Prediction of crop productivity and evapotranspiration with two photosynthetic parameter regionalization methods

SUMMARYParameter regionalization is the foundation for the spatial application of an ecosystem model at the canopy level and has been improved greatly by remote sensing (RS). Photosynthetic rate is restricted by the carboxylation rate, which is limited by the activity of the enzyme Rubisco. By including RS normalized difference vegetation index (NDVI) and census data of grain yield at the county level in an ecosystem model (vegetation interface processes (VIP) model), the pattern of photosynthetic parameter Vcmax (maximum catalytic activity of Rubisco) of winter wheat was obtained and then used to simulate the wheat yield and evapotranspiration (ET) in the North China Plain (referred to as the Vcmax method). To evaluate its performance, the simulated yield and ET were compared with those derived by the leaf area index (LAI) method using the retrieved LAI from NDVI to drive the VIP model. The results showed that the Vcmax method performed better than the LAI method in highly productive fields, while the LAI method described the inter-annual variations of yield more favourably in fields with low productivity. Over the study area, average yield (4520 kg/ha) and seasonal ET (360 mm) simulated by the LAI method was slightly lower than those simulated using the Vcmax method (4730 kg/ha for yield and 372 mm for ET). Compared with the census data of yield, the relative root mean square error (RMSE) of grain yield with Vcmax method (0·17) was lower than that of the LAI method (0·20). In conclusion, the physical model with spatial Vcmax pattern from remote sensing is reliable for regional crop productivity prediction.

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The Use of Remote Sensing to Assess the Effects of Water Stress on Wheat

Experimental Agriculture ◽

10.1017/s0014479700019402 ◽

1991 ◽

Vol 27 (4) ◽

pp. 423-429 ◽

Cited By ~ 20

Author(s):

R. K. Mahey ◽

Rajwant Singh ◽

S. S. Sidhu ◽

R. S. Narang

Keyword(s):

Remote Sensing ◽

Water Stress ◽

Grain Yield ◽

Spectral Data ◽

Growth And Development ◽

Vegetation Index ◽

Normalized Difference Vegetation Index ◽

Spectral Response ◽

Canopy Cover ◽

Area Index

SUMMARYGround-based radiometric measurements in the red and infrared bands were used to monitor the growth and development of wheat under irrigated and stressed conditions throughout the 1987–88 and 1988–89 growth cycles. Spectral data were correlated with plant height, leaf area index, total fresh and total dry biomass, plant water content and grain yield. The radiance ratio (R) and normalized difference vegetation index (NDVI) were highly and linearly correlated with yield, establishing the potential which remote sensing has for predicting grain yield. The correlation for R and NDVI was at a maximum between 75 and 104 days after sowing, corresponding with maximum green crop canopy cover. The differences in spectral response over time between irrigated and unirrigated crops allowed detection of water stress effects on the crop, indicating that a hand-held radiometer can be used to collect spectral data which can supply information on wheat growth and development.Efectos de lafalta de agua en el trigo

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Optimal time for remote sensing to relate to crop grain yield on the Canadian prairies

Canadian Journal of Plant Science ◽

10.4141/p03-070 ◽

2004 ◽

Vol 84 (1) ◽

pp. 97-103 ◽

Cited By ~ 25

Author(s):

Prakash Basnyat, Brian McConkey ◽

Guy P. Lafond ◽

Alan Moulin ◽

Yann Pelcat

Keyword(s):

Remote Sensing ◽

Grain Yield ◽

Spring Wheat ◽

Vegetation Index ◽

Normalized Difference Vegetation Index ◽

Crop Productivity ◽

Field Pea ◽

Optimal Time ◽

Remotely Sensed Data ◽

Canadian Prairies

The optimal time to acquire remote sensing imagery to relate to grain yield has not been thoroughly investigated for the Canadian prairies. Remotely sensed data collected when there is the best relationship with yield should provide useful information on the in-field spatial variability of biophysical factors affecting crop productivity relevant to site-specific management. The correlations of normalized difference vegetation index (NDVI) with grain yield for three dates in 2000 at Indian Head and Swift Current, SK, for field pea, canola, and spring wheat were compared. No single date consistently had the highest NDVI-yield correlation for all crops. The period between Jul. 10 to 30 was optimal to obtain NDVI to relate to grain yield for springseeded crops that typically mature in August. Significant NDVI-yield correlations for this period were confirmed in three additional site-years. In a further site-year, however, NDVI-yield correlation was significant for wheat and pea, but not for canola. Occasional problems relating the NDVI to canola yield were attributed to characteristics of the canola canopy, namely, the highly reflective flowers and the dropping of leaves after flowering. In terms of both magnitude and temporal stability of the NDVI-yield correlation, we ranked the crops as: spring wheat, then pea, and then canola. Key words: Remote sensing; grain yield, field pea, canola, wheat, normalized difference vegetation index

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Detecting Forest Degradation in the Three-North Forest Shelterbelt in China from Multi-Scale Satellite Images

Remote Sensing ◽

10.3390/rs13061131 ◽

2021 ◽

Vol 13 (6) ◽

pp. 1131

Author(s):

Tao Yu ◽

Pengju Liu ◽

Qiang Zhang ◽

Yi Ren ◽

Jingning Yao

Keyword(s):

Remote Sensing ◽

Vegetation Index ◽

Normalized Difference Vegetation Index ◽

Net Primary Production ◽

Remote Sensing Data ◽

Forest Degradation ◽

Observation Data ◽

Area Index ◽

Sensing Data ◽

Multi Scale

Detecting forest degradation from satellite observation data is of great significance in revealing the process of decreasing forest quality and giving a better understanding of regional or global carbon emissions and their feedbacks with climate changes. In this paper, a quick and applicable approach was developed for monitoring forest degradation in the Three-North Forest Shelterbelt in China from multi-scale remote sensing data. Firstly, Normalized Difference Vegetation Index (NDVI), Enhanced Vegetation Index (EVI), Ratio Vegetation Index (RVI), Leaf Area Index (LAI), Fraction of Photosynthetically Active Radiation (FPAR) and Net Primary Production (NPP) from remote sensing data were selected as the indicators to describe forest degradation. Then multi-scale forest degradation maps were obtained by adopting a new classification method using time series MODerate Resolution Imaging Spectroradiometer (MODIS) and Landsat Enhanced Thematic Mapper Plus (ETM+) images, and were validated with ground survey data. At last, the criteria and indicators for monitoring forest degradation from remote sensing data were discussed, and the uncertainly of the method was analyzed. Results of this paper indicated that multi-scale remote sensing data have great potential in detecting regional forest degradation.

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Assessment of Maize Growth and Development with High- and Medium-Resolution Remote Sensing Products

Agronomy ◽

10.3390/agronomy11050940 ◽

2021 ◽

Vol 11 (5) ◽

pp. 940

Author(s):

Rocío Ballesteros ◽

Miguel A. Moreno ◽

Fellype Barroso ◽

Laura González-Gómez ◽

José F. Ortega

Keyword(s):

Remote Sensing ◽

Precision Agriculture ◽

Vegetation Index ◽

Normalized Difference Vegetation Index ◽

Landsat Imagery ◽

Canopy Cover ◽

Remote Sensing Data ◽

Multilinear Regression ◽

Area Index ◽

Canopy Development

The availability of a great amount of remote sensing data for precision agriculture purposes has set the question of which resolution and indices, derived from satellites or unmanned aerial vehicles (UAVs), offer the most accurate results to characterize vegetation. This study focused on assessing, comparing, and discussing the performances and limitations of satellite and UAV-based imagery in terms of canopy development, i.e., the leaf area index (LAI), and yield, i.e., the dry aboveground biomass (DAGB), for maize. Three commercial maize fields were studied over four seasons to obtain the LAI and DAGB. The normalized difference vegetation index (NDVI) and visible atmospherically resistant index (VARI) from satellite platforms (Landsat 5TM, 7 ETM+, 8OLI, and Sentinel 2A MSI) and the VARI and green canopy cover (GCC) from UAV imagery were compared. The remote sensing predictors in addition to the growing degree days (GDD) were assessed to estimate the LAI and DAGB using multilinear regression models (MRMs). For LAI estimation, better adjustments were obtained when predictors from the UAV platform were considered. The DAGB estimation revealed similar adjustments for both platforms, although the Landsat imagery offered slightly better adjustments. The results obtained in this study demonstrate the advantage of remote sensing platforms as a useful tool to estimate essential agronomic features.

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Determining nitrogen deficiencies for maize using various remote sensing indices

Precision Agriculture ◽

10.1007/s11119-021-09861-4 ◽

2022 ◽

Author(s):

Brayden W. Burns ◽

V. Steven Green ◽

Ahmed A. Hashem ◽

Joseph H. Massey ◽

Aaron M. Shew ◽

...

Keyword(s):

Remote Sensing ◽

Grain Yield ◽

Nitrogen Fertilizer ◽

Vegetation Index ◽

Normalized Difference Vegetation Index ◽

Nitrogen Deficiency ◽

Maize Grain Yield ◽

Nitrogen Inputs ◽

Chlorophyll Index ◽

Maize Grain

AbstractDetermining a precise nitrogen fertilizer requirement for maize in a particular field and year has proven to be a challenge due to the complexity of the nitrogen inputs, transformations and outputs in the nitrogen cycle. Remote sensing of maize nitrogen deficiency may be one way to move nitrogen fertilizer applications closer to the specific nitrogen requirement. Six vegetation indices [normalized difference vegetation index (NDVI), green normalized difference vegetation index (GNDVI), red-edge normalized difference vegetation index (RENDVI), triangle greenness index (TGI), normalized area vegetation index (NAVI) and chlorophyll index-green (CIgreen)] were evaluated for their ability to detect nitrogen deficiency and predict grain maize grain yield. Strip trials were established at two locations in Arkansas, USA, with nitrogen rate as the primary treatment. Remote sensing data was collected weekly with an unmanned aerial system (UAS) equipped with a multispectral and thermal sensor. Relationships among index value, nitrogen fertilizer rate and maize growth stage were evaluated. Green NDVI, RENDVI and CIgreen had the strongest relationship with nitrogen fertilizer treatment. Chlorophyll Index-green and GNDVI were the best predictors of maize grain yield early in the growing season when the application of additional nitrogen was still agronomically feasible. However, the logistics of late season nitrogen application must be considered.

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Analyzing the Relationship between Animal Diversity and the Remote Sensing Vegetation Parameters: The Case of Xinjiang, China

Sustainability ◽

10.3390/su13179897 ◽

2021 ◽

Vol 13 (17) ◽

pp. 9897

Author(s):

Jinhui Wu ◽

Haoxin Li ◽

Huawei Wan ◽

Yongcai Wang ◽

Chenxi Sun ◽

...

Keyword(s):

Remote Sensing ◽

Vegetation Index ◽

Normalized Difference Vegetation Index ◽

Remote Sensing Data ◽

Bird Diversity ◽

Area Index ◽

Sensing Data ◽

Animal Diversity ◽

The Mean ◽

The Impact

An explicit analysis of the impact for the richness of species of the vegetation phenological characteristics calculated from various remote sensing data is critical and essential for biodiversity conversion and restoration. This study collected long-term the Normalized Difference Vegetation Index (NDVI), the Leaf Area Index (LAI), the Fraction of Absorbed Photosynthetically Active Radiation (FAPAR), and the Fractional Vegetation Cover (FVC), and calculated the six vegetation phenological characteristic parameters: the mean of the growing season, the mean of the mature season, the mean of the withered season, the annual difference value, the annual cumulative value, and the annual standard deviation in the Xinjiang Uygur Autonomous Region. The relationships between the vegetation phenological characteristics and the species richness of birds and mammals were analyzed in spatial distribution. The main findings include: (1) The correlation between bird diversity and vegetation factors is greater than that of mammals. (2) For remote sensing data, FAPAR is the most important vegetation parameter for both birds and mammals. (3) For vegetation phenological characteristics, the annual cumulative value of the LAI is the most crucial vegetation phenological parameter for influencing bird diversity distribution, and the annual difference value of the NDVI is the most significant driving factor for mammal diversity distribution.

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Estimation of the average district winter wheat yield based on satellite and ground meteorological data

Hydrometeorological research and forecasting ◽

10.37162/2618-9631-2020-3-103-121 ◽

2020 ◽

Vol 3 ◽

pp. 103-121

Author(s):

A.D. Kleschenko ◽

◽

O.V. Savitskaya ◽

S.A. Kosyakin ◽

◽

...

Keyword(s):

Winter Wheat ◽

Vegetation Index ◽

Normalized Difference Vegetation Index ◽

Meteorological Data ◽

Wheat Yield ◽

Condition Index ◽

Area Index ◽

The North ◽

Independent Information ◽

Winter Wheat Yield

The research results of the dependence of the average district winter wheat yield on satellite and ground meteorological information for the subjects of the North Caucasian and Volga UGMS are presented. The following satellite indices were used in the work: NDVI (Normalized Difference Vegetation Index), VCI (Vegetation Condition Index) and LAI (Leaf Area Index). The method of interpolation of inverse weighted squares of distances for obtain a set of meteorological parameters for districts there were no weather stations was used. Districts for taking into account agroclimatic conditions were combined into groups using Shashko's Agroclimatic Regionalization method. The selection of parameters that have the greatest impact on the yield was carried out using the correlation-regression analysis method. The corresponding regression models were obtained for the researched regions of the Russian Federation. Verification of the obtained models on dependent and independent information showed a fairly good result. Keywords: NDVI, LAI, interpolation, Shashko's Agroclimatic Regionalization, average district yield, meteorological information Tab. 5. Fig. 7. Ref. 20.

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Field estimate with NDVI of grain yield and quality of wheat flour

Revista Brasileira de Engenharia Agrícola e Ambiental ◽

10.1590/1807-1929/agriambi.v25n12p801-806 ◽

2021 ◽

Vol 25 (12) ◽

pp. 801-806

Author(s):

João P. K. Reznick ◽

Volnei Pauletti ◽

Gabriel Barth

Keyword(s):

Grain Yield ◽

Optical Sensors ◽

Vegetation Index ◽

Normalized Difference Vegetation Index ◽

Wheat Yield ◽

Field Conditions ◽

Yield And Quality ◽

Technological Quality ◽

Grain Yield And Quality

ABSTRACT Nitrogen fertilization is essential for wheat yield and quality but needs more accuracy, and the use of proximal optical sensors in the field can assist in this goal. This study aimed to verify if it is possible to use the normalized difference vegetation index (NDVI) obtained throughout the wheat growth phase to estimate the grain yield and the technological quality of the flour from cultivars submitted to nitrogen doses. The experiment was conducted at field conditions in Ponta Grossa, PR, Southern Brazil. The experimental design was randomized blocks in a 4 × 6 factorial scheme with four replicates. The cultivars Quartzo, Gralha Azul, Sinuelo, and Toruk, combined with six doses of N (0, 40, 80, 120, 160, and 200 kg ha-1 of N), were evaluated. The NDVI values were sensitive to both nitrogen doses and the different cultivars. There was a relationship between NDVI and grain yield, protein, and gluten concentration of flour. The NDVI estimated the gluten strength, stability, tenacity, extensibility of the mass, and tenacity/extensibility ratio of the flour obtained at the beginning of the cycle, but not for all cultivars. The determinations of NDVI with active optical sensor GreenSeeker in wheat are efficient to estimate the grain yield and the flour quality under field conditions, allowing to generate models for estimation of these variables separately for each cultivar.

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Investigação do Uso de Parâmetros Biofísicos na Influência do Microclima Dentro de Áreas Urbanas

Ensaios e Ciência C Biológicas Agrárias e da Saúde ◽

10.17921/1415-6938.2021v25n1p16-20 ◽

2021 ◽

Vol 25 (1) ◽

pp. 16-20

Author(s):

Denise Pereira Canedo Meira Vieira ◽

Victor Hugo De Morais Danelichen ◽

Mariane Batista de Lima Moraes Brandão Campos

Keyword(s):

Remote Sensing ◽

Image Processing ◽

Digital Image Processing ◽

Digital Image ◽

Vegetation Index ◽

Normalized Difference Vegetation Index ◽

Urban Spaces ◽

Biophysical Parameters ◽

Electronic Library ◽

Area Index

Diante da necessidade de obtenção de informações relacionadas ao microclima e influência da vegetação dentro de um perímetro urbano na qualidade de vida dos seus habitantes se define como parâmetros biofísicos a serem estudados nesta pesquisa o Normalized Difference Vegetation Index (NDVI) e Leaf Area Index (LAI). Considerando que o Sensoriamento Remoto é uma tecnologia de baixo custo e fácil aquisição podendo ser obtidas gratuitamente, via banco de dados disponíveis na Internet, verifica-se que o sensoriamento pode ser utilizado como fonte confiável de levantamento desses parâmetros. Este estudo tem como objetivo analisar a produção científica sobre o uso do sensoriamento remoto como tecnologia alternativa para obtenção de informações de parâmetros biofísicos, como o NDVI e LAI, possibilitando a preservação e o planejamento da vegetação nos espaços urbanos. O artigo se trata de uma revisão narrativa realizada através de consultas aos bancos de dados Scientific Electronic Library Online (SciELO), Literatura Latino-Americana e, principalmente, do banco de dados Scopus (Elsevier) da CAPES. Como critérios de inclusão foram aplicados: artigos com disponibilidade completa de 2010 até 2020 e com relação direta com o estudo. É possível concluir que como o ambiente sofre alterações constantes pela ação antrópica e a interpretação de imagens de satélite é uma fonte direta de se determinar a dinâmica dos processos envolvidos em tais alterações, a fotointerpretação e o processamento digital de imagens assumem papel de grande importância. Tais ferramentas permitem fornecer subsídios para a compreensão dos fenômenos ambientais, além da possibilidade de planejamento estratégico no planejamento urbano. As revisões bibliográficas realizadas indicam a fotointerpretação e o processamento digital de imagens como ferramentas ainda pouco utilizadas para estimar os parâmetros biofísicos no contexto urbano. Palavras-chave: Sensoriamento Remoto. Espaços Urbanos. NDVI e LAI. AbstractGiven the need to obtain information related to the vegetation microclimate and influence within an urban perimeter on the quality of life of its inhabitants, it was defined as biophysical parameters to be studied in this research the Normalized Difference Vegetation Index (NDVI) and Leaf Area Index (LAI). Considering that Remote Sensing is a low cost and easy acquisition technology and can be obtained free of charge via the database available on the Internet, it will be verified that sensing can be used as a reliable source of survey of these parameters. This study aims to analyze the scientific production on the use of remote sensing as an alternative technology to obtain information from biophysical parameters, such as NDVI and LAI, enabling the vegetation preservation and planning in urban spaces. The article is a narrative review carried out through consultations with the Databases Scientific Electronic Library Online (SciELO), Latin American Literature and mainly the Scopus database (Elsevier) of CAPES. Inclusion criteria were applied: articles with complete availability from 2010 to 2020 and with direct relation to the study. It is possible to conclude that the environment undergoes constant changes by anthropic action and the satellite images interpretation is a direct source for determining the processes dynamics involved in such changes, photointerpretation and digital image processing play a major role. Such tools allow to provide subsidies for the understanding of environmental phenomena, in addition to the possibility of strategic planning in urban planning. The bibliographic reviews performed indicate photointerpretation and digital image processing as tools still little used to estimate biophysical parameters in the urban context. Keywords: Remote Sensing. Urban Spaces. NDVI and LAI

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Analisis Tutupan Mangrove Taman Nasional Berbak–Sembilang melalui Citra Landsat-8 dan Pemantauan LAI

Jurnal Kelautan Tropis ◽

10.14710/jkt.v24i3.12278 ◽

2021 ◽

Vol 24 (3) ◽

pp. 393-401

Author(s):

Tengku Zia Ulqodry ◽

Andreas Eko Aprianto ◽

Andi Agussalim ◽

Riris Aryawati ◽

Afan Absori

Keyword(s):

Remote Sensing ◽

Leaf Area Index ◽

Leaf Area ◽

Vegetation Index ◽

Normalized Difference Vegetation Index ◽

Remote Sensing Data ◽

Landsat 8 ◽

Area Index ◽

Sensing Data ◽

Canopy Coverage

Berbak Sembilang National Park of South Sumatra Region (BSNP South Sumatera) is the largest mangrove ecosystem in the western part of Indonesia. Monitoring of mangrove coverage in BSNP South Sumatera carried out using Landsat-8 imagery data based on NDVI values (Normalized Difference Vegetation Index) integrated with mangrove LAI (Leaf Area Index) data. The research purpose was to analyze the mangrove coverage and mapping the density of the mangrove vegetation canopy with the integration of remote sensing data and LAI. This research conducted field survey with LAI measurement of mangrove canopy coverage and integrated with remote sensing data to validate map. The determination and correlation coefficient of NDVI and LAI value of canopy coverage was high (R2 = 0.69 ; r = 83.07).The results of research indicated that the overall distribution of the mangrove area was 94,622.05 ha. The NDVI image integration map with LAI resulted in 4 mangrove canopy density classes consisted of rare canopy (688.80 ha ; 0.73%), moderately dense canopy (1,139.55 ha ; 1.2%), dense canopy (35,003.46 ha ; 37%), and very dense canopy (57,790.20 ha ; 61.07%). Taman Nasional Berbak Sembilang wilayah Sumatera Selatan (TNBS Sumsel) merupakan kawasan ekosistem mangrove terluas di wilayah Indonesia bagian barat. Pemantauan kerapatan kanopi vegetasi mangrove di TNBS Sumsel dilakukan menggunakan data Citra Landsat-8 berdasarkan nilai NDVI (Normalized Difference Vegetation Index) yang diintegrasikan dengan data LAI (Leaf Area Index) mangrove di lapangan. Penelitian ini bertujuan untuk menganalisis tutupan vegetasi mangrove dan memetakan sebaran kerapatan kanopi mangrove dengan integrasi data penginderaan jauh dan LAI. Penelitian ini menggunakan metode pengolahan data survei lapangan dan hasil pengolahan citra satelit. Nilai koefisien determinasi dan korelasi antara nilai NDVI dengan nilai LAI tutupan Kanopi di Lapangan dikategorikan tinggi (R2 = 0,69 ; r = 83,07). Hasil penelitian menunjukkan tutupan mangrove secara keseluruhan seluas 94.622,05 ha. Peta integrasi citra NDVI dengan LAI mangrove di lapangan menghasilkan 4 kelas kerapatan kanopi mangrove yakni kanopi jarang seluas 688,80 ha (0,73%), kanopi sedang seluas 1.139,55 ha (1,2%), kanopi lebat seluas 35.003,46 ha (37%), dan kanopi sangat lebat seluas 57.790,20 ha (61,07%).

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