Landsat-based monitoring of seasonal and near real-time evapotranspiration (ET) in California vineyards is currently challenged by its low temporal revisit period and missing data under cloudy conditions. Gap-filling approaches, such as data fusion with high-temporal resolution images (e.g., MODIS) and interpolation of actual to potential ET ratio (ET/ETo) between image acquisition dates are now commonly used to overcome this challenge. However, these methods may not fully capture non-linear changes in crop condition due to scheduled irrigation, and other management decisions affecting ET during days when satellite images are unavailable and can lead to biased ET estimates. In this study, we combined Landsat-8 and Sentinel-2 data to develop a Shuttleworth-Wallace (SW) based near real-time ET modeling framework for mapping daily ET across three California Vineyard sites. In addition, we utilized daily Leaf area index (LAI) products derived from the Harmonized Landsat and Sentinel-2 (HLS) surface reflectance and MODIS LAI data products to constrain key resistance parameters in the SW model and tested the model across nine flux towers covering three vineyard sites in California. Results suggest that compared to the linear interpolation-based ET/ETo approach, this framework can help reduce biases and root mean squared error of estimated daily ET by over 10%. Results point to a potential utility of the combined Landsat-8 and Sentinel-2 based approach to monitor near real-time ET and complement ongoing thermal remote sensing-based ET modeling approaches to better characterize near real-time crop water status in California vineyards.
Efficient use of available water resources is key to sustainable viticulture management in California (CA) and other regions with limited water availability in the western US and abroad. This requires remote and frequent field-scale information on vineyard water status. Though the Sentinel-2 sensors offer good spatial (10-60m) and temporal (~5 days) coverages, their utility in monitoring vineyard evapotranspiration (ET) has not been considered viable primarily due to the lack of a thermal band. However, recently, a new spectral-based Shuttleworth Wallace (SW) ET model, which uses a contextual framework to determine dry and wet extremes from the Sentinel-2 (SW-S2) surface reflectance data, has shown promise when tested over a single GRAPEX (Grape Remote-sensing Atmospheric Profile and Evapotranspiration eXperiment) site in CA. However, current knowledge on its applicability across a climate gradient in CA with different topography, soils, trellis design and vine variety is lacking. Moreover, how the selection of modeling domain and meteorological forcing data influence model output is limited. Consequently, this presentation expands the evaluation of the SW-S2 model across multiple domains and meteorological inputs covering all three GRAPEX vineyard sites spanning a north to south climate gradient over three recent growing seasons (2018-2020). In comparison with flux tower observations, the size of the modeling domain and the source and quality of meteorological forcing data on the performance of the SW-S2 model as well as application to the three different vineyard study sites will be presented. Future research on merging output from more-frequent spectral and less-frequent thermal-based ET models to reduce latency in ET monitoring of California vineyards will also be discussed.
Estimating the fraction of photosynthetically active radiation absorbed by vegetation (i.e., fPAR) is crucial for quantifying the carbon uptake activity of terrestrial ecosystems. Satellite-derived vegetation index (i.e., NDVI) is the powerful indicator of fPAR, enabling us to capture spatiotemporal variations in ecosystem carbon uptake activity. Since 2015, Sentinel-2 having about 3-day of revisit interval and 10 m of spatial resolution has been operated. This study evaluates the relationship between ground observed fPAR and Sentinel-2 derived NDVI in four temperate forests in Niigata prefecture, central Japan. Briefly, fPAR for April to July (from 0.44 to 0.96) varied depending on the seasons and the forest ages and types (i.e., two young mixed forests, a mature mixed forest, and a mature needleleaf forest). In the two young mixed forests and the mature needleleaf forest, NDVI was positively correlated with fPAR (r = 0.81 to 0.99), where a 0.1 increase in NDVI implied a 0.2 increase in fPAR. However, the correlation between NDVI and fPAR in the mature mixed forest was weak (r = 0.38 to 0.53). Thus, this study confirmed that the effectiveness of Sentinel-2 derived NDVI tracking spatiotemporal variations in fPAR and carbon uptake activity likely varied depending on types and ages of forests.
Although natural disturbances such as wildfire, extreme weather events, and insect outbreaks play a key role in structuring ecosystems and watersheds worldwide, climate change has intensified many disturbance regimes, which can have compounding negative effects on ecosystem processes and services. Recent studies have highlighted the need to understand whether wildfire increases or decreases after large-scale beetle outbreaks. However, observational studies have produced mixed results. To address this, we applied a coupled ecohydrological-fire regime-beetle effects model (RHESSys-WMFire-Beetle) in a semiarid watershed in the western US. We found that surface fire probability and fire size decreased in the red phase (0-5 years post-outbreak), increased in the gray phase (6-15 years post-outbreak), and depended on mortality level in the old phase (one to several decades post-outbreak). In the gray and old phases, surface fire size and probability did not respond to low levels of beetle-caused mortality (<=20%), increased during medium levels of mortality (>20% and <=50%), and remained elevated but did not change with mortality (during the gray phase) or decreased (during the old phase) when mortality was high (>50%). Wildfire responses also depended on fire regime. In fuel-limited locations, fire typically increased with increasing fuel loads, whereas in fuel-abundant (flammability-limited) systems, fire sometimes decreased due to decreases in fuel aridity. This modeling framework can improve our understanding of the mechanisms driving wildfire responses and aid managers in predicting when and where fire hazards will increase.
The Middle East is one of the world’s most vulnerable areas to climate change, which has exacerbated environmental, agricultural, water conflict, and public health issues in the region. Here we analyze the latest climate model projections of precipitation and temperature for the very high emissions scenario, SSP5-8.5, to detect potential future changes in this region. A baseline period (1981-2010) is compared with the middle (2040-2069) and end (2070-2099) of the 21st century. The results, representing the worst-case scenario, identify the Tigris-Euphrates headwaters as the hotspot of future compounding effects of climate change in the Middle East. Those effects result from the coincidence of elevated temperature, reduced precipitation, and enhanced interannual variability of precipitation. The hotspot overlays the location of the Southeastern Anatolia Project (in Turkish, GAP) irrigation initiative. In this climate context, risks to GAP viability and downstream water security, and associated potential for water-related conflicts and migration are considerable and demand a reconsideration of the risk-benefit assessment of GAP. This need has become more urgent after the recent widespread and deadly climate-related conflicts and wildfires in summer 2021 across the Middle East that further underlined vulnerability of the region to climate extremes.
We explore a metric-learning approach to create representations of sorghum images grown in a field setting. We train a convolutional neural network to embed images so that images from the same variety have similar features and images of different varieties have different features. We that these features are good at discriminating unseen cultivars, can be used to predict standard phenotypes (height and leaf length and width), and can be used to predict presence or absence of genetic mutations. We evaluate these results using TERRA-REF data from field-scale trials of hundreds of varieties of sorghum. This demonstrates an end-to-end solution for creating useful image phenotypes in
(250 words) Micronutrients, such as iron, zinc, and sulfur, play a vital role in both plant and human development. Understanding how plants sense and allocate nutrients within their tissues may offer different venues to develop plants with high nutritional value. Despite decades of intensive research, more than 40% of genes in Arabidopsis remain uncharacterized or have no assigned function. While several resources such as mutant populations or diversity panels offer the possibility to identify genes critical for plant nutrition, the ability to consistently track and assess plant growth in an automated, unbiased way is still a major limitation. High-throughput phenotyping (HTP) is the new standard in plant biology but few HTP systems are open source and user friendly. Therefore, we developed OPEN Leaf, an open source HTP for hydroponic experiments. OPEN Leaf is capable of tracking changes in both size and color of the whole plant and specific regions of the rosette. We have also integrated communication platforms (Slack) and cloud services (CyVerse) to facilitate user communication, collaboration, data storage, and analysis in real time. As a proof-of-concept, we report the ability of OPEN Leaf to track changes in size and color when plants are growing hydroponically with different levels of nutrients. We expect that the availability of open source HTP platforms, together with standardized experimental conditions agreed by the scientific community, will advance the identification of genes and networks mediating nutrient uptake and allocation in plants.
Agronomic technological advancements provide more precise means to establish methodologies that can estimate yield response in many different ways. We are experimenting with a new image-based technique to predict the yield response of Canola using the rate of ground cover accumulation. Our trial was composed of row spacing and seeding rate as factors that influence the growth and the spatial distribution to evaluate its influence on the yield. Using the Visible Band Difference Vegetation Index (VDVI) from digital images, we estimated the ground cover and modelled the change over time. We regressed ground cover accumulation and integrated the function to calculate the area under the curve to regress against yield. Preliminary analysis indicates that the green ground cover accumulation overtime is sufficiently correlated with the yield (F=168.1, p=2.2e-16, R2=0.4694). Further, our results suggest the amount of green ground cover accumulation over time is dependent on the seeding density and row spacing. The analysis shows the higher seeding densities, 40 plants/m2 and above, acquire biomass rapidly, and the most stable yield predictions with ground cover are likely reached at similar plant densities. The most stable yield predictions in-relation to row spacing obtained from either 0.3m, 0.45m or 0.6m spacing (R-squares 0.94, 0.93, and 0.89, respectively). We are further experimenting to understand what growth period of the crop is most suitable for ground cover based yield predictions. Our primary target is to develop a high throughput image-based methodology to estimate the yield response using the ground cover accumulation rate for on-farm precision agronomy.
Climate extremes such as droughts, floods, heatwaves, frosts, and windstorms add considerable variability to the global year-to-year increase in atmospheric CO2 through their influence on terrestrial ecosystems. While the impact of droughts on terrestrial ecosystems has received considerable attention, the response to flooding events of varying intensity is poorly understood. To improve upon such understanding, the impact of the 2019 US flooding on regional CO2 vegetation fluxes is examined in the context of 2017-2018 years when such precipitation anomalies are not observed. CO2 is simulated with NASA’s Global Earth Observing System (GEOS) combined with the Low-order Flux Inversion (LoFI), where fluxes of CO2 are estimated using a suite of remote sensing measurements including greenness, night lights, and fire radiative power and bias corrected based on in situ observations. Net ecosystem exchange CO2 tracer is separated into the three regions covering the Midwest, South, and Eastern Texas and adjusted to match CO2 observations from towers located in Iowa, Mississippi, and Texas. Results indicate that for the Midwestern region consisting primarily of corn and soybeans crops, flooding contributes to a 15-25% reduction of net carbon uptake in May-September of 2019 in comparison to 2017 and 2018. These results are supported by independent reports of changes in agricultural activity. For the Southern region, comprised mainly of non-crop vegetation, net carbon uptake is enhanced in May-September of 2019 by about 10-20% in comparison to 2017 and 2018. These outcomes show the heterogeneity in effects that excess wetness can bring to diverse ecosystems.
Water is one of the most important substances for plants. Limited water supplies directly influence crop yield, which eventually leads to food scarcity to humans. In this present study, we quantify the dynamics of a fluttering leaf as a function of the days without water. The deformation and vibration of a plant leaf can be induced by dropping an object or tapping with a finger. Using multiple cameras, the 3D motion of a leaf can be measured. We found that the frequency of a leaf increases with water stress. In terms of natural frequency, it tends to increase when moisture stress is applied to leaves. These results suggest that the stiffness of leaves according to moisture stress is related, so it can be used as an indicator of the overall performance of plants. This would lead to a nondestructive way to measure water stress through leaf stiffness.
Land management activities that provide higher soil organic carbon stimulate microbial activity and enzymatic reactions. Riparian forest, agroforestry, and row-crop agriculture treatments are among common land-use systems in the lower Missouri River Floodplain (MRF) region in New Franklin, MO. The study of soil enzyme activities under different land use in this region is of importance for monitoring soil quality and evaluation of climatic changes on soil health. This investigation aimed to characterize soil properties such as soil C and N, porosity, moisture content under three-land use (agroforestry, riparian forest, and agriculture) and correlate their influence on soil microbial communities and enzyme activities. Soil samples were collected from the three land management systems, and enzyme activity was measured in three seasons of Fall 2019, Summer 2020, and Spring 2021. Results revealed significantly higher levels of β-glucosidase, β-glucosaminidase, and dehydrogenase activity in agroforestry (AF) and riparian forest (RF) treatments relative to agriculture (AG) management in all three studied seasons. Dehydrogenase activity was higher (p<0.0001) in RF relative to AF and AG sites. Efforts to incorporate perennial management systems in river-floodplain landscapes will help increase organic matter content, which stimulates microbial diversity and soil enzyme activity as well as improving the performance of conservation buffers. The study concluded that tree-based AF systems enhance soil physicochemical and biological properties.
Laboratory studies have shown that rhizodeposits could lead to either soil structural formation or dispersion depending on plant species, soil conditions, and microbial activity. However, these studies have usually been conducted in dry soils and rarely considered the combined effect of rhizodeposit and organic residues on soil structure. This study hypothesizes that root exudates promote soil dispersion initially, but over time decomposition of root exudates produce binding agents that promote stable soil structure in the rhizosphere. To test this hypothesis, a sandy loam soil sieved to < 500 µm particle size was first amended with root exudate compounds (14.4 mg C g-1), δ13C-barley residue (0.44 mg C g-1 soil), or both. Six replicate samples per treatment were packed in cores to a bulk density of 1.27 g cm-3 and then equilibrated on a tension table at -2 kPa matric potential. Rheological measurements of flow characteristics (dynamic viscosity) and strength (storage modulus, loss modulus, tan δ, and yield stress) of the control and amended soils were obtained immediately after amendment and after twelve days of incubation at 20 oC. Only root exudate compounds initially decreased the capacity of soil to retain water at -2 kPa by 21% and by 49% after incubation. Likewise, the yield stress of root exudate amended soil was significantly (P < 0.05) lower than that of the unamended soil, reflecting dispersion of soil. However, microbial decomposition/activities significantly (P < 0.05) increased yield stress over the corresponding pre-incubation values for these treatments by 200% (root exudate) and 230% (root exudate + δ13C-barley residue). These results confirmed the hypothesized dual effect of root exudates on rhizosphere structure. The initial soil dispersion may facilitate root growth by augmenting soil penetrability and releasing nutrients that were occluded in soil aggregates, whereas stable soil structure is achieved upon decomposition of root exudates.
The total population of Ghana has tripled between 1960 and 2015. During the same period, the urban population, however, grew more than 11 times. Rapid urbanization and large increase in population dramatically changed the land cover of the West African country. For example, agricultural land expanded from occupying 13% in mid-1970s to more than a third of Ghana’s total land area today. In the meantime, forests and savannas face a huge pressure of being converted to agricultural or urban land uses. The Ghana Land Use Project (GALUP) aims at enhancing the country’s capacity in dealing with these challenges. The project engages both institutions and government agencies in Ghana to deliver a series of training workshops focused on remote sensing and geospatial technologies that can facilitate the formulation of sustainable land use plans. In-person workshops were planned initially, but because of travel restrictions due to the COVID-19 pandemic, the first GALUP workshop—Land-Use Suitability Analysis with QGIS Tools—was conducted online. Such means of capacity building presented an exceptional opportunity to explore novel methods for transferring knowledge while also forging strong partnerships that are easier with in-person meetings. The 3-month long workshop was delivered in a hybrid mode featuring synchronous and asynchronous components. This hybrid mode was unusual for both trainers and the 41 trainees from four organizations including the Land Use and Spatial Planning Authority (LUSPA), the Center for Remote Sensing and Geographic Information Services (CERSGIS), the International Crop Research Institute for Semi-Arid Tropics (ICRISAT) and the Agro-Hydrological and Meteorological Centre (AGRHYMET) in Niger. The synchronous component involved weekly meetings and discussion session, and the asynchronous component consisted of a GitHub repository. The repository contained (a) fourteen open-source GIS tools developed for land-use suitability modeling, (b) a discussion channel for Q&A and idea-sharing, and (c) four modules of training materials, each equipped with customized videos and multiple exercises to boost the learning process. The repository has received over 13,000 views since the beginning of the workshop.
Drought is a recurring and extreme hydroclimatic hazard with serious impacts on agriculture and overall society. Delineation and forecasting of agricultural and meteorological drought are essential for water resource management and sustainable crop production. Agricultural drought assessment is defined as the deficit of root-zone soil moisture (RZSM) during active crop growing season, whereas meteorological drought is defined as subnormal precipitation over months to years. Several indices have been used to characterize droughts, however, there is a lack of study focusing on comprehensive comparison among different agricultural and meteorological drought indices for their ability to delineate and forecast drought across major climate regimes and land cover types. This study evaluates the role of RZSM from Soil Moisture Active Passive (SMAP) mission along with two other soil moisture (SM) based indices (e.g., Palmer Z and SWDI) for agricultural and meteorological drought monitoring in comparison with two popular meteorological drought indices (e.g., SPEI and SPI) and a hybrid (Comprehensive Drought Index, CDI) drought index. Results demonstrate that SM-based indices (e.g., Palmer Z, SMAP, SWDI) delineated agricultural drought events better than meteorological (e.g., SPI, SPEI) and hybrid (CDI) drought indices, whereas the latter three performed better in delineating meteorological drought across the contiguous USA during 2015–2019. SM-based indices showed skills for forecasting agricultural drought (represented by end-of-growing season gross primary productivity) in the early growing seasons. The results further confirm the key role of SM on ecosystem dryness and corroborate the SM-memory in land-atmosphere coupling.
Rice (Oryza sativa) is a major staple food crop in India occupying about 44 million ha (Mha) of cropped land in meeting food requirements for about 65% of the population. As water scarcity has become a major concern in changing climatic scenarios precise measurements of actual evapotranspiration (ETa) and crop coefficients (Kc) are needed to better manage the limited water resources and improve irrigation scheduling. The eddy covariance (EC) method was used to determine ETa and Kc of tropical lowland rice in eastern India over two years. Reference evapotranspiration (ETa) was estimated by four different approaches– the Food and Agriculture Organization-Penman-Monteith (FAO-PM) method, the Hargreaves, and Samani (HS) method, the Mahringer (MG) method, and pan evaporation (Epan) measurements. Measurements of turbulent and available energy fluxes were taken using EC during two rice growing seasons: dry season (January-May) and wet season (July-November) and also in the fallow period where no crop was grown. Results demonstrated that the magnitude of average ETa during dry seasons (2.86 and 3.32 mm d-1 in 2015 and 2016, respectively) was higher than the wet seasons (2.3 and 2.2 mm d-1) in both the years of the experiment. The FAO-PM method best-represented ETa in this lowland rice region of India as compared to the other three methods. The energy balance was found to be more closed in the dry seasons (75–84%) and dry fallow periods (73–81%) as compared to the wet seasons (42–48%) and wet fallows (33-69%) period of both the years of study, suggesting that lateral heat transport was an important term in the energy balance calculation. The estimated Kc values for lowland rice in dry seasons by the FAO-PM method at the four crop growth stages; namely, initial, crop development, reproductive, and late-season were 0.23, 0.42, 0.64, and 0.90, respectively, in 2015 and 0.32, 0.52, 0.76 and 0.88, respectively, in 2016. The FAO-PM, HS, and MG methods produced reliable estimates of Kc values in dry seasons, whereas Epan; performed better in wet seasons. The results further demonstrated that the Kc values derived for tropical lowland rice in eastern India are different from those suggested by the FAO implying revision of Kc values for regional-scale irrigation planning.
The spatio-temporal variation of stomatal conductance directly regulates photosynthesis, water partitioning, and biosphere-atmosphere interactions. While many studies have focused on stomatal response to stresses, the spatial variation of unstressed stomatal conductance remains poorly determined, and is usually characterized in land surface models (LSMs) simply based on plant functional type (PFT). Here, we derived unstressed stomatal conductance at the ecosystem-scale using observations from 115 global FLUXNET sites. When aggregated by PFTs, the across-PFT pattern was highly consistent with the parameterizations of LSMs. However, PFTs alone captured only 17\% of the variation in unstressed stomatal conductance across sites. Within the same PFT, unstressed stomatal conductance was negatively related to climate dryness and canopy height, which explained 45\% of the total spatial variation. Our results highlight the importance of plant-environment interactions in shaping stomatal traits. The trait-environment relationship established here provides an empirical approach for improved parameterizations of stomatal conductance in LSMs.
More and more applications of electrical resistivity tomography (ERT) for cylindrical objects have been rising in recent decades. This paper presents a 2.5-dimensional differential resistivity reconstruction scheme of cylindrical objects. The forward modeling algorithm incorporates the modified optimization wavenumbers to achieve an accurate 2.5-dimensional forward modeling. The modified optimization wavenumber selection is based on the approximate analytic solution of the circumference potential distribution of an infinitely long homogeneous cylindrical model, making it more accurate for cylindrical objects compared to the traditional optimization wavenumber selection which is only applicable for the half-space condition. In the laboratory, we measured the resistivity and resistance distributions of the sodium chloride solution-filled cylindrical tanks with/without a high resistivity rubber bar in the central. The modified and traditional optimization wavenumbers are included respectively to calculate the resistance distribution of the measured objects. The comparison results between the calculated and measured resistance distribution show that the modified optimization wavenumbers proposed in this paper can obtain higher calculation accuracy. The differential ERT incorporating the modified optimization wavenumbers is then employed to reconstruct the resistivity distribution of the cylindrical objects. The inversed resistivity values are in good agreement with the measured values. We, therefore, conclude that the modified optimization wavenumbers can result in better accuracy than the traditional one and the proposed 2.5-dimensional differential resistivity reconstruction scheme is time-saving and has great promise for the imaging of cylindrical objects.
Agriculture is the leading sector in the Ethiopian economy, which contributes about 44 percent of the total GDP as compared to 14 percent from industry and 42 percent from services. Although it is still the dominant sector, most of Ethiopia’s cultivated land is under rainfed agriculture, and only 7.5 percent of irrigable areas are under irrigation schemes. The objectives of this study were to: (1) identify where and quantify highly suitable areas for irrigation schemes and rainfed and analyze the gaps with the existing areas for irrigation & rainfed, (2) identify development potentials for both irrigation and rainfed scenarios by using determining factors affecting their potential, (3) draw the attention and provide a better guide for investment decisions that would enhance national & regional development potential in boosting agricultural production and productivity in Ethiopia. Regarding land suitability, different input datasets were analyzed and suitable areas were identified for irrigation and rainfed agriculture. Some common variables were used to identify land suitability of both scenarios (irrigation and rainfed) including agroecology, slope classes, land use land cover types, road networks, soil types, and districts and town populations. Whereas rainfall and rainfall variability were additional inputs for rainfed agriculture but the river and river flow rates were used as additional inputs for land suitability for irrigable areas. Furthermore, five major factors were identified to model development potential options for rainfed and irrigations scenarios, which are (1) land suitability, (2) agroecology, (3) population density, (4) market access, and (5) length of growing periods. These