Important Indicator ◽  
Modern Varieties

Цель исследований – дать оценку сохраняемости и болезнеустойчивости современных сортов и гибридов моркови столовой и определить корреляционные зависимости влияния биохимических показателей качества на сохраняемость и степень поражения моркови столовой различными видами болезней в процессе хранения. Исследования проводились в 2011–2016 годах во ВНИИО – филиале ФГБНУ ФНЦО по общепринятым методикам. В биохимической лаборатории отдела земледелия и агрохимии содержание сухого вещества определяли высушиванием до абсолютно сухого веса, общего сахара – по Бертрану, аскорбиновой кислоты – по Мурри, нитраты – ионоселективным методом. При характеристике моркови столовой важнейший показатель, определяющий его качество – количество сухого вещества и сахаров. В процессе хранения были выявлены следующие болезни моркови: серая гниль (Botrytis cinerea Pers. ex Fr.), белая гниль (Sclerotinia sclerotiorum (Lib.)), белая парша (Rhizoctonia carotae Rad.), альтернариоз (Alternaria radicina M., Dr. et E.). В большей степени сортообразцы моркови столовой поражались серой гнилью. Лучшими по сохраняемости сортообразцами были Корсар (94,6%), F1Берлин (94,5%), Берликум Роял (94,1%) и F1 Звезда (94%). Сохраняемость у зарубежных сортов и гибридов моркови столовой была немного выше, чем у отечественных (на 0,4%) как за счет меньшей величины убыли массы (6,3% против 6,4%), так и потерь от болезней (1,6% против 1,9%). Сохраняемость корнеплодов моркови находится в прямой корреляционной зависимости от содержания сухого вещества (r=+0,41), каротиноидов (r=+0,39), моносахаров (r­=+0,30) и суммы сахаров (r=+0,27). Проявление серой гнили находится в обратной корреляционной связи с содержанием сухого вещества и каротиноидов (r=-0,37 и r=-0,35 соответственно), белой парши – в прямой корреляции с содержанием сухого вещества , моносахаров и дисахаров (r= +0,21; r= +0,39; r= -0,41 соответственно), белой гнили в обратной корреляционной связи с содержанием сухого вещества, моносахаров и дисахаров. The purpose of the research is to assess the persistence and disease resistance of modern varieties and hybrids of carrots and to determine the correlation between the influence of biochemical quality indicators on the persistence and degree of damage to carrots by various types of diseases during storage. The research was conducted in 2011–2016 at ARRIVG – branch of FSBSI FSVC, according to generally accepted methods. In the biochemical laboratory of the Department of Agriculture and Agrochemistry, the dry matter content was determined by drying to absolutely dry weight, total sugar – by Bertran, ascorbic acid – by Murri, nitrates – by the ion-selective method. When describing carrots, the most important indicator that determines its quality is the amount of dry matter and sugars. During storage, the following diseases of carrots were detected: gray rot (Botrytis cinerea Pers. ex Fr.), white rot (Sclerotinia sclerotiorum (Lib.), white scab (Rhizoctonia carotae Rad.), alternariasis (Alternaria radicina M., Dr. et E.). To a greater extent, varieties of table carrots were affected by gray rot. The best preserved varieties were Corsar (94.6%), F1 Berlin (94.5%), Berlicum Royal (94.1%) and F1 Zvezda (94%). The persistence of foreign varieties and hybrids of table carrots was slightly higher than that of domestic ones by 0.4%. both due to a smaller amount of weight loss (6.3% vs. 6.4%) and losses from diseases (1.6% vs. 1.9%). The persistence of carrot root crops is directly correlated with the content of dry matter (r=+0.41), carotenoids (r=+0.39), monosaccharides (r=+0.30) and the amount of sugars (r=+0.27). The manifestation of gray rot is in inverse correlation with the content of dry matter and carotenoids (r=-0.37 and r=-0.35, respectively), white scab is in direct correlation with the content of dry matter (r= +0.21; r= +0.39; r= –0.41, respectively), white rot is in inverse correlation with the content of dry matter, monosaccharides and disaccharides.

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