Showing papers by "Yufang Jin published in 2019"

An enhanced bloom index for quantifying floral phenology using multi-scale remote sensing observations

Abstract: Floral phenology, the timing and intensity of flowering, is intimately tied to the reproduction of terrestrial ecosystem and highly sensitive to climate change. However, observational records of flowering are very sparse, limiting our understanding of spatiotemporal dynamics of floral phenology from local to regional scales. Satellite remote sensing provides unique opportunities to monitor flowers through space and time in a cost-effective way. Here we developed an enhanced bloom index (EBI), based on the multispectral remotely sensed data, to quantify flowering status over almond (Prunus dulcis) orchards in Central Valley of California. Our test studies with unmanned aerial vehicle (UAV) multispectral imagery at 2.6–5.2 cm demonstrated that the EBI enhanced the signals of flowers and reduced the background noise from soil and green vegetation, and agreed well with the bloom coverage derived from supervised classification, with a R2 of 0.72. Experimental tests with multi-scale remote sensing observations from CERES aerial (0.2 m), PlanetScope (3 m), Sentinel-2 (10 m), and Landsat (30 m) satellite imagery further showed the robustness of the EBI in capturing the flower information. We found that the relatively dense time series of PlanetScope and Sentinel-2 imagery were able to capture the bloom dynamics of almond orchards. Satellite derived EBI is expected to track the bloom information and thus improve our understanding and prediction of flower and pollination response to weather and ultimately the yield.

California Almond Yield Prediction at the Orchard Level With a Machine Learning Approach.

Abstract: California's almond growers face challenges with nitrogen management as new legislatively mandated nitrogen management strategies for almond have been implemented. These regulations require that growers apply nitrogen to meet, but not exceed, the annual N demand for crop and tree growth and nut production. To accurately predict seasonal nitrogen demand, therefore, growers need to estimate block-level almond yield early in the growing season so that timely N management decisions can be made. However, methods to predict almond yield are not currently available. To fill this gap, we have developed statistical models using the Stochastic Gradient Boosting, a machine learning approach, for early season yield projection and mid-season yield update over individual orchard blocks. We collected yield records of 185 orchards, dating back to 2005, from the major almond growers in the Central Valley of California. A large set of variables were extracted as predictors, including weather and orchard characteristics from remote sensing imagery. Our results showed that the predicted orchard-level yield agreed well with the independent yield records. For both the early season (March) and mid-season (June) predictions, a coefficient of determination (R 2) of 0.71, and a ratio of performance to interquartile distance (RPIQ) of 2.6 were found on average. We also identified several key determinants of yield based on the modeling results. Almond yield increased dramatically with the orchard age until about 7 years old in general, and the higher long-term mean maximum temperature during April-June enhanced the yield in the southern orchards, while a larger amount of precipitation in March reduced the yield, especially in northern orchards. Remote sensing metrics such as annual maximum vegetation indices were also dominant variables for predicting the yield potential. While these results are promising, further refinement is needed; the availability of larger data sets and incorporation of additional variables and methodologies will be required for the model to be used as a fertilization decision support tool for growers. Our study has demonstrated the potential of automatic almond yield prediction to assist growers to manage N adaptively, comply with mandated requirements, and ensure industry sustainability.

Modeling the aerosol chemical composition of the tropopause over the Tibetan Plateau during the Asian summer monsoon

Abstract: . Enhanced aerosol abundance in the upper troposphere and lower stratosphere (UTLS) associated with the Asian summer monsoon (ASM) is referred to as the Asian Tropopause Aerosol Layer (ATAL). The chemical composition, microphysical properties, and climate effects of aerosols in the ATAL have been the subject of discussion over the past decade. In this work, we use the ECHAM/MESSy Atmospheric Chemistry (EMAC) general circulation model at a relatively fine grid resolution (about 1.1×1.1 ∘ ) to numerically simulate the emissions, chemistry, and transport of aerosols and their precursors in the UTLS within the ASM anticyclone during the years 2010–2012. We find a pronounced maximum of aerosol extinction in the UTLS over the Tibetan Plateau, which to a large extent is caused by mineral dust emitted from the northern Tibetan Plateau and slope areas, lofted to an altitude of at least 10 km, and accumulating within the anticyclonic circulation. We also find that the emissions and convection of ammonia in the central main body of the Tibetan Plateau make a great contribution to the enhancement of gas-phase NH3 in the UTLS over the Tibetan Plateau and ASM anticyclone region. Our simulations show that mineral dust, water-soluble compounds, such as nitrate and sulfate, and associated liquid water dominate aerosol extinction in the UTLS within the ASM anticyclone. Due to shielding of high background sulfate concentrations outside the anticyclone from volcanoes, a relative minimum of aerosol extinction within the anticyclone in the lower stratosphere is simulated, being most pronounced in 2011, when the Nabro eruption occurred. In contrast to mineral dust and nitrate concentrations, sulfate increases with increasing altitude due to the larger volcano effects in the lower stratosphere compared to the upper troposphere. Our study indicates that the UTLS over the Tibetan Plateau can act as a well-defined conduit for natural and anthropogenic gases and aerosols into the stratosphere.

Estimating Rangeland Forage Production Using Remote Sensing Data from a Small Unmanned Aerial System (sUAS) and PlanetScope Satellite

Abstract: Rangelands cover ~23 million hectares and support a $3.4 billion annual cattle industry in California. Large variations in forage production from year to year and across the landscape make grazing management difficult. We here developed optimized methods to map high-resolution forage production using multispectral remote sensing imagery. We conducted monthly flights using a Small Unmanned Aerial System (sUAS) in 2017 and 2018 over a 10-ha deferred grazing rangeland. Daily maps of NDVI at 30-cm resolution were first derived by fusing monthly 30-cm sUAS imagery and more frequent 3-m PlanetScope satellite observations. We estimated aboveground net primary production as a product of absorbed photosynthetically active radiation (APAR) derived from NDVI and light use efficiency (LUE), optimized as a function of topography and climate stressors. The estimated forage production agreed well with field measurements having a R2 of 0.80 and RMSE of 542 kg/ha. Cumulative NDVI and APAR were less correlated with measured biomass ( R 2 = 0.68). Daily forage production maps captured similar seasonal and spatial patterns compared to field-based biomass measurements. Our study demonstrated the utility of aerial and satellite remote sensing technology in supporting adaptive rangeland management, especially during an era of climatic extremes, by providing spatially explicit and near-real-time forage production estimates.

Automatic mapping of planting year for tree crops with Landsat satellite time series stacks

Abstract: California’s Central Valley faces serious challenges of water scarcity and degraded groundwater quality due to nitrogen leaching. Orchard age is one of the key determinants for fruit and nut production and directly affects consumptive water use and fertilizer demand. However, regional and statewide spatially explicit information on orchard planting years in California is still lacking, despite some attempts to estimate tree ages using multi-temporal satellite imagery in other regions. Here we developed a robust detection method to track crop cover dynamics and identify the planting year through time series of Landsat imagery within the Google Earth Engine (GEE) platform. We used the full archive of Landsat data (Landsat-5 TM, Landsat-7 ETM+, and Landsat-8 OLI) from 1984 to 2017 as inputs and automated the GEE workflow for the on-fly-mapping. Preprocessing was initially performed using JavaScript to obtain high quality reflectance and Normalized Difference Vegetation Index (NDVI) time series for each Landsat pixel. Annual maximum NDVI was then aggregated to the orchard level based on the field boundary. Our change detection algorithm incorporated a set of decision rules, including adaptive identification of potential years with robust Z-score thresholds, elimination of false detections based on the post-planting growth curve, and estimation of planting year using the most recent minimum strategy. Our method showed a very high accuracy of estimating tree crop ages, with a R2 of 0.96 and a mean absolute error of less than half a year, when compared with 142 records provided by almond growers. We further evaluated the accuracy of the statewide mapping of planting years for all fruit and nut trees in California, and found an overall agreement of 89.2%. This automatic cloud-based application is expected to greatly strengthen our ability to forecast yield dynamics, estimate water use and fertilizer inputs, at individual field, county and statewide basis.

Microclimate–forage growth linkages across two strongly contrasting precipitation years in a Mediterranean catchment

Abstract: Author(s): Devine, SM; O'Geen, AT; Larsen, RE; Dahlke, HE; Liu, H; Jin, Y; Dahlgren, RA | Abstract: Given the complex topography of California rangelands, contrasting microclimates affect forage growth at catchment scales. However, documentation of microclimate–forage growth associations is limited, especially in Mediterranean regions experiencing pronounced climate change impacts. To better understand microclimate–forage growth linkages, we monitored forage productivity and root-zone soil temperature and moisture (0–15 and 15–30ncm) in 16 topographic positions in a 10-ha annual grassland catchment in California's Central Coast Range. Data were collected through two strongly contrasting growing seasons, a wet year (2016–17) with 287-mm precipitation and a dry year (2017–18) with 123-mm precipitation. Plant-available soil water storage (0–30ncm) was more than half full for most of the wet year; mean peak standing forage was 2790nkgnha−1 (range: 1597–4570nkgnha−1). The dry year had restricted plant-available water and mean peak standing forage was reduced to 970nkgnha−1 (range: 462–1496nkgnha−1). In the wet year, forage growth appeared energy limited (light and temperature): warmer sites produced more forage across a 3–4°C soil temperature gradient but late season growth was associated with moister sites spanning this energy gradient. In the dry year, the warmest topographic positions produced limited forage across a 10°C soil temperature gradient until late season rainfall in March. Linear models accounting for interactions between soil moisture and temperature explained about half of rapid, springtime forage growth variance. These findings reveal dynamic but clear microclimate–forage growth linkages in complex terrain, and thus, have implications for rangeland drought monitoring and dryland ecosystems modeling under climate change.

Fire Behavior Monitoring and Assessment in California with Multi-sensor Satellite Observations

Abstract: The past two years became the most destructive wildfire years on record in the state of California, including the Mendocino Complex and wind-driven Camp fire in 2018. Strong "Santa Ana" winds fueled fast moving fires which burned into developed areas of Los Angeles in Southern California, while strong offshore "Diablo" winds drove a series of deadly fires Northern California wine country in 2017. These devastating wildfires killed hundreds of people, destroyed thousands of structures, led to evacuation of hundreds of thousands of people, and caused severe air quality problems in downwind urban centers far away from burned areas.