Mapping of multitemporal rice (Oryza sativa L.) growth stages using remote sensing with multi-sensor and machine learning : a thesis dissertation presented in partial fulfilment of the requirements for the degree of Doctor of Philosophy in Earth Science at Massey University, Manawatū, New Zealand

Figure 2.1 is adapted and re-used under a Creative Commons Attribution 4.0 International (CC BY 4.0) license.Rice (Oryza Sativa) plays a pivotal role in food security for Asian countries, especially in Indonesia. Due to the increasing pressure of environmental changes, such as land use and climate, rice cultivation areas need to be monitored regularly and spatially to ensure sustainable rice production. Moreover, timely information of rice growth stages (RGS) can lead to more efficient of inputs distribution from water, seed, fertilizer, and pesticide. One of the efficient solutions for regularly mapping the rice crop is using Earth observation satellites. Moreover, the increasing availability of open access satellite images such as Landsat-8, Sentinel-1, and Sentinel-2 provides ample opportunities to map continuous and high-resolution rice growth stages with greater accuracy. The majority of the literature has focused on mapping rice area, cropping patterns and relied mainly on the phenology of vegetation. However, the mapping process of RGS was difficult to assess the accuracy, time-consuming, and depended on only one sensor. In this work, we discuss the use of machine learning algorithms (MLA) for mapping paddy RGS with multiple remote sensing data in near-real-time. The study area was Java Island, which is the primary rice producer in Indonesia. This study has investigated: (1) the mapping of RGS using Landsat-8 imagery and different MLAs, and their rigorous performance was evaluated by conducting a multitemporal analysis; (2) the temporal consistency of predicting RGS using Sentinel-2, MOD13Q1, and Sentinel-1 data; (3) evaluating the correlation of local statistics data and paddy RGS using Sentinel-2, PROBA-V, and Sentinel-1 with MLAs. The ground truth datasets were collected from multi-year web camera data (2014-2016) and three months of the field campaign in different regions of Java (2018). The study considered the RGS in the analysis to be vegetative, reproductive, ripening, bare land, and flooding, and MLAs such as support vector machines (SVMs), random forest (RF), and artificial neural network (ANN) were used. The temporal consistency matrix was used to compare the classification maps within three sensor datasets (Landsat-8 OLI, Sentinel-2, and Sentinel-2, MOD13Q1, Sentinel-1) and in four periods (5, 10, 15, 16 days). Moreover, the result of the RGS map was also compared with monthly data from local statistics within each sub-district using cross-correlation analysis. The result from the analysis shows that SVM with a radial base function outperformed the RF and ANN and proved to be a robust method for small-size datasets (< 1,000 points). Compared to Sentinel-2, Landsat-8 OLI gives less accuracy due to the lack of a red-edge band and larger pixel size (30 x 30 m). Integration of Sentinel-2, MOD13Q1, and Sentinel-1 improved the classification performance and increased the temporal availability of cloud-free maps. The integration of PROBA-V and Sentinel-1 improved the classification accuracy from the Landsat-8 result, consistent with the monthly rice planting area statistics at the sub-district level. The western area of Java has the highest accuracy and consistency since the cropping pattern only relied on rice cultivation. In contrast, less accuracy was noticed in the eastern area because of upland rice cultivation due to limited irrigation facilities and mixed cropping. In addition, the cultivation of shallots to the north of Nganjuk Regency interferes with the model predictions because the cultivation of shallots resembles the vegetative phase due to the water banks. One future research idea is the auto-detection of the cropping index in the complex landscape to be able to use it for mapping RGS on a global scale. Detection of the rice area and RGS using Google Earth Engine (GEE) can be an action plan to disseminate the information quickly on a planetary scale. Our results show that the multitemporal Sentinel-1 combined with RF can detect rice areas with high accuracy (>91%). Similarly, accurate RGS maps can be detected by integrating multiple remote sensing (Sentinel-2, Landsat-8 OLI, and MOD13Q1) data with acceptable accuracy (76.4%), with high temporal frequency and lower cloud interference (every 16 days). Overall, this study shows that remote sensing combined with the machine learning methodology can deliver information on RGS in a timely fashion, which is easy to scale up and consistent both in time and space and matches the local statistics. This thesis is also in line with the existing rice monitoring projects such as Crop Monitor, Crop Watch, AMIS, and Sen4Agri to support disseminating information over a large area. To sum up, the proposed workflow and detailed map provide a more accurate method and information in near real-time for stakeholders, such as governmental agencies against the existing mapping method. This method can be introduced to provide accurate information to rice farmers promptly with sufficient inputs such as irrigation, seeds, and fertilisers for ensuring national food security from the shifting planting time due to climate change

Applications of satellite ‘hyper-sensing’ in Chinese agriculture:Challenges and opportunities

Ensuring adequate food supplies to a large and increasing population continues to be the key challenge for China. Given the increasing integration of China within global markets for agricultural products, this issue is of considerable significance for global food security. Over the last 50 years, China has increased the production of its staple crops mainly by increasing yield per unit land area. However, this has largely been achieved through inappropriate agricultural practices, which have caused environmental degradation, with deleterious consequences for future agricultural productivity. Hence, there is now a pressing need to intensify agriculture in China using practices that are environmentally and economically sustainable. Given the dynamic nature of crops over space and time, the use of remote sensing technology has proven to be a valuable asset providing end-users in many countries with information to guide sustainable agricultural practices. Recently, the field has experienced considerable technological advancements reflected in the availability of ‘hyper-sensing’ (high spectral, spatial and temporal) satellite imagery useful for monitoring, modelling and mapping of agricultural crops. However, there still remains a significant challenge in fully exploiting such technologies for addressing agricultural problems in China. This review paper evaluates the potential contributions of satellite ‘hyper-sensing’ to agriculture in China and identifies the opportunities and challenges for future work. We perform a critical evaluation of current capabilities in satellite ‘hyper-sensing’ in agriculture with an emphasis on Chinese sensors. Our analysis draws on a series of in-depth examples based on recent and on-going projects in China that are developing ‘hyper-sensing’ approaches for (i) measuring crop phenology parameters and predicting yields; (ii) specifying crop fertiliser requirements; (iii) optimising management responses to abiotic and biotic stress in crops; (iv) maximising yields while minimising water use in arid regions; (v) large-scale crop/cropland mapping; and (vi) management zone delineation. The paper concludes with a synthesis of these application areas in order to define the requirements for future research, technological innovation and knowledge exchange in order to deliver yield sustainability in China

High-throughput estimation of crop traits: A review of ground and aerial phenotyping platforms

Crop yields need to be improved in a sustainable manner to meet the expected worldwide increase in population over the coming decades as well as the effects of anticipated climate change. Recently, genomics-assisted breeding has become a popular approach to food security; in this regard, the crop breeding community must better link the relationships between the phenotype and the genotype. While high-throughput genotyping is feasible at a low cost, highthroughput crop phenotyping methods and data analytical capacities need to be improved. High-throughput phenotyping offers a powerful way to assess particular phenotypes in large-scale experiments, using high-tech sensors, advanced robotics, and imageprocessing systems to monitor and quantify plants in breeding nurseries and field experiments at multiple scales. In addition, new bioinformatics platforms are able to embrace large-scale, multidimensional phenotypic datasets. Through the combined analysis of phenotyping and genotyping data, environmental responses and gene functions can now be dissected at unprecedented resolution. This will aid in finding solutions to currently limited and incremental improvements in crop yields

Estimating rice production in the Mekong Delta, Vietnam, utilizing time series of Sentinel-1 SAR data

Rice is the most important food crop in Asia and rice exports can significantly contribute to a country's GDP. Vietnam is the third largest exporter and fifth largest producer of rice, the majority of which is grown in the Mekong Delta. The cultivation of rice plants is important, not only in the context of food security, but also contributes to greenhouse gas emissions, provides man-made wetlands as an ecosystem, sustains smallholders in Asia and influences water resource planning and run-off water management. Rice growth can be monitored with Synthetic Aperture Radar (SAR) time series due to the agronomic flooding followed by rapid biomass increase affecting the backscatter signal. With the advent of Sentinel-1 a wealth of free and open SAR data is available to monitor rice on regional or larger scales and limited data availability should not be an issue from 2015 onwards. We used Sentinel-1 SAR time series to estimate rice production in the Mekong Delta, Vietnam, for three rice seasons centered on the year 2015. Rice production for each growing season was estimated by first classifying paddy rice area using superpixel segmentation and a phenology based decision tree, followed by yield estimation using random forest regression models trained on in situ yield data collected by surveying 357 rice farms. The estimated rice production for the three rice growing seasons 2015 correlates well with data at the district level collected from the province statistics offices with R2s of 0.93 for the Winter–Spring, 0.86 for the Summer–Autumn and 0.87 for the Autumn–Winter season

Remote sensing applications: an overview

Remote Sensing (RS) refers to the science of identification of earth surface features and estimation of their geo-biophysical properties using electromagnetic radiation as a medium of interaction. Spectral, spatial, temporal and polarization signatures are major characteristics of the sensor/target, which facilitate target discrimination. Earth surface data as seen by the sensors in different wavelengths (reflected, scattered and/or emitted) is radiometrically and geometrically corrected before extraction of spectral information. RS data, with its ability for a synoptic view, repetitive coverage with calibrated sensors to detect changes, observations at different resolutions, provides a better alternative for natural resources management as compared to traditional methods. Indian Earth Observation (EO) programme has been applications-driven and national development has been its prime motivation. From Bhaskara to Cartosat, India's EO capability has increased manifold. Improvements are not only in spatial, spectral, temporal and radiometric resolutions, but also in their coverage and value-added products. Some of the major operational application themes, in which India has extensively used remote sensing data are agriculture, forestry, water resources, land use, urban sprawl, geology, environment, coastal zone, marine resources, snow and glacier, disaster monitoring and mitigation, infrastructure development, etc. The paper reviews RS techniques and applications carried out using both optical and microwave sensors. It also analyses the gap areas and discusses the future perspectives

Mapping and modeling groundnut growth and productivity in rainfed areas of Tamil Nadu

A research study was conducted at Tamil Nadu Agricultural University, Coimbatore during kharif and rabi 2015 to estimate groundnut area, model growth and productivity and assess the vulnerability of groundnut to drought using remote sensing techniques. Multi temporal Sentinel 1A satellite data at VV and VH polarization with 20 m spatial resolution was acquired from May, 2015 to January, 2016 at 12 days interval and processed using MAPscape-RICE software. Continuous monitoring was done for ground truth on crop parameters in twenty monitoring sites and validation exercise was done for accuracy assessment. Input files on soil, weather and management practices were generated and crop coefficients pertaining to varieties were developed to assess growth and productivity of groundnut using DSSAT CROPGRO-Peanut model. Outputs from remote sensing and DSSAT model were assimilated to generate LAI thereby groundnut yield spatially and validated against observed yields. Being a rainfed crop, vulnerability of groundnut to drought was assessed integrating different meteorological and spectral indices viz., Standardized Precipitation Index (SPI), Normalized Difference Vegetation Index (NDVI) and Water Requirement Satisfaction Index (WRSI).Spectral dB curve of groundnut was generated using temporal multi date Sentinel 1A data. A detailed analysis of temporal signatures of groundnut showed a minimum at sowing and a peak at pod development stage and decreasing thereafter towards maturity. Groundnut crop expressed a significant temporal behaviour and large dynamic range (-11.74 to -5.31 in VV polarization and -20.04 to -13.05 in VH polarization) during its growth period. Groundnut area map was generated using maximum likelihood classifier integrating multi temporal features with a classification accuracy of 87.2 per cent and a kappa score of 0.74. The total classified groundnut area in the study districts was 88023 ha covering 17817 and 22582 ha in Salem and Namakkal districts during kharif 2015 while Villupuram and Tiruvannamalai districts accounted for 22722 and 24903 ha respectively during rabi 2015. Blockwise statistics on groundnut area during both seasons were also generated. To model growth and productivity of groundnut in DSSAT, weather and soil input files were generated using weatherman and ‘S’ build respectively besides deriving genetic coefficients for CO 6, TMV 7 and VRI 2 varieties of groundnut. Growth and development variables of groundnut were simulated using CROPGROPeanut model i.e., days to emergence (7-9 days) and anthesis (25-32 days), canopy height (63 to 70 cm), maximum LAI (1.12 to 3.07) and biomass (4176 to 9576 kg ha-1 across twenty monitoring locations spatially. The resultant pod yield was simulated to be 1796 to 3060 kg ha-1 with a harvest index of 0.28 to 0.43. On comparison of LAI between observed (2.01 to 4.05) and simulated values (1.12 to 3.07) the CROPGRO-Peanut model was found to under estimate the values with R2, RMSE and NRMSE of 0.82, 1.10 and 34 per cent. However, the model predicted the biomass of groundnut with an agreement of 89 per cent through the simulated values of 4176 to9576 kg ha-1 as against the observed biomass to 4620 to 9959 kg ha-1. The simulated pod yields of groundnut in the study area were 1796 to 3060 kg ha-1 as compared to the observed yields of 2115 to 2750 kg ha-1. The overall agreement between simulated and observed yields was 84 per cent with the average errors of 0.81, 342 kg ha-1 and 16 percent for R2, RMSE and NRMSE respectively. LAI values of groundnut, generated spatially through suitable regression models using dB from satellite images and LAI from DSSAT, ranged from 1.31 to 3.23 with R2, RMSE and NRMSE of 0.86, 0.78 and 24 per cent respectively on comparison with observed values. Remote sensing based spatial estimation resulted in groundnut pod yields of 1570 to 3102 kg ha-1 across the study districts of Salem, Namakkal, Tiruvannamalai and Villupuram. In the 20 monitoring locations, the pod yields were estimated to be 1912 to 2975 kg ha-1 as against the observed pod yields of 1450 to 2750 kg ha-1 with a fairly good agreement of 80 per cent. The vulnerability of groundnut was assessed using different drought indices viz., SPI, NDVI and WRSI. Considering SPI, out of the total groundnut area of 88023 ha, an area of 86607 ha was found to be under near normal condition based on deviation of rainfall received during cropping season from historical precipitation. Similarly NDVI, an indicator of vegetation condition during the cropping season, showed that 14272 ha of groundnut area were under stressed condition during 2015. An area of 40981 ha in Villupuram and Tiruvannamalai districts was found to be under chances of crop failure based on Water Requirement Satisfaction index (WRSI). Major groundnut areas of Salem district (14188 ha) was under medium risk zone. Considering overall vulnerability, whole district of Villupuram was adjudged as highly vulnerable to drought with regard to groundnut cultivation whereas four blocks of Salem, eight blocks of Namakkal and all the blocks of Tiruvannamalai were found to be moderately vulnerable to drought

Spring wheat (Triticum aestivum L.) ideotype responses to elevated CO2 and temperature levels : A cereal yield modeling study using satellite information

The wheat (Triticum aestivum L.) ideotype concept is defined as the optimal wheat genotype with a maximum potential for grain yield under optimal growing conditions. The ideotype concept has been widely reviewed in agronomy research for a variety of crops. The wheat ideotype with optimum yielding capacity and with adaptation to elevated atmospheric CO2 concentrations should have rapid canopy closure at the tillering stage and a long grain-filling period, with high temperature sum requirements from anthesis to maturity. The CERES-Wheat modeling results using the non-limited Open Top Chamber (OTC) data (1992-1994) indicated, when using the CERES-Wheat potential, non-limiting model, that the simulated grain yield of high-latitude cv. Polkka increased under elevated CO2 conditions (700 ppm) to 142 % and to 161 % for the mid-European cv. Nandu, as compared with the reference level (ypot, 100%). The corresponding observed average 1992-1994 increase in OTC experiments was lower (112 % cv. Polkka). The elevated temperature (+ 3 °C) accelerated phenological development, especially during the generative phase, according to the CERES-Wheat model estimations. The yield of cv. Polkka decreased on average to 80.4 % (59 % cv. Nandu, vs. 84 % OTC observed) due to temperature increase from the simulated reference level (ypot, 100%). When modeling the elevated temperature and CO2 interaction, the increase in grain yield under elevated CO2 was reduced by the elevated temperature, accelerating phenological development, especially during the generative phase, resulting in a shorter grain-filling period. The combined CO2 and temperature effect increased cv. Polkka grain yield to 106 % (107 % for cv. Nandu) under non-limited growing conditions (vs. 102 % OTC observed) as compared with the simulated reference level (ypot,100 %). The modeling results from the CERES-Wheat crop model, ideotype and cultivation value models imply that with new high yielding mid-European ideotypes, the nonpotential baseline yield (yb) would be on average 5150 kg ha-1 (+ 108 %) vs. new highlatitude ideotypes (yb 4770 kg ha-1, 100%) grown under the elevated CO2(700ppm)×temperature(+3ºC) growing conditions projected for the year 2100 FINSKEN climate change scenario for southern Finland, with elevated CO2 (733 ppm) and temperature (+4.4 °C) levels. The Ideotype, Cultivation value, Mixed structural covariance, Path and yield component analysis results emphasized that especially grains/ear, harvest index (HI) and maximum 1000 kernel weight were significant factors defining the highest yield potential for high-latitude and mid-European spring wheat genotypes. In addition, the roles of flag leaf area and dry weight, especially during the generative phase after heading, were important factors defining the final grain yield potential for new highyielding wheat ideotypes. The 1989-2004 averaged cereal yield modeling results using optical and microwave satellite data from southern Finland with Vegetation Indices (VGI) and Composite Multispectral (CMM) models, suggest a non-potential baseline yield level (yb, kg ha-1) of 3950 kg ha-1 (R2 0.630, RMSE 9.1 %) for spring cereals (including spring wheat, barley (Hordeum vulgare L.), and oats (Avena sativa L.) cultivars), 4330 kg ha-1 (R2 0.630, RMSE 6.7 %) for winter cereals (winter wheat and rye (Secale cereale L.) cultivars) and 4240 kg ha-1 (R2 0.764, RMSE 6.6 %) for spring wheat cultivars grown in actual field conditions on farms in southern Finland. The modeled VGI and CMM yield estimates (yb) were compared with corresponding measured averaged yields in the 6 experimental areas in the Etelä-Pohjanmaa, Nylands Svenska and Häme Agricultural Advisory and Rural Development Centres (Growing zones I-III) in southern Finland. The combined modeling results from this study suggest that the 5 t ha-1 yield barrier will be surpassed with new high yielding mid-European and high-latitude optimal ideotypes introduced into cultivation after the 1990s, when also taking into account the elevated atmospheric CO2 and temperature effects, thereby increasing the average spring wheat non-potential yield levels by 1-6 % of high-latitude ideotypes (4-13 % for mid-European ideotypes) by 2100 in southern Finland. The extrapolation modeling results, combined with earlier sowing and elevated atmospheric CO2 (700 ppm) and temperature (+3 ºC) effects, suggest an average net increase of 30 million kg annually in spring wheat total production in Finland by 2100 using new high-latitude wheat ideotypes (60 million kg with new mid-European ideotypes) and assuming no changes in wheat cultivated area and land use. Currently the averaged annual spring wheat total production is on the 600 million kg level in Finland, varying significantly between years with changes in wheat total cultivation area in Finland.Tutkimuksessa arvioitiin kasvumalleilla nykyisten ja uusien kevätvehnägenotyyppien (alkuperä Keski-Eurooppa/Skandinavia) sekä potentiaalista, ympäristö ja kasvutekijöiden rajoittamatonta (ypot, kg ha-1) että non-potentiaalista (yb,kg ha-1) sadontuottokykyä tulevaisuuden kohonneiden ilmakehän CO2 ja keskilämpötilojen kasvuolosuhteissa Etelä-Suomessa vuosien 2000-2100 aikavälillä. Lisäksi tutkimuksessa arvioitiin mahdollisuuksia arvioida kevätviljojen ja erityisesti kevätvehnän non-potentiaalista satotasoa laajoilla viljelyalueilla Etelä-Suomen ja Pohjanmaan maaseutukeskuksissa (Etelä-Suomen ja Pohjanmaan kasvuvyöhykkeet I-IV) hyödyntäen sekä optisten että mikroaaltotaajuus kaukokartoitussatelliittien mittausdataaa (1998-2004). CERES wheat -kasvumallilla simuloitiin kohotettujen CO2:n (700 ppm, parts per million) ja lämpötilan(+3 °C) vaikutuksia kevätvehnälajike Polkan (Triticum aestivum L.) fenologiseen kehitykseen sekä biomassa- ja sadontuottomahdollisuuksiin optimaalisissa kasvuoloissa (potentiaalinen kasvumalli). Toinen simulointi suoritettiin kasvukauden aikaisten stressitekijöiden (sää, kuivuus, sadanta ja typpilannoitus) vaikuttaessa fenologiseen kehitykseen ja sadontuotantoon (non-potentiaalinen kasvumalli). Suomen ilmastonmuutos -tutkimusohjelman (SILMU) skenaarioiden mukaan Suomen kasvuolosuhteet tulevat muistuttamaan v. 2100 olosuhteita,jotka vallitsevat tällä hetkellä Tanskassa ja Pohjois-Saksassa. Tällöin keskilämpötila on kohonnut 3 °C ja ilmakehän CO2-taso kaksinkertaistunut vuoden 1990 keskimääräisestä 350 ppm tasota 700 ppm tasoon. CERES wheat -kasvumallituksen tulokset indikoivat kaksinkertaisen CO2-tason kohottavan Polkka lajikkeen satoa 142 % potentiaalisella mallilla (167 % non-potentiaalisella) laskettuna nykyisestä referenssitasosta (100 %, ambientti lämpötila, CO2 350 ppm). Kohotettu lämpötila (+3 °C) pienensi Polkan satoa 80,4 %:iin referenssitasosta (100 %, 6,16 t ha -1) potentiaalisella mallilla (76,8 % non-potentiaalisella mallilla referenssitasosta 4,49 t ha -1). Kohotettu lämpötila lyhensi kasvin kasvuaikaa kiihdyttämällä kasvua vegetatiivisessa ja generatiivisessa vaiheessa. Kasvuajan lyhentyminen puolestaan alensi Polkka kevätvehnän satoa. Simuloitaessa kohotettujen CO2-tason ja lämpötilan yhteisvaikutusta Polkan satoon, kiihdytti kohotettu CO2-taso vegetatiivisessa vaiheessa biomassan muodostumista ja generatiivisessa vaiheessa sadonmuodostusta. Toisaalta kohotettu lämpötila lyhensi kasvin generatiivista vaihetta ja pienensi CO2:n satoa kohottavaa vaikutusta. Tällöin kohotettu lämpötila aiheutti tähkän täystuleentumisen aikaisemmin ja sato jäi alhaisemmaksi (106 % potentiaalinen malli, 122 % non-potentiaalinen malli laskettuna ambientista lämpötila ja CO2 tasosta). Tulokset olivat samansuuntaiset Maatalouden tutkimuskeskuksessa v. 1992-1994 Polkka kevätvehnällä tehtyjen open top-kasvukammio (OTC) kokeiden kanssa (Hakala 1998). Ideotyyppi, Cultivation value, Mixed structural covariance, Path ja yield component mallitustulokset indikoivat jyvien lukumäärä/tähkä, satoindeksi (HI), ja 1000 siemenen paino olevan merkittäviä satokomponentteja, jotka vaikuttivat merkittävästi uusien (alkuperä Keski-Eurooppa/Skandinavia) korkea satoisten kevätvehnägenotyyppien loppusatoon. Vuosina 1989-2004 Etelä-Suomessa ja Pohjanmaalla suoritetuissa kenttäkokeissa verrattiin sekä optisten että mikroaaltosatelliittien antaman reflektanssi ja takaisinsironta informaation ennustavuutta kevätviljojen loppusatojen arvioinnissa käyttäen VGI (Vegetation Indices) ja CMM (Composite Multispectral Model) malleja.VGI ja CMM mallien estimoima non-potentiaalinen keskisatoestimaatti (yb) oli kevätviljoille (kevätvehnä, ohra, kaura) 3950 kg ha-1 (R2 0.630, RMSE 9.1 %) ja 4240 kg ha-1 (R2 0.764, RMSE 6.6 %) kevätvehnälle. VGI ja CMM mallien satoestimaatteja verrattiin Maa ja Metsätalousministeriön vuosittaisiin kevätviljojen keskimääräisiin inventaario-satotasoihin maaseutukeskuksissa sekä vastaaviin MTT maa- ja elintarviketalouden tutkimuskeskuksen virallisten lajikekokeiden satotuloksiin Etelä-Suomen ja Pohjanmaan kasvuvyöhykkeillä I-IV. Tutkimuksen yhteenvetona eri osajulkaisuista voidaan päätellä, että uusien korkeasatoisten kevätvehnien (alkuperä Keski-Eurooppa/Skandinavia, viljelykseen otto 1990 jälkeen) non-potentiaalinen keskimääräinen satotaso (yb) ylittää 5 t ha-1 tason 2050-2100 aikavälillä Etelä-Suomessa kun huomioidaan keskilämpötilan ja ilmakehän CO2 pitoisuuden kohoaminen yhdessä viljelytekniikan ja kasvinjalostuken keskimääräistä satotasoa kohottavat tekijät. Tutkimuksen perusteella arvioitiin uusien korkeasatoisten Skandinaavisten kevätvehnälajikkeiden lisäävän keskimäärin 30 miljoona kg/vuosi (+ 60 miljoonaa kg/vuosi käyttäen uusia korkeasatoisia Keski-Eurooppalaisia lajikkeita) Suomen kansallista kevätvehnän kokonaissatoa 2050-2100 aikavälillä yhdessä kohotetun CO2 ja lämpötilatason kanssa laskettuna Suomen keskimääräisestä 600 miljoonan kg/vuosi tuotantotasosta. Vuotuinen kevätvehnän kokonaistuotantotaso vaihtelee kuitenkin voimakkaasti vuosien välillä riippuen voimakkaast kevätvehnän kokoviljelyalasta Suomessa

ALOS-2/PALSAR-2 Calibration, Validation, Science and Applications

Twelve edited original papers on the latest and state-of-art results of topics ranging from calibration, validation, and science to a wide range of applications using ALOS-2/PALSAR-2. We hope you will find them useful for your future research

Artificial Neural Networks in Agriculture

Modern agriculture needs to have high production efficiency combined with a high quality of obtained products. This applies to both crop and livestock production. To meet these requirements, advanced methods of data analysis are more and more frequently used, including those derived from artificial intelligence methods. Artificial neural networks (ANNs) are one of the most popular tools of this kind. They are widely used in solving various classification and prediction tasks, for some time also in the broadly defined field of agriculture. They can form part of precision farming and decision support systems. Artificial neural networks can replace the classical methods of modelling many issues, and are one of the main alternatives to classical mathematical models. The spectrum of applications of artificial neural networks is very wide. For a long time now, researchers from all over the world have been using these tools to support agricultural production, making it more efficient and providing the highest-quality products possible