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

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

Agricultural Research Service research highlights in remote sensing for calendar year 1981

Selected examples of research accomplishments related to remote sensing are compiled. A brief statement is given to highlight the significant results of each research project. A list of 1981 publication and location contacts is given also. The projects cover emission and reflectance analysis, identification of crop and soil parameters, and the utilization of remote sensing data

Earth Observations and Integrative Models in Support of Food and Water Security

Global food production depends upon many factors that Earth observing satellites routinely measure about water, energy, weather, and ecosystems. Increasingly sophisticated, publicly-available satellite data products can improve efficiencies in resource management and provide earlier indication of environmental disruption. Satellite remote sensing provides a consistent, long-term record that can be used effectively to detect large-scale features over time, such as a developing drought. Accuracy and capabilities have increased along with the range of Earth observations and derived products that can support food security decisions with actionable information. This paper highlights major capabilities facilitated by satellite observations and physical models that have been developed and validated using remotely-sensed observations. Although we primarily focus on variables relevant to agriculture, we also include a brief description of the growing use of Earth observations in support of aquaculture and fisheries

Field-scale mapping of evaporative stress indicators of crop yield: An application over Mead, NE, USA

The Evaporative Stress Index (ESI) quantifies temporal anomalies in a normalized evapotranspiration (ET) metric describing the ratio of actual-to-reference ET (fRET) as derived from satellite remote sensing. At regional scales (3–10 km pixel resolution), the ESI has demonstrated the capacity to capture developing crop stress and impacts on regional yield variability in water-limited agricultural regions. However, its performance in some regions where the vegetation cycle is intensively managed appears to be degraded due to spatial and temporal limitations in the standard ESI products. In this study, we investigated potential improvements to ESI by generating maps of ET, fRET, and fRET anomalies at high spatiotemporal resolution (30-m pixels, daily time steps) using a multi-sensor data fusion method, enabling separation of landcover types with different phenologies and resilience to drought. The study was conducted for the period 2010–2014 covering a region around Mead, Nebraska that includes both rainfed and irrigated crops. Correlations between ESI and measurements of maize yield were investigated at both the field and county level to assess the potential of ESI as a yield forecasting tool. To examine the role of crop phenology in yield-ESI correlations, annual input fRET time series were aligned by both calendar day and by biophysically relevant dates (e.g. days since planting or emergence). At the resolution of the operational U.S. ESI product (4 km), adjusting fRET alignment to a regionally reported emergence date prior to anomaly computation improves r2 correlations with county-level yield estimates from 0.28 to 0.80. At 30-m resolution, where pure maize pixels can be isolated from other crops and landcover types, county-level yield correlations improved from 0.47 to 0.93 when aligning fRET by emergence date rather than calendar date. Peak correlations occurred 68 days after emergence, corresponding to the silking stage for maize when grain development is particularly sensitive to soil moisture deficiencies. The results of this study demonstrate the utility of remotely sensed ET in conveying spatially and temporally explicit water stress information to yield prediction and crop simulation models

Multilevel measurements of surface temperature over undulating terrain planted to barley

A ground and aircraft program was conducted to extend ground based methods for measuring soil moisture and crop water stress to aircraft and satellite altitudes. A 260ha agricultural field in California was used over the 1977-78 growing season. For cloud free days ground based temperature measurements over bare soil were related to soil moisture content. Water stress resulted from too much water, not from lack of it, as was expected. A theoretical examination of the canopy air temperature difference as affected by vapor pressure deficit and net radiation was developed. This analysis shows why surface temperatures delineate crop water stress under conditions of low humidity, but not under high humidity conditions. Multilevel temperatures acquired from the ground, low and high altitude aircraft, and the Heat Capacity Mapping Mission (HCMM) spacecraft were compared for two day and one night overpasses. The U-2 and low altitude temperatures were within 0.5 C. The HCMM data were analyzed using both the pre- and post-launch calibrations, with the former being considerably closer in agreement with the aircraft data than the latter

Advances in field-based high-throughput photosynthetic phenotyping

Gas exchange techniques revolutionized plant research and advanced understanding, including associated fluxes and efficiencies, of photosynthesis, photorespiration, and respiration of plants from cellular to ecosystem scales. These techniques remain the gold standard for inferring photosynthetic rates and underlying physiology/biochemistry, although their utility for high-throughput phenotyping (HTP) of photosynthesis is limited both by the number of gas exchange systems available and the number of personnel available to operate the equipment. Remote sensing techniques have long been used to assess ecosystem productivity at coarse spatial and temporal resolutions, and advances in sensor technology coupled with advanced statistical techniques are expanding remote sensing tools to finer spatial scales and increasing the number and complexity of phenotypes that can be extracted. In this review, we outline the photosynthetic phenotypes of interest to the plant science community and describe the advances in high-throughput techniques to characterize photosynthesis at spatial scales useful to infer treatment or genotypic variation in field-based experiments or breeding trials. We will accomplish this objective by presenting six lessons learned thus far through the development and application of proximal/remote sensing-based measurements and the accompanying statistical analyses. We will conclude by outlining what we perceive as the current limitations, bottlenecks, and opportunities facing HTP of photosynthesis

A multi-temporal phenology based classification approach for Crop Monitoring in Kenya

The SBAM (Satellite Based Agricultural Monitoring) project, funded by the Italian Space Agency aims at: developing a validated satellite imagery based method for estimating and updating the agricultural areas in the region of Central-Africa; implementing an automated process chain capable of providing periodical agricultural land cover maps of the area of interest and, possibly, an estimate of the crop yield. The project aims at filling the gap existing in the availability of high spatial resolution maps of the agricultural areas of Kenya. A high spatial resolution land cover map of Central-Eastern Africa including Kenya was compiled in the year 2000 in the framework of the Africover project using Landsat images acquired, mostly, in 1995. We investigated the use of phenological information in supporting the use of remotely sensed images for crop classification and monitoring based on Landsat 8 and, in the near future, Sentinel 2 imagery. Phenological information on crop condition was collected using time series of NDVI (Normalized Difference Vegetation Index) based on Landsat 8 images. Kenyan countryside is mainly characterized by a high number of fragmented small and medium size farmlands that dramatically increase the difficulty in classification; 30 m spatial resolution images are not enough for a proper classification of such areas. So, a pan-sharpening FIHS (Fast Intensity Hue Saturation) technique was implemented to increase image resolution from 30 m to 15 m. Ground test sites were selected, searching for agricultural vegetated areas from which phenological information was extracted. Therefore, the classification of agricultural areas is based on crop phenology, vegetation index behaviour retrieved from a time series of satellite images and on AEZ (Agro Ecological Zones) information made available by FAO (FAO, 1996) for the area of interest. This paper presents the results of the proposed classification procedure in comparison with land cover maps produced in the past years by other projects. The results refer to the Nakuru County and they were validated using field campaigns data. It showed a satisfactory overall accuracy of 92.66 % which is a significant improvement with respect to previous land cover maps

Agricultural Drought Monitoring in Kenya Using Evapotranspiration Derived from Remote Sensing and Reanalysis Data

More than half of the people in sub-Saharan Africa live on less than US$ 1.25 per day, and nearly 30% do not receive sufficient nourishment to maintain daily health (UN, 2009a). These figures are expected to rise as a result of the recent global financial crisis that has led to an increase in food prices. Food for Peace (FFP), the program that administers more than 85% of U.S. international food aid, recently reported that the seven largest recipient countries of food aid worldwide are in sub-Saharan Africa (FFP, 2010). In Kenya, the fifth largest recipient of food aid from FFP and a country highly dependent on rainfed agriculture, below-average precipitation in 2009 led to a 20% reduction in maize production and a 100% increase in domestic maize prices (FEWS NET, 2009). Given these sorts of climatic shocks, it is imperative that mitigation strategies be developed for sub-Saharan Africa and other regions of the developing world to improve the international and national response to impending food crises. Crop monitoring is an important tool used by national agricultural offices and other stakeholders to inform food security analyses and agricultural drought mitigation. Remote sensing and surface reanalysis data facilitate efficient and cost-effective approaches to measuring determinants of agricultural drought. In this chapter, we explore how remotely sensed estimates of actual evapotranspiration (ETa) can be integrated with surface reanalysis data to augment agricultural drought monitoring systems. Although water availability is important throughout every stage of crop development, from germination to harvest, crops are most sensitive to moisture deficits during the reproductive stages (Shanahan and Nielsen, 1987). A study that analyzed maize, for example, showed that a 1% decline in seasonal ETa led to an average loss of 1.5% in crop yield, whereas water stress in the same proportion concentrated during the reproductive phases led to a 2.6% decline in crop yield (Stegman, 1982). Agricultural drought can therefore be defined as inadequate soil water availability, particularly during the reproductive phase, caused by low precipitation, insufficient water-holding capacity in the root zone of the soil, and/or high atmospheric water demand (potential evapotranspiration, ETp), which results in a reduction in crop yield. Agricultural droughts differ in timescale and impact from shorter-term meteorological droughts, which are characterized by negative precipitation anomalies on the order of days to weeks, and the longer-term negative runoff and water storage anomalies that characterize hydrological drought (Dracup et al., 1980)

Agricultural Drought Monitoring in Kenya Using Evapotranspiration Derived from Remote Sensing and Reanalysis Data

More than half of the people in sub-Saharan Africa live on less than US$ 1.25 per day, and nearly 30% do not receive sufficient nourishment to maintain daily health (UN, 2009a). These figures are expected to rise as a result of the recent global financial crisis that has led to an increase in food prices. Food for Peace (FFP), the program that administers more than 85% of U.S. international food aid, recently reported that the seven largest recipient countries of food aid worldwide are in sub-Saharan Africa (FFP, 2010). In Kenya, the fifth largest recipient of food aid from FFP and a country highly dependent on rainfed agriculture, below-average precipitation in 2009 led to a 20% reduction in maize production and a 100% increase in domestic maize prices (FEWS NET, 2009). Given these sorts of climatic shocks, it is imperative that mitigation strategies be developed for sub-Saharan Africa and other regions of the developing world to improve the international and national response to impending food crises. Crop monitoring is an important tool used by national agricultural offices and other stakeholders to inform food security analyses and agricultural drought mitigation. Remote sensing and surface reanalysis data facilitate efficient and cost-effective approaches to measuring determinants of agricultural drought. In this chapter, we explore how remotely sensed estimates of actual evapotranspiration (ETa) can be integrated with surface reanalysis data to augment agricultural drought monitoring systems. Although water availability is important throughout every stage of crop development, from germination to harvest, crops are most sensitive to moisture deficits during the reproductive stages (Shanahan and Nielsen, 1987). A study that analyzed maize, for example, showed that a 1% decline in seasonal ETa led to an average loss of 1.5% in crop yield, whereas water stress in the same proportion concentrated during the reproductive phases led to a 2.6% decline in crop yield (Stegman, 1982). Agricultural drought can therefore be defined as inadequate soil water availability, particularly during the reproductive phase, caused by low precipitation, insufficient water-holding capacity in the root zone of the soil, and/or high atmospheric water demand (potential evapotranspiration, ETp), which results in a reduction in crop yield. Agricultural droughts differ in timescale and impact from shorter-term meteorological droughts, which are characterized by negative precipitation anomalies on the order of days to weeks, and the longer-term negative runoff and water storage anomalies that characterize hydrological drought (Dracup et al., 1980)