The application of ground-based and satellite remote sensing for estimation of bio-physiological parameters of wheat grown under different water regimes

Remote sensing technologies have been widely studied for the estimation of crop biometric and physiological parameters. The number of sensors and data acquisition methods have been increasing, and their evaluation is becoming a necessity. The aim of this study was to assess the performance of two remote sensing data for describing the variations of biometric and physiological parameters of durum wheat grown under different water regimes (rainfed, 50% and 100% of irrigation requirements). The experimentation was carried out in Policoro (Southern Italy) for two growing seasons. The Landsat 8 and Sentinel-2 images and radiometric ground-based data were acquired regularly during the growing season with plant biometric (leaf area index and dry aboveground biomass) and physiological (stomatal conductance, net assimilation, and transpiration rate) parameters. Water deficit index was closely related to plant water status and crop physiological parameters. The enhanced vegetation index showed slightly better performance than the normalized difference vegetation index when plotted against the leaf area index with R2 = 0.73. The overall results indicated that the ground-based vegetation indices were in good agreement with the satellite-based indices. The main constraint for effective application of satellite-based indices remains the presence of clouds during the acquisition time, which is particularly relevant for winter-spring crops. Therefore, the integration of remote sensing and field data might be needed to optimize plant response under specific growing conditions and to enhance agricultural production

Multiscale Assimilation of Sentinel and Landsat Data for Soil Moisture and Leaf Area Index Predictions Using an Ensemble-Kalman-Filter-Based Assimilation Approach in a Heterogeneous Ecosystem

Data assimilation techniques allow researchers to optimally merge remote sensing observations in ecohydrological models, guiding them for improving land surface fluxes predictions. Presently, freely available remote sensing products, such as those of Sentinel 1 radar, Landsat 8 sensors, and Sentinel 2 sensors, allow the monitoring of land surface variables (e.g., radar backscatter for soil moisture and the normalized difference vegetation index (NDVI) and for leaf area index (LAI)) at unprecedentedly high spatial and time resolutions, appropriate for heterogeneous ecosystems, typical of semiarid ecosystems characterized by contrasting vegetation components (grass and trees) competing for water use. A multiscale assimilation approach that assimilates radar backscatter and grass and tree NDVI in a coupled vegetation dynamic-land surface model is proposed. It is based on the ensemble Kalman filter (EnKF), and it is not limited to assimilating remote sensing data for model predictions, but it uses assimilated data for dynamically updating key model parameters (the ENKFdc approach), including saturated hydraulic conductivity and grass and tree maintenance respiration coefficients, which are highly sensitive parameters of soil-water balance and biomass budget models, respectively. The proposed EnKFdc assimilation approach facilitated good predictions of soil moisture, grass, and tree LAI in a heterogeneous ecosystem in Sardinia for a 3-year period with contrasting hydrometeorological (dry vs. wet) conditions. Contrary to the EnKF-based approach, the proposed EnKFdc approach performed well for the full range of hydrometeorological conditions and parameters, even assuming extremely biased model conditions with very high or low parameter values compared with the calibrated ("true") values. The EnKFdc approach is crucial for soil moisture and LAI predictions in winter and spring, key seasons for water resources management in Mediterranean water-limited ecosystems. The use of ENKFdc also enabled us to predict evapotranspiration and carbon flux well, with errors of less than 4% and 15%, respectively; such results were obtained even with extremely biased initial model conditions

Using a Remote Sensing-Supported Hydro-Agroecological Model for Field-Scale Simulation of Heterogeneous Crop Growth and Yield: Application for Wheat in Central Europe

The challenge of converting global agricultural food, fiber and energy crop cultivation into an ecologically and economically sustainable production process requires the most efficient agricultural management strategies. Development, control and maintenance of these strategies are highly dependent on temporally and spatially continuous information on crop status at the field scale. This paper introduces the application of a process-based, coupled hydro-agroecological model (PROMET) for the simulation of temporally and spatially dynamic crop growth on agriculturally managed fields. By assimilating optical remote sensing data into the model, the simulation of spatial crop dynamics is improved to a point where site-specific farming measures can be supported. Radiative transfer modeling (SLC) is used to provide maps of leaf area index from Earth Observation (EO). These maps are used in an assimilation scheme that selects closest matches between EO and PROMET ensemble runs. Validation is provided for winter wheat (years 2004, 2010 and 2011). Field samples validate the temporal dynamics of the simulations (avg. R-2 = 0.93) and > 700 ha of calibrated combine harvester data are used for accuracy assessment of the spatial yield simulations (avg. RMSE = 1.15 t center dot ha(-1)). The study shows that precise simulation of field-scale crop growth and yield is possible, if spatial remotely sensed information is combined with temporal dynamics provided by land surface process models. The presented methodology represents a technical solution to make the best possible use of the growing stream of EO data in the context of sustainable land surface management

Using a Remote Sensing-Supported Hydro-Agroecological Model for Field-Scale Simulation of Heterogeneous Crop Growth and Yield: Application for Wheat in Central Europe

The challenge of converting global agricultural food, fiber and energy crop cultivation into an ecologically and economically sustainable production process requires the most efficient agricultural management strategies. Development, control and maintenance of these strategies are highly dependent on temporally and spatially continuous information on crop status at the field scale. This paper introduces the application of a process-based, coupled hydro-agroecological model (PROMET) for the simulation of temporally and spatially dynamic crop growth on agriculturally managed fields. By assimilating optical remote sensing data into the model, the simulation of spatial crop dynamics is improved to a point where site-specific farming measures can be supported. Radiative transfer modeling (SLC) is used to provide maps of leaf area index from Earth Observation (EO). These maps are used in an assimilation scheme that selects closest matches between EO and PROMET ensemble runs. Validation is provided for winter wheat (years 2004, 2010 and 2011). Field samples validate the temporal dynamics of the simulations (avg. R-2 = 0.93) and > 700 ha of calibrated combine harvester data are used for accuracy assessment of the spatial yield simulations (avg. RMSE = 1.15 t center dot ha(-1)). The study shows that precise simulation of field-scale crop growth and yield is possible, if spatial remotely sensed information is combined with temporal dynamics provided by land surface process models. The presented methodology represents a technical solution to make the best possible use of the growing stream of EO data in the context of sustainable land surface management

Effect of bioenergy crops and fast growing trees on hydrology and water quality in the Little Vermilion River Watershed

Energy security and sustainability require a suite of biomass crops, including woody species. Short rotation woody crops (SRWCs) such as Populus have great potential as biofuel feedstocks. Quantifying biomass yields of bioenergy crop and hydrologic and water quality responses to growth is important should it be widely planted in the Midwestern U.S. Subsurface tile drainage systems enable the Midwest area to become highly productive agricultural lands, but also create environmental problems like nitrate-N contamination of the water it drains. The Soil and Water Assessment Tool (SWAT) has been used to model watersheds with tile drainage, but the new tile drainage routine in SWAT2012 has not been fully tested. The objectives of this study were to develop algorithms and growth parameters of Populus in Agricultural Land Management Alternative with Numerical Assessment Criteria (ALMANAC) and SWAT models, compare performance of tile drainage routines in SWAT2009 and SWAT2012 in simulating tile drainage, and simulate biomass yields of bioenergy crops and the impacts of their impacts on water quantity and quality for a typical tile-drained watershed in the Midwest USA. The functional components and parameters of hybrid poplar Tristis #1 (Populus balsamifera L. × P.tristis Fisch) and eastern cottonwood (Populus deltoides Bartr.) were determined, and related algorithms improved in ALMANAC and SWAT based on improved simulation of leaf area, plant biomass and biomass partitioning. Long-term (1991-2003) field site and river station data from the Little Vermilion River (LVR) watershed in Illinois were used to evaluate performance of tile drainage routines in SWAT2009 revision 528 (the old routine) and SWAT2012 revision 615 and 645 (the new routine). Calibrated monthly tile flow, surface flow, nitrate in tile and surface flow, sediment and annual corn and soybean yield results at field sites, and flow, sediment load and nitrate load at the river station for the old and new tile drainage routines were compared with observed values. Crop residue from corn stover, perennial grasses, switchgrass and Miscanthus, and hybrid poplar trees were considered as potential bioenergy crops for the LVR watershed. SWAT2012 (Revision 615) with the new tile drainage routine (DRAINMOD routine) and improved perennial grass and tree growth simulation was used to model long-term annual biomass yields, flow, tile flow, sediment load, total nitrogen, nitrate load in flow, nitrate in tile flow, soluble nitrogen, organic nitrogen, total phosphorus, mineral phosphorus and organic phosphorus under various bioenergy scenarios in the LVR watershed. Simulated results from different bioenergy crop scenarios were compared with those from the baseline. Tree growth calibration and validation results showed that improved algorithms of leaf area index (LAI) and biomass simulation and suggested values and potential parameter range for hybrid poplar Tristis #1 and Eastern cottonwood ( Populus deltoides Bartr.) were reasonable, and performance of the modified ALMANAC in simulating LAI, aboveground biomass and root biomass of Populus was good. Performance of the modified SWAT simulated hybrid poplar LAI and aboveground woody biomass (PBIAS: -57 ~ 7%, NSE: 0.94 ~ 0.99, and R2: 0.74 ~ 0.99), and cottonwood aboveground biomass, seasonal mean runoff, mean sediment, mean nitrate-N and total nitrate-N were satisfactory (PBIAS: -39 ~ 11%, NSE: 0.86 ~ 0.99, and R2: 0.93 ~ 0.99). Additionally, tile drainage calibration and validation results indicated that the new routine provides acceptable simulated tile flow (NSE = 0.50 ~ 0.68), and nitrate in tile flow (NSE = 0.50 ~ 0.77) for field sites, while the old routine simulated tile flow (NSE = -0.77~ -0.20) and nitrate in tile flow (NSE = -0.99 ~ 0.21) for the field site with constant tile spacing were unacceptable. The new modified curve number calculation method in revision 645 (NSE = 0.56 ~ 0.82) better simulated surface runoff than revision 615 (NSE = -5.95 ~ 0.5). Bioenergy crop simulation results showed that 38% corn stover removal (66,439 Mg/yr) with combination of Miscanthus both on highly erodible areas and marginal land (19,039 Mg/yr) provided the highest biofeedstock production. Flow, tile flow, erosion and nutrient losses were slightly reduced under bioenergy crop scenarios of Miscanthus, switchgrass, and hybrid poplar on highly erodible areas, marginal land and marginal land with forest. The increase in sediment load and nutrient losses resulting from corn stover removal could be offset under scenarios with various combinations of bioenergy crops. Corn stover removal with bioenergy crops both on highly erodible areas and marginal land could provide more biofuel production relative to the baseline, and was beneficial to hydrology and water quality at the watershed scale. The modified ALMANAC and SWAT can be used for biofeedstock production modeling for Populus. The modified SWAT model can be used for Populus biofeedstock production modeling and hydrologic and water quality response to its growth. The improved algorithms of LAI and biomass simulation for tree growth should also be useful for other process based models, such as SWAT, EPIC and APEX. Tile drainage calibration and validation results provided reasonable parameter sets for the old and new tile drainage routines to accurately simulate hydrologic processes in mildly-sloped watersheds. Bioenergy crop simulation results provided guidance for further research on evaluation of bioenergy crop scenarios in a typical extensively tile-drained watershed in the Midwestern US

Assessing Agave sisalana biomass from leaf to plantation level using field measurements and multispectral satellite imagery

Biomassa, eli kasviaineksen määrä, on tärkeä muuttuja viljelykasvien kasvun seurannassa sekä arvioitaessa hiilen kiertoa. Kenttätöissä biomassaa voidaan arvioida kasveja vahingoittamatta hyödyntämällä allometrisia malleja. Suuremmassa mittakaavassa biomassaa voidaan kartoittaa kaukokartoitusmenetelmillä. Tässä tutkimuksessa arvioitiin Agave sisalanan eli sisalin lehtien kuivaa biomassaa. Sisal on trooppisilla ja subtrooppisilla alueilla viljeltävä monivuotinen kasvi, jonka lehdistä tuotetaan kuitua ja biopolttoainetta. Lehtibiomassan arvioimiseksi luotiin ensin allometrinen malli, minkä jälkeen biomassa mallinnettiin 8851 hehtaarin plantaasille Kaakkois-Keniassa käyttämällä Sentinel-2 multispektraalista satellittikuva-aineistoa. Allometrista mallia varten kerättiin 38:n lehden otos. Kasvin korkeuden ja lehden suurimman ympärysmitan avulla muodostettiin tilavuusarvio, jonka yhteyttä biomassaan mallinnettiin lineaarisella regressiolla. Muuttujien välille löytyi vahva log-log lineaarinen yhteys ja ristiinvalidointi osoitti, että mallin ennusteet ovat tarkkoja (R2 = 0.96, RMSE = 7.69g). Mallin avulla ennustettiin lehtibiomassa 58:lle koealalle, jotka muodostivat otoksen biomassan mallinnukseen Sentinel-2 kuvalla. Mallinnuksessa käytettiin yleistettyjä additiivisia malleja, joiden avulla tutkittiin lukuisten spektraalisten kasvillisuusindeksien yhteyttä biomassaan. Parhaaksi osoittautuivat indeksit, jotka laskettiin hyödyntämällä vihreää ja lähi-infrapunakanavaa, sekä ns. ”red-edge”-kanavia (D2 = 74%, RMSE = 4.96 Mg/ha). Keskeisin mallin selitysastetta heikentävä tekijä vaikutti olevan suuresti vaihteleva aluskasvillisuuden määrä. Hyödyntämällä parhaaksi todettua kasvillisuusindeksiä lehtibiomassa mallinnettiin koko plantaasin peltoalalle. Biomassa vaihteli 0 ja 45.1 Mg/ha välillä, keskiarvon ollessa 9.9 Mg/ha. Tämän tutkimuksen tuloksena syntyi allometrinen malli, jota voidaan käyttää sisalin lehtibiomassan arviointiin. Jatkotutkimuksissa tulisi ottaa huomioon myös kasvin muut osat, kuten varsi ja juuret. Biomassan mallinnus multispektraalisilla kasvillisuusindekseillä osoitti menetelmän toimivuuden sisalin biomassan kartoituksessa, mutta vaihtelevan aluskasvillisuuden todettiin heikentävän mallin suorituskykyä. Aluskasvillisuuden vaikutusta ja täydentäviä aineistolähteitä tulisi tutkia tulevaisuudessa. Plantaasin lehtibiomassan, ja näin ollen maanpäälle sitoutuneen hiilen määrä, on saman suuruinen, kuin alueen luonnollisella pensassavannilla. Sisal-plantaasin hiilen kierron kokonaisvaltainen ymmärtäminen vaatii kuitenkin lisätietoa kasvien ja maaperän hiilivuosta sekä maaperän hiilensitomisesta.Biomass is an important parameter for crop monitoring and management, as well as for assessing carbon cycle. In the field, allometric models can be used for non-destructive biomass assessment, whereas remote sensing is a convenient method for upscaling the biomass estimations over large areas. This study assessed the dry leaf biomass of Agave sisalana (sisal), a perennial crop whose leaves are grown for fibre and biofuel production in tropical and subtropical regions. First, an allometric model was developed for predicting the leaf biomass. Then, Sentinel-2 multispectral satellite imagery was used to model the leaf biomass at 8851 ha plantation in South-Eastern Kenya. For the allometric model 38 leaves were sampled and measured. Plant height and leaf maximum diameter were combined into a volume approximation and the relation to biomass was formalised with linear regression. A strong log-log linear relation was found and leave-one-out cross-validation for the model showed good prediction accuracy (R2 = 0.96, RMSE = 7.69g). The model was used to predict biomass for 58 field plots, which constituted a sample for modelling the biomass with Sentinel-2 data. Generalised additive models were then used to explore how well biomass was explained by various spectral vegetation indices (VIs). The highest performance (D2 = 74%, RMSE = 4.96 Mg/ha) was achieved with VIs based on the red-edge (R740 and R783), near-infrared (R865) and green (R560) spectral bands. Highly heterogeneous growing conditions, mainly variation in the understory vegetation seemed to be the main factor limiting the model performance. The best performing VI (R740/R783) was used to predict the biomass at plantation level. The leaf biomass ranged from 0 to 45.1 Mg/ha, with mean at 9.9 Mg/ha. This research resulted a newly established allometric equation that can be used as an accurate tool for predicting the leaf biomass of sisal. Further research is required to account for other parts of the plant, such as the stem and the roots. The biomass-VI modelling results showed that multispectral data is suitable for assessing sisal leaf biomass over large areas, but the heterogeneity of the understory vegetation limits the model performance. Future research should address this by investigating the background effects of understory and by looking into complementary data sources. The carbon stored in the leaf biomass at the plantation corresponds to that in the woody aboveground biomass of natural bushlands in the area. Future research is needed on soil carbon sequestration and soil and plant carbon fluxes, to fully understand the carbon cycle at sisal plantation

Application of Sentinel-2A data for pasture biomass monitoring using a physically based radiative transfer model

A large proportion of the global land surface is covered by pasture. The advent of the Sentinel satellites program provides free datasets with good spatiotemporal resolution that can be a valuable source of information for monitoring pasture resources. We combined optical remote sensing data (proximal hyperspectral and Sentinel 2A) with a radiative transfer model (PROSAIL) to estimate leaf area index (LAI), and biomass, in a dairy farming context. Three sites in Southern England were used: two pasture farms that differed in pasture type and management, and a set of small agronomy trial plots with different mixtures of grasses, legumes and herbs, as well as pure perennial ryegrass. The proximal and satellite spectral data were used to retrieve LAI via PROSAIL model inversion, which were compared against field observations of LAI. The potential of bands of Sentinel 2A that corresponded with a 10 m resolution was studied by convolving narrow spectral bands (from a handheld hyperspectral sensor) into Sentinel 2A bands (10 m). Retrieved LAI, using these spectrally resampled S2A data, compared well with measured LAI, for all sites, even for those with mixed species cover (although retrieved LAI was somewhat overestimated for pasture mixtures with high LAI). This proved the suitability of 10 m Sentinel 2A spectral bands for capturing LAI dynamics for different types of pastures. We also found that inclusion of 20 m bands in the inversion scheme did not lead to any further improvement in retrieved LAI. Sentinel 2A image based retrieval yielded good agreement with LAI measurements obtained for a typical perennial ryegrass based pasture farm. LAI retrieved in this way was used to create biomass maps (that correspond to indirect biomass measurements by Rising Plate Meter (RPM)), for mixed-species paddocks for a farm for which limited field data were available. These maps compared moderately well with farmer-collected RPM measurements for this farm. We propose that estimates of paddock-averaged and within-paddock variability of biomass are more reliably obtained from a combined Sentinel 2A-PROSAIL approach, rather than by manual RPM measurements. The physically based radiative transfer model inversion approach outperformed the Normalised Difference Vegetation Index based retrieval method, and does not require site specific calibrations of the inversion scheme

Growth simulation and yield prediction for perennial jujube fruit tree by integrating age into the WOFOST model

peer reviewedMathematical models have been widely employed for the simulation of growth dynamics of annual crops, thereby performing yield prediction, but not for fruit tree species such as jujube tree (Zizyphus jujuba). The objectives of this study were to investigate the potential use of a modified WOFOST model for predicting jujube yield by introducing tree age as a key parameter. The model was established using data collected from dedicated field experiments performed in 2016–2018. Simulated growth dynamics of dry weights of leaves, stems, fruits, total biomass and leaf area index (LAI) agreed well with measured values, showing root mean square error (RMSE) values of 0.143, 0.333, 0.366, 0.624 t ha−1 and 0.19, and R2 values of 0.947, 0.976, 0.985, 0.986 and 0.95, respectively. Simulated phenological development stages for emergence, anthesis and maturity were 2, 3 and 3 days earlier than the observed values, respectively. In addition, in order to predict the yields of trees with different ages, the weight of new organs (initial buds and roots) in each growing season was introduced as the initial total dry weight (TDWI), which was calculated as averaged, fitted and optimized values of trees with the same age. The results showed the evolution of the simulated LAI and yields profiled in response to the changes in TDWI. The modelling performance was significantly improved when it considered TDWI integrated with tree age, showing good global (R2≥0.856, RMSE≤0.68 t ha−1) and local accuracies (mean R2≥0.43, RMSE≤0.70 t ha−1). Furthermore, the optimized TDWI exhibited the highest precision, with globally validated R2 of 0.891 and RMSE of 0.591 t ha−1, and local mean R2 of 0.57 and RMSE of 0.66 t ha−1, respectively. The proposed model was not only verified with the confidence to accurately predict yields of jujube, but it can also provide a fundamental strategy for simulating the growth of other fruit trees