Coffee yield estimation by Landsat-8 imagery considering shading effects of planting row's orientation in center pivot

2021 ◽  
Vol 24 ◽  
pp. 100613
Author(s):  
Pedro Arthur de Azevedo Silva ◽  
Marcelo de Carvalho Alves ◽  
Fábio Moreira da Silva ◽  
Vanessa Castro Figueiredo
Keyword(s):  
Landsat 8 ◽  
Yield Estimation ◽  
Coffee Yield ◽  
Center Pivot ◽  
Shading Effects

scholarly journals Mapping groundwater abstractions from irrigated agriculture: big data, inverse modeling, and a satellite–model fusion approach

2020 ◽  
Vol 24 (11) ◽  
pp. 5251-5277 ◽  
Author(s):  
Oliver Miguel López Valencia ◽  
Kasper Johansen ◽  
Bruno José Luis Aragón Solorio ◽  
Ting Li ◽  
Rasmus Houborg ◽  
...  
Keyword(s):  
Water Management ◽  
Saudi Arabia ◽  
Land Surface ◽  
Landsat 8 ◽  
Groundwater Abstraction ◽  
Model Fusion ◽  
Center Pivot ◽  
Gravimetric Data ◽  
Water Accounting ◽  
Fusion Approach

Abstract. The agricultural sector in Saudi Arabia has witnessed rapid growth in both production and area under cultivation over the last few decades. This has prompted some concern over the state and future availability of fossil groundwater resources, which have been used to drive this expansion. Large-scale studies using satellite gravimetric data show a declining trend over this region. However, water management agencies require much more detailed information on both the spatial distribution of agricultural fields and their varying levels of water exploitation through time than coarse gravimetric data can provide. Relying on self-reporting from farm operators or sporadic data collection campaigns to obtain needed information are not feasible options, nor do they allow for retrospective assessments. In this work, a water accounting framework that combines satellite data, meteorological output from weather prediction models, and a modified land surface hydrology model was developed to provide information on both irrigated crop water use and groundwater abstraction rates. Results from the local scale, comprising several thousand individual center-pivot fields, were then used to quantify the regional-scale response. To do this, a semi-automated approach for the delineation of center-pivot fields using a multi-temporal statistical analysis of Landsat 8 data was developed. Next, actual crop evaporation rates were estimated using a two-source energy balance (TSEB) model driven by leaf area index, land surface temperature, and albedo, all of which were derived from Landsat 8. The Community Atmosphere Biosphere Land Exchange (CABLE) model was then adapted to use satellite-based vegetation and related surface variables and forced with a 3 km reanalysis dataset from the Weather Research and Forecasting (WRF) model. Groundwater abstraction rates were then inferred by estimating the irrigation supplied to each individual center pivot, which was determined via an optimization approach that considered CABLE-based estimates of evaporation and TSEB-based satellite estimates. The framework was applied over two study regions in Saudi Arabia: a small-scale experimental facility of around 40 center pivots in Al Kharj that was used for an initial evaluation and a much larger agricultural region in Al Jawf province comprising more than 5000 individual fields across an area exceeding 2500 km2. Total groundwater abstraction for the year 2015 in Al Jawf was estimated at approximately 5.5 billion cubic meters, far exceeding any recharge to the groundwater system and further highlighting the need for a comprehensive water management strategy. Overall, this novel data–model fusion approach facilitates the compilation of national-scale groundwater abstractions while also detailing field-scale information that allows both farmers and water management agencies to make informed water accounting decisions across multiple spatial and temporal scales.

In-season crop classification using optical remote sensing with random forest over irrigated agricultural fields in Australia

2021 ◽  
Author(s):  
Zitian Gao ◽  
Danlu Guo ◽  
Dongryeol Ryu ◽  
Andrew Western
Keyword(s):  
Random Forest ◽  
Water Use ◽  
Landsat 8 ◽  
Irrigation Season ◽  
Yield Estimation ◽  
Cropping Season ◽  
Crop Type ◽  
Eastern Australia ◽  
Crop Classification ◽  
Type Classification

<p>Timely classification of crop types is critical for agronomic planning in water use and crop production. However, crop type mapping is typically undertaken only after the cropping season, which precludes its uses in later-season water use planning and yield estimation. This study aims 1) to understand how the accuracy of crop type classification changes within cropping season and 2) to suggest the earliest time that it is possible to achieve reliable crop classification. We focused on three main summer crops (corn/maize, cotton and rice) in the Coleambally Irrigation Area (CIA), a major irrigation district in south-eastern Australia consisting of over 4000 fields, for the period of 2013 to 2019. The summer irrigation season in the CIA is from mid-August to mid-May and most farms use surface irrigation to support the growth of summer crops. We developed models that combine satellite data and farmer-reported information for in-season crop type classification. Monthly-averaged Landsat spectral bands were used as input to Random Forest algorithm. We developed multiple models trained with data initially available at the start of the cropping season, then later using all the antecedent images up to different stages within the season. We evaluated the model performance and uncertainty using a two-fold cross validation by randomly choosing training vs. validation periods. Results show that the classification accuracy increases rapidly during the first three months followed by a marginal improvement afterwards. Crops can be classified with a User’s accuracy above 70% based on the first 2-3 months after the start of the season. Cotton and rice have higher in-season accuracy than corn/maize. The resulting crop maps can be used to support activities such as later-season system scale irrigation decision-making or yield estimation at a regional scale.</p><p>Keywords: Landsat 8 OLI, in-season, multi-year, crop type, Random Forest</p>

scholarly journals The Impact of Multi-Sensor Data Assimilation on Plant Parameter Retrieval and Yield Estimation for Sugar Beet

2015 ◽  
Vol XL-7/W3 ◽  
pp. 19-25 ◽  
Author(s):  
M. Hodrius ◽  
S. Migdall ◽  
H. Bach ◽  
T. Hank
Keyword(s):  
Data Assimilation ◽  
Plant Growth ◽  
Sugar Beet ◽  
Leaf Area ◽  
Satellite Imagery ◽  
Crop Growth ◽  
Sensor Data ◽  
Landsat 8 ◽  
Yield Estimation ◽  
The Impact

Yield Maps are a basic information source for site-specific farming. For sugar beet they are not available as in-situ measurements. This gap of information can be filled with Earth Observation (EO) data in combination with a plant growth model (PROMET) to improve farming and harvest management. The estimation of yield based on optical satellite imagery and crop growth modelling is more challenging for sugar beet than for other crop types since the plants’ roots are harvested. These are not directly visible from EO. In this study, the impact of multi-sensor data assimilation on the yield estimation for sugar beet is evaluated. Yield and plant growth are modelled with PROMET. This multi-physics, raster-based model calculates photosynthesis and crop growth based on the physiological processes in the plant, including the distribution of biomass into the different plant organs (roots, stem, leaves and fruit) at different phenological stages. <br><br> The crop variable used in the assimilation is the green (photosynthetically active) leaf area, which is derived as spatially heterogeneous input from optical satellite imagery with the radiative transfer model SLC (Soil-Leaf-Canopy). Leaf area index was retrieved from RapidEye, Landsat 8 OLI and Landsat 7 ETM+ data. It could be shown that the used methods are very suitable to derive plant parameters time-series with different sensors. The LAI retrievals from different sensors are quantitatively compared to each other. Results for sugar beet yield estimation are shown for a test-site in Southern Germany. The validation of the yield estimation for the years 2012 to 2014 shows that the approach reproduced the measured yield on field level with high accuracy. Finally, it is demonstrated through comparison of different spatial resolutions that small-scale in-field variety is modelled with adequate results at 20 m raster size, but the results could be improved by recalculating the assimilation at a finer spatial resolution of 5 m.

scholarly journals IMAGENS DO LANDSAT- 8 NO MAPEAMENTO DE SUPERFÍCIES EM ÁREA IRRIGADA

Irriga ◽  
2015 ◽  
Vol 1 (2) ◽  
pp. 30-36
Author(s):  
JANNAYLTON EVERTON OLIVIERA SANTOS ◽  
Donizeti Aparecido Pastori Nicolete ◽  
Roberto Filgueiras ◽  
Victor Costa Leda ◽  
Célia Regina Lopes Zimback
Keyword(s):  
Vegetation Index ◽  
Atmospheric Correction ◽  
Growth Stages ◽  
Landsat 8 ◽  
Land Uses ◽  
Radiometric Calibration ◽  
Estimation Of Parameters ◽  
Irrigated Area ◽  
Dark Object ◽  
Center Pivot

IMAGENS DO LANDSAT- 8 NO MAPEAMENTO DE SUPERFÍCIES EM ÁREA IRRIGADA  JANNAYLTON ÉVERTON OLIVEIRA SANTOS¹; DONIZETI APARECIDO PASTORI NICOLETE¹; ROBERTO FILGUEIRAS¹; VICTOR COSTA LEDA² E CÉLIA REGINA LOPES ZIMBACK¹ [1] Departamento de Ciência do Solo e Recursos Ambientais da UNESP - campus Botucatu – SP,Programa de Irrigação e Drenagem UNESP/FCA. Email:[email protected], [email protected], [email protected], [email protected] Departamento de Ciência do Solo e Recursos Ambientais da UNESP - campus Botucatu – SP, Programa de Energia na agricultura UNESP/FCA. Email: [email protected]  1 RESUMO O trabalho tem como objetivo analisar os parâmetros NDVI (Normalized Difference Vegetation Index) e SAVI (Soil Adjusted Vegetation Index) para dois períodos, chuvoso e seco, em área irrigada. A área de estudo apresenta constante expansão na irrigação por pivô central, sendo localizada nas proximidades do município de Paranapanema – SP. As imagens foram processadas utilizando o programa QGIS 2.2. Para a obtenção dos índices realizou-se a calibração radiométrica, que consiste na transformação dos números digitais para correspondentes físicos, radiância e reflectância, e correção atmosférica por meio do método DOS 1 (Dark Object Substraction). Após os processamentos computou-se os índices de vegetação, os quais deram subsídio para o monitoramento das culturas agrícolas nos diferentes manejos (irrigado e sequeiro) e épocas de análise (chuvoso e seco). Como auxílio para o monitoramento das áreas, fusionou-se uma composição RGB 432, com a banda pancromática, o que permitiu uma pré-análise das condições e dos tipos de uso do solo na área de estudo. As cartas obtidas de NDVI e SAVI permitiram inferir sobre as condições fisiológicas e estádios fenológicos da vegetação nos diferentes usos do solo. No período de estiagem os índices médios obtiveram valores inferiores ao do período chuvoso, tendo isto ocorrido, principalmente, devido as condições de estresse hídrico característico da época. Desse modo, o cômputo dos parâmetros para a área de estudo foram de extrema valia na análise das condições da vegetação nos diferentes cenários, pois por meio desses foi possível inferir sobre as diferenças encontradas nos períodos e nos diferentes usos do solo, o que auxilia os agricultores em tomadas de decisão com relação ao manejo de suas áreas, no que tange as questões relacionadas a necessidades hídrica das culturas.Palavras-chave: Sensoriamento remoto, monitoramento agrícola, pivô central.  SANTOS, J. E. O.; NICOLETE, D. A. P.; FILGUEIRAS, R.; LEDA, V. C.; ZIMBACK, C. R. L.IMAGES OF LANDSAT-8 TO MONITOR THE SURFACES ON IRRIGATED AREA    2 ABSTRACT The study aims to analyze NDVI (Difference Vegetation Index Normalized) and SAVI (Soil Adjusted Vegetation Index) for two periods (rainy and dry) on irrigated area. The study area has constant expansion on irrigation center pivot, it is located near the Paranapanema ­- SP county. For this study we used two images of Landsat ­8 orbital platform. The images were processed using QGIS 2.2 program. To obtain the indexes, it was held radiometric calibration, which is the transformation of digital numbers in corresponding physical, radiance and reflectance, and atmospheric correction using the DOS method (Dark Object Substraction). These procedures were performed on semi automatic classification plugin. After appropriate calibrations and corrections, it were computed the vegetation indexes. These gave allowance for monitoring agricultural crops in different management systems (irrigated and rainfed) and analysis of seasons (wet and dry). As an aid for monitoring areas, we merged a RGB ­432 composition, with a panchromatic band. This product allowed a pre - analysis of conditions and types of land use in the study area. The maps obtained from NDVI and SAVI, allowed to infer about the physiological conditions and growth stages vegetation in different land uses. During the dry season, we found average rates which has lower values than the rainy season. This occurred, mainly, due to water stress conditions, which is characteristic of that season. Thus, the estimation of parameters for the study area were extremely valuable in analysis of vegetation conditions, on different scenarios, because through these, became possible to infer about the differences in seasons analized and different land uses. Then, these analisys served as an aid for farmers in decision­ making, regard the management of their areas, which is related to water requirements of crops. Keywords: Remote sensing, agriculture monitoring, center pivot.

scholarly journals Mapping groundwater abstractions from irrigated agriculture: big data, inverse modeling and a satellite-model fusion approach

2020 ◽  
Author(s):  
Oliver Lopez ◽  
Kasper Johansen ◽  
Bruno Aragon ◽  
Ting Li ◽  
Rasmus Houborg ◽  
...  
Keyword(s):  
Water Management ◽  
Saudi Arabia ◽  
Land Surface ◽  
Landsat 8 ◽  
Groundwater Abstraction ◽  
Model Fusion ◽  
Center Pivot ◽  
Gravimetric Data ◽  
Water Accounting ◽  
Fusion Approach

Abstract. The agricultural sector in Saudi Arabia has witnessed rapid growth in both production and area under cultivation over the last few decades. This has prompted some concern over the state and future availability of fossil groundwater resources, which have been used to drive this expansion. Large-scale studies using satellite gravimetric data show a declining trend over this region. However, water management agencies require much more detailed information on both the spatial distribution of agricultural fields, and their varying levels of water exploitation through time, than coarse gravimetric data can provide. Relying on self-reporting from farm operators or sporadic data collection campaigns to obtain needed information are not feasible options, nor do they allow for retrospective assessments. In this work, a water accounting framework that combines satellite data, meteorological output from weather prediction models, and a modified land surface hydrology model, was developed to provide information on both irrigated crop-water use and groundwater abstraction rates. Results from the local-scale, comprising several thousand individual center-pivot fields, were then used to quantify the regional-scale response. To do this, a semi-automated approach for the delineation of center-pivot fields using a multi-temporal statistical analysis of Landsat 8 data was developed. Next, actual crop evaporation rates were estimated using a two-source energy balance (TSEB) model driven by leaf area index, land surface temperature, and albedo inputs, all of which were derived from Landsat 8. The Community Atmosphere Biosphere Land Exchange (CABLE) model was then adapted to use satellite-based vegetation and related surface variables, and forced with a 3 km reanalysis dataset from the Weather Research and Forecasting (WRF) model. Groundwater abstraction rates were then inferred by estimating the irrigation supplied to each individual center-pivot, which was determined via an optimization approach that considered CABLE-based estimates of evaporation and TSEB-based satellite estimates. The framework was applied over two study regions in Saudi Arabia: a small-scale experimental facility of around 40 center-pivots in Al Kharj that was used for an initial evaluation, and a much larger agricultural region in Al Jawf province comprising more than 5,000 individual fields across an area exceeding 2,500 km2. Total groundwater abstraction for the year 2015 in Al Jawf were estimated at approximately 5.5 billion cubic meters, far exceeding any recharge to the groundwater system and further highlighting the need for a comprehensive water management strategy. Overall, this novel data-model fusion approach facilitates the compilation of national-scale groundwater abstractions, while also detailing field-scale information that allows both farmers and water management agencies to make informed water accounting decisions across multiple spatial and temporal scales.

scholarly journals Geospatial Techniques for Paddy Crop Acreage and Yield Estimation

2020 ◽  
pp. 71-79
Author(s):  
Krishna Desai ◽  
N. L. Rajesh ◽  
U. K. Shanwad ◽  
N. Ananda ◽  
B. G. Koppalkar ◽  
...  
Keyword(s):  
Biomass Yield ◽  
Satellite Image ◽  
Ground Truth ◽  
Landsat 8 ◽  
Yield Estimation ◽  
Crop Acreage ◽  
Relative Deviation ◽  
Ground Truth Data ◽  
Ground Truthing ◽  
Paddy Crop

Paddy crop acreage and yield estimation using geospatial technology were carried out in North Eastern Dry Zone (Zone-2) covering Shorapur taluk, Yadgir district, Karnataka state, India, during rabi late sown or summer 2016-17 season. The study area is located between 16° 20ꞌ to 17° 45ꞌ north latitude and 76° 04ꞌ to 77° 42ꞌ east longitude, at an elevation of 428 meters above mean sea level. The RESOURCESAT-1 LISS III satellite image of 31st January 2017, 24th February 2017, 20th March 2017 and LANDSAT-8 of 15th April 2017 were used for paddy crop acreage estimation at taluk level. Paddy signatures were identified using ground truth GPS data and then, these temporal imageries were subjected to NDVI classification and estimated the paddy biomass and further validated with the ground-truthing in corresponding to Green Seeker NDVI value. The estimated paddy crop acreage through imagery NDVI were 2145.75 ha, 17602.21 ha, 19838 ha and 23004.01 ha area during Jan-2017, Feb-2017, March-2017 and April-2017 respectively. When these results were compared with acreage estimates as reported by the State Department of Agriculture, shown a relative deviation of 11.41, 35.78, 23.01& 3.89 per cent for Jan-2017, Feb-2017, March-2017 and April-2017 respectively. Therefore, LandSat-8 NDVI paddy acreage has showed significantly on par with the ground truth data at the crop harvest stage. Relative deviation of 10.75 for yield comparison among imagery NDVI biomass yield with the DOA yield estimation infer that NDVI biomass yield estimation would give better result at 90 days after sowing. Positive correlation of NDVI values with estimated acreage and yield, indicates that application of remote sensing techniques for forecasting paddy biomass yield is more accurate, economical and could be beneficial to the policy makers for quick decisions.

scholarly journals LEVANTAMENTO DA ÁREA IRRIGADA POR PIVÔ CENTRAL NO ESTADO DO RIO GRANDE DO SUL

Irriga ◽  
2018 ◽  
Vol 21 (2) ◽  
pp. 300 ◽  
Author(s):  
Juliano Dalcin Martins ◽  
Iago Samuel Bohrz ◽  
Enrico Fleck Tura ◽  
Miguel Fredrich ◽  
Rodrigo Porto Veronez ◽  
...  
Keyword(s):  
Rio Grande ◽  
Google Earth ◽  
Irrigated Agriculture ◽  
Landsat 8 ◽  
Rio Grande Do Sul ◽  
Landsat 8 Oli ◽  
Santa Barbara ◽  
Center Pivot ◽  
Average Size ◽  
E Mail

LEVANTAMENTO DA ÁREA IRRIGADA POR PIVÔ CENTRAL NO ESTADO DO RIO GRANDE DO SUL JULIANO DALCIN MARTINS1; IAGO SAMUEL BOHRZ2; MIGUEL FREDRICH2; RODRIGO PORTO VERONEZ2; GREISSON ALEX KUNZ2;ENRICO FLECK TURA2 1Eng. Agrônomo, Prof. Doutor, Instituto Federal de Educação, Ciência e Tecnologia do Rio Grande do Sul - Câmpus Ibirubá, IFRS–Ibirubá, Rua Nelsi Ribas Fritsch, 1111. Bairro Esperança, CEP: 98200-000, Ibirubá/RS. Fone(54) 3324-8100. E-mail: [email protected]êmico do Curso de Agronomia. IFRS-Câmpus Ibirubá. E-mail: [email protected], [email protected], [email protected], [email protected]  1 RESUMO O objetivo deste estudo foi identificar e quantificar as áreas irrigadas por pivô central no Estado do Rio Grande do Sul, por bacias hidrográficas e por municípios. As áreas irrigadas por pivôs centrais foram levantadas através da identificação visual, com base no mosaico formado por imagens do satélite Landsat 8 OLI/TIRS, inseridos na plataforma Google Earth e na base de dados do levantamento de pivôs centrais no Brasil 2013 realizado pela (EMBRAPA/ANA). O período de mapeamento utilizado foi considerando imagens disponíveis até Março de 2015. Foram identificados 1.753 pivôs centrais, ocupando uma área irrigada de 111.122,9 hectares, e apresentando tamanho médio de 63,66 ha. Cerca de 84,04% dos pivôs concentram-se nas bacias hidrográficas do Rio Alto Jacuí (21,5%), Rio Ijuí (19,3%), Rio Piratinim (15,4%), Rios Turvo, Santa Rosa e Santo Cristo (14,4%) Rio Ibicuí (8,4%), e Rio da Várzea (5,7%). A maior concentração de pivôs centrais ocorre nos municípios de Cruz Alta (129 pivôs, 9.050,05 ha), São Miguel das Missões (76 pivôs, 5.433,5 ha), Santo Augusto (85 pivôs, 5.353,54 ha), Santa Bárbara do Sul (78 pivôs, 5.333,29 ha) e São Borja (62 pivôs, 4.560,8 ha). Palavras-chave: agricultura irrigada, bacias hidrográficas, recursos hídricos.  MARTINS, J. D.; BOHRZ, I. S.; FREDRICH, M.; VERONEZ, R. P.; KUNZ, G. A.ASSESSMENT OF AREA IRRIGATED BY CENTER PIVOT IN STATE OF RIO GRANDE DO SUL  2 ABSTRACT The aim of this study was to identify and quantify the areas irrigated by center pivot in the state of Rio Grande do Sul, for watersheds and municipalities. The areas irrigated by central pivots were digitized by visual identification based on the mosaic formed by images of Landsat 8 OLI/TIRS inserted in the Google Earth platform and survey data base center pivots in Brazil in 2013 conducted by (EMBRAPA/ANA). The mapping period used considered images available until March 2015. 1,753 center pivots were identified, occupying an irrigated area of 111,122,9 ha, and with average size of 63.66 ha. Around 84.04% of the center pivots are concentrated in the basin of the High Jacuí River (21.5%), Ijuí River (19.3%), Piratinim River (15.4%), Turvo, Santa Rosa and Santo Cristo Rivers ( 14.4%) Ibicuí River (8.4%), and Várzea River (5.7%). The municipalities with the highest areas occupied by pivots occur in Cruz Alta (129 pivots, 9050.05 ha), São Miguel das Missões (76 pivots, 5433.5 ha), Santo Augusto (85 pivots, 5353.54 ha), Santa Barbara do Sul (78 pivots, 5333.29 ha) and São Borja (62 pivots, 4560.8 ha). Keywords:  irrigated agriculture, watersheds, water resources

scholarly journals Farmland productivity estimation based on vegetation indexes from remote sensing data

2020 ◽  
Vol 206 ◽  
pp. 02015
Author(s):  
Shaoshuai Li ◽  
Baipeng Li ◽  
Wenjing Cao
Keyword(s):  
Remote Sensing ◽  
Production Capacity ◽  
Remote Sensing Data ◽  
High Standard ◽  
Estimation Accuracy ◽  
Landsat 8 ◽  
Yield Estimation ◽  
Sensing Technology ◽  
Vegetation Indexes ◽  
Estimation Models

Ensuring food security is a long-term and arduous task. Timely and accurate grasp of grain production capacity information can provide favourable data support for the nation to formulate macroeconomic plans and food policies. With the development of remote sensing technology, it has been widely used in crop yield estimation models. In this paper, the yield of spring maize in Da’an of Jilin province was estimated based on vegetation indexes calculated from Landsat-8 images. The results have shown that the fitting degree and estimation accuracy of yield estimation models at tasselling stage are significantly better than those at milk stage. Among these vegetation indexes, the model based on GNDVI has better fitting degree and estimation accuracy. This paper can provide reference for the post construction evaluation of high standard farmland in China.

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