THE BIG PICTURE - SATELLITE REMOTE SENSING APPLICATIONS IN RANGELAND ASSESSMENT AND CROP INSURANCE

Livestock Production/Industries, Risk and Uncertainty,

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)

An automated approach to map winter cropped area of smallholder farms across large scales using MODIS imagery

Fine-scale agricultural statistics are an important tool for understanding trends in food production and their associated drivers, yet these data are rarely collected in smallholder systems. These statistics are particularly important for smallholder systems given the large amount of fine-scale heterogeneity in production that occurs in these regions. To overcome the lack of ground data, satellite data are often used to map fine-scale agricultural statistics. However, doing so is challenging for smallholder systems because of (1) complex sub-pixel heterogeneity; (2) little to no available calibration data; and (3) high amounts of cloud cover as most smallholder systems occur in the tropics. We develop an automated method termed the MODIS Scaling Approach (MSA) to map smallholder cropped area across large spatial and temporal scales using MODIS Enhanced Vegetation Index (EVI) satellite data. We use this method to map winter cropped area, a key measure of cropping intensity, across the Indian subcontinent annually from 2000-2001 to 2015-2016. The MSA defines a pixel as cropped based on winter growing season phenology and scales the percent of cropped area within a single MODIS pixel based on observed EVI values at peak phenology. We validated the result with eleven high-resolution scenes (spatial scale of 5 × 5 m2 or finer) that we classified into cropped versus non-cropped maps using training data collected by visual inspection of the high-resolution imagery. The MSA had moderate to high accuracies when validated using these eleven scenes across India (R2 ranging between 0.19 and 0.89 with an overall R2 of 0.71 across all sites). This method requires no calibration data, making it easy to implement across large spatial and temporal scales, with 100% spatial coverage due to the compositing of EVI to generate cloud-free data sets. The accuracies found in this study are similar to those of other studies that map crop production using automated methods and use no calibration data. To aid research on agricultural production at fine spatial scales in India, we make our annual winter crop maps from 2000-2001 to 2015-2016 at 1 × 1 km2 produced in this study publically available through the NASA Socioeconomic Data and Applications Center (SEDAC) hosted by the Center for International Earth Science Information Network (CIESIN) at Columbia University. We also make our R script available since it is likely that this method can be used to map smallholder agriculture in other regions across the globe given that our method performed well in disparate agro-ecologies across India

Winter crop sensitivity to inter-annual climate variability in central India

India is predicted to be one of the most vulnerable agricultural regions to future climate changes. Here, we examined the sensitivity of winter cropping systems to inter-annual climate variability in a local market and subsistence-based agricultural system in central India, a data-rich validation site, in order to identify the climate parameters to which winter crops – mainly wheat and pulses in this region – might be sensitive in the future. We used satellite time-series data to quantify inter-annual variability in multiple climate parameters and in winter crop cover, agricultural census data to quantify irrigation, and field observations to identify locations for specific crop types. We developed three mixed-effect models (250 m to 1 km scale) to identify correlations between crop cover (wheat and pulses) and twenty-two climate and environmental parameters for 2001-2013. We find that winter daytime mean temperature (November–January) is the most significant factor affecting winter crops, irrespective of crop type, and is negatively associated with winter crop cover. With pronounced winter warming projected in the coming decades, effective adaptation by smallholder farmers in similar landscapes would require additional strategies, such as access to fine-scale temperature forecasts and heat-tolerant winter crop varieties

Winter crop sensitivity to inter-annual climate variability in central India

India is predicted to be one of the most vulnerable agricultural regions to future climate changes. Here, we examined the sensitivity of winter cropping systems to inter-annual climate variability in a local market and subsistence-based agricultural system in central India, a data-rich validation site, in order to identify the climate parameters to which winter crops – mainly wheat and pulses in this region – might be sensitive in the future. We used satellite time-series data to quantify inter-annual variability in multiple climate parameters and in winter crop cover, agricultural census data to quantify irrigation, and field observations to identify locations for specific crop types. We developed three mixed-effect models (250 m to 1 km scale) to identify correlations between crop cover (wheat and pulses) and twenty-two climate and environmental parameters for 2001-2013. We find that winter daytime mean temperature (November–January) is the most significant factor affecting winter crops, irrespective of crop type, and is negatively associated with winter crop cover. With pronounced winter warming projected in the coming decades, effective adaptation by smallholder farmers in similar landscapes would require additional strategies, such as access to fine-scale temperature forecasts and heat-tolerant winter crop varieties

Mapping cropping intensity of smallholder farms: A comparison of methods using multiple sensors

The food security of smallholder farmers is vulnerable to climate change and climate variability. Cropping intensity, the number of crops planted annually, can be used as a measure of food security for smallholder farmers given that it can greatly affect net production. Current techniques for quantifying cropping intensity may not accurately map smallholder farms where the size of one field is typically smaller than the spatial resolution of readily available satellite data. We evaluated four methods that use multi-scalar datasets and are commonly used in the literature to assess cropping intensity of smallholder farms: 1) the Landsat threshold method, which identifies if a Landsat pixel is cropped or uncropped during each growing season, 2) the MODIS peak method, which determines if there is a phenological peak in the MODIS Enhanced Vegetation Index time series during each growing season, 3) the MODIS temporal mixture analysis, which quantifies the sub-pixel heterogeneity of cropping intensity using phenological MODIS data, and 4) the MODIS hierarchical training method, which quantifies the sub-pixel heterogeneity of cropping intensity using hierarchical training techniques. Each method was assessed using four criteria: 1) data availability, 2) accuracy across different spatial scales (at aggregate scales 250 × 250 m, 1 × 1 km, 5 × 5 km, and 10 × 10 km), 3) ease of implementation, and 4) ability to use the method over large spatial and temporal scales. We applied our methods to two regions in India (Gujarat and southeastern Madhya Pradesh) that represented diversity in crop type, soils, climatology, irrigation access, cropping intensity, and field size. We found that the Landsat threshold method is the most accurate (R2 greater than or equal to 0.71 and RMSE less than or equal to 0.14), particularly at smaller scales of analysis. Yet given the limited availability of Landsat data, we find that the MODIS hierarchical training method meets multiple criteria for mapping cropping intensity over large spatial and temporal scales. Furthermore, the adjusted R2 between predicted and validation data generally increased and the RMSE decreased with spatial aggregation greater than or equal to 5 × 5 km (R2 up to 0.97 and RMSE as low as 0.00). Our model accuracy varied based on the region and season of analysis and was lowest during the summer season in Gujarat when there was high sub-pixel heterogeneity due to sparsely cropped agricultural land-cover. While our results specifically apply to our study regions in India, they most likely also apply to smallholder agriculture in other locations across the globe where the same types of satellite data are readily available

Satellite remote sensing for assessment of irrigation system performance: a case study in India

Irrigation management / Irrigated farming / Agricultural production / Irrigation systems / Food production / Rice / Cropping systems / Crop yield / Remote sensing / GIS / Models / Policy / Case studies / Satellite surveys / Performance evaluation / India / Bhadra Project

Sensitivity of crop cover to climate variability: Insights from two Indian agro-ecoregions

Crop productivity in India varies greatly with inter-annual climate variability and is highly dependent on monsoon rainfall and temperature. The sensitivity of yields to future climate variability varies with crop type, access to irrigation and other biophysical and socio-economic factors. To better understand sensitivities to future climate, this study focuses on agro-ecological subregions in Central and Western India that span a range of crops, irrigation, biophysical conditions and socioeconomic characteristics. Climate variability is derived from remotely-sensed data products, Tropical Rainfall Measuring Mission (TRMM – precipitation) and Moderate Resolution Imaging Spectroradiometer (MODIS – temperature). We examined green-leaf phenologies as proxy for crop productivity using the MODIS Enhanced Vegetation Index (EVI) from 2000 to 2012. Using both monsoon and winter growing seasons, we assessed phenological sensitivity to inter-annual variability in precipitation and temperature patterns. Inter-annual EVI phenology anomalies ranged from −25% to 25%, with some highly anomalous values up to 200%. Monsoon crop phenology in the Central India site is highly sensitive to climate, especially the timing of the start and end of the monsoon and intensity of precipitation. In the Western India site, monsoon crop phenology is less sensitive to precipitation variability, yet shows considerable fluctuations in monsoon crop productivity across the years. Temperature is critically important for winter productivity across a range of crop and management types, such that irrigation might not provide a sufficient buffer against projected temperature increases. Better access to weather information and usage of climate-resilient crop types would play pivotal role in maintaining future productivity. Effective strategies to adapt to projected climate changes in the coming decades would also need to be tailored to regional biophysical and socio-economic conditions