Monitoring of Spatiotemporal Dynamics of Rabi Rice Fallows in South Asia Using Remote Sensing

Cereals and grain legumes are the most important part of human diet and nutrition. The expansion of grain legumes with improved productivity to cater the growing population’s nutritional security is of prime importance and need of the hour. Rice fallows are best niche areas with residual moisture to grow short-duration legumes, thereby achieving intensification. Identifying suitable areas for grain legumes and cereal grains is important in this region. In this context, the goal of this study was to map fallow lands followed by rainy season ( kharif ) rice cultivation or post-rainy ( rabi ) fallows in rice-growing environments between 2005 and 2015 using temporal moderate-resolution imaging spectroradiometer (MODIS) data applying spectral matching techniques. This study was conducted in South Asia where different rice ecosystems exist. MODIS 16 day normalized difference vegetation index (NDVI) at 250 m spatial resolution and season-wise-intensive ground survey data were used to map rice systems and the fallows thereafter ( rabi fallows) in South Asia. The rice maps were validated with independent ground survey data and compared with available subnational-level statistics. Overall accuracy and kappa coefficient estimated for rice classes were 81.5% and 0.79%, respectively, with ground survey data. The derived physical rice area and irrigated areas were highly correlated with the subnational statistics with R ^ 2 values of 94% at the district level for the years 2005–2006 and 2015–2016. Results clearly show that rice fallow areas increased from 2005 to 2015. The results show spatial distribution of rice fallows in South Asia, which are identified as target domains for sustainable intensification of short-duration grain legumes, fixing the soil nitrogen and increasing incomes of small-holder farmers

Hyperspectral Remote Sensing for Terrestrial Applications

Remote sensing data are considered hyperspectral when the data are gathered from numerous wavebands, contiguously over an entire range of the spectrum (e.g., 400–2500 nm). Goetz (1992) defines hyperspectral remote sensing as “The acquisition of images in hundreds of registered, contiguous spectral bands such that for each picture element of an image it is possible to derive a complete reflectance spectrum.” However, Jensen (2004) defines hyperspectral remote sensing as “The simultaneous acquisition of images in many relatively narrow, contiguous and/or non contiguous spectral bands throughout the ultraviolet, visible, and infrared portions of the electromagnetic spectrum.”..

A Holistic View of Global Croplands and Their Water Use for Ensuring Global Food Security in the 21st Century through Advanced Remote Sensing and Non-remote Sensing Approaches

This paper presents an exhaustive review of global croplands and their water use, for the end of last millennium, mapped using remote sensing and non-remote sensing approaches by world’s leading researchers on the subject. A comparison at country scale of global cropland area estimated by these studies had a high R2-value of 0.89–0.94. The global cropland area estimates amongst different studies are quite close and range between 1.47–1.53 billion hectares. However, significant uncertainties exist in determining irrigated areas which, globally, consume nearly 80% of all human water use. The estimates show that the total water use by global croplands varies between 6,685 to 7,500 km3 yr−1 and of this around 4,586 km3 yr−1 is by rainfed croplands (green water use) and the rest by irrigated croplands (blue water use). Irrigated areas use about 2,099 km3 yr−1 (1,180 km3 yr−1 of blue water and the rest from rain that falls over irrigated croplands). However, 1.6 to 2.5 times the blue water required by irrigated croplands is actually withdrawn from reservoirs or pumping of ground water, suggesting an irrigation efficiency of only between 40–62 percent. The weaknesses, trends, and future directions to precisely estimate the global croplands are examined. Finally, the paper links global croplands and their water use to a paradigm for ensuring future food security

Agricultural cropland extent and areas of South Asia derived using Landsat satellite 30-m time-series big-data using random forest machine learning algorithms on the Google Earth Engine cloud

The South Asia (India, Pakistan, Bangladesh, Nepal, Sri Lanka and Bhutan) has a staggering 900 million people (~43% of the population) who face food insecurity or severe food insecurity as per United Nations, Food and Agriculture Organization’s (FAO) the Food Insecurity Experience Scale (FIES). The existing coarse-resolution (≥250-m) cropland maps lack precision in geo-location of individual farms and have low map accuracies. This also results in uncertainties in cropland areas calculated fromsuch products. Thereby, the overarching goal of this study was to develop a high spatial resolution (30-m or better) baseline cropland extent product of South Asia for the year 2015 using Landsat satellite time-series big-data and machine learning algorithms (MLAs) on the Google Earth Engine (GEE) cloud computing platform. To eliminate the impact of clouds, 10 time-composited Landsat bands (blue, green, red, NIR, SWIR1, SWIR2, Thermal, EVI, NDVI, NDWI) were derived for each of the three timeperiods over 12 months (monsoon: Days of the Year (DOY) 151–300; winter: DOY 301–365 plus 1–60; and summer: DOY 61–150), taking the every 8-day data from Landsat-8 and 7 for the years 2013–2015, for a total of 30-bands plus global digital elevation model (GDEM) derived slope band. This 31-band mega-file big data-cube was composed for each of the five agro-ecological zones (AEZ’s) of South Asia and formed a baseline data for image classification and analysis. Knowledgebase for the Random Forest (RF) MLAs were developed using spatially well spread-out reference training data (N = 2179) in five AEZs. The classification was performed on GEE for each of the five AEZs using well-established knowledge-base and RF MLAs on the cloud. Map accuracies were measured using independent validation data (N = 1185). The survey showed that the South Asia cropland product had a producer’s accuracy of 89.9% (errors of omissions of 10.1%), user’s accuracy of 95.3% (errors of commission of 4.7%) and an overall accuracy of 88.7%. The National and sub-national (districts) areas computed from this cropland extent product explained 80-96% variability when compared with the National statistics of the South Asian Countries. The full-resolution imagery can be viewed at full-resolution, by zooming-in to any location in South Asia or the world, atwww.croplands. org and the cropland products of South Asia downloaded from The Land Processes Distributed Active Archive Center (LP DAAC) of National Aeronautics and Space Administration (NASA) and the United States Geological Survey (USGS): https://lpdaac.usgs.gov/products/gfsad30saafgircev001/

Monitoring Changes in Croplands Due to Water Stress in the Krishna River Basin Using Temporal Satellite Imagery

Remote sensing-based assessments of large river basins such as the Krishna, which supplies water to many states in India, are useful for operationally monitoring agriculture, especially basins that are affected by abiotic stress. Moderate-Resolution Imaging Spectroradiometer (MODIS) time series products can be used to understand cropland changes at the basin level due to abiotic stresses, especially water scarcity. Spectral matching techniques were used to identify land use/land cover (LULC) areas for two crop years: 2013–2014, which was a normal year, and 2015–2016, which was a water stress year. Water stress-affected crop areas were categorized into three classes—severe, moderate and mild—based on the normalized difference vegetation index (NDVI) and intensity of damage assessed through field sampling. Furthermore, ground survey data were used to assess the accuracy of MODIS-derived classification individual products. Water inflows into and outflows from the Krishna river basin during the study period were used as direct indicators of water scarcity/availability in the Krishna Basin. Furthermore, ground survey data were used to assess the accuracy of MODIS-derived LULC classification of individual year products. Rainfall data from the tropical rainfall monitoring mission (TRMM) was used to support the water stress analysis. The nine LULC classes derived using the MODIS temporal imagery provided overall accuracies of 82% for the cropping year 2013–2014 and 85% for the year 2015–2016. Kappa values are 0.78 for 2013–2014 and 0.82 for 2015–2016. MODIS-derived cropland areas were compared with national statistics for the cropping year 2013–2014 with a R2 value of 0.87. Results show that both rainfed and irrigated areas in 2015–2016 saw significant changes that will have significant impacts on food security. It has been also observed that the farmers in the basin tend to use lower inputs and labour per ha during drought years. Among all, access to water is the major driver determining the crop choice and extent of input-use in the basin

Inland Valley Wetland Cultivation and Preservation for Africa’s Green and Blue Revolution Using Multi-Sensor Remote Sensing

Africa is the second largest continent after Asia with a total area of 30.22 million km2 (including the adjacent islands). It has great rivers such as the River Nile, which is the longest in the world and flows a distance of 6650 km, and the River Congo, which is the deepest in the world, as well as the second largest in the world in terms of water availability. Yet, Africa also has vast stretches of arid, semiarid, and desert lands with little or no water. Further, Africa’s population is projected to increase by four times by the year 2100, reaching about four billion from the current population of little over one billion. Food insecurity and malnutrition are already highest in Africa (Heidhues et al., 2004) and the challenge of meeting the food security needs of the fastest-growing continent in the twenty-first century is daunting. So, many solutions are thought of to ensure food security in Africa. These ideas include such measures as increasing irrigation in a continent that currently has just about 2% of the global irrigated areas (Thenkabail et al., 2009a, 2010), improving crop productivity (kg m−2), and increasing water productivity (kg m−3). However, an overwhelming proportion of Africa’s agriculture now takes place on uplands that have poor soil fertility and water availability (Scholes, 1990). Thereby, the interest in developing sustainable agriculture in Africa’s lowland wetlands, considered by some as the “new frontier” in agriculture, has swiftly increased in recent years. The lowland wetland systems include the big wetland systems that are prominent and widely recognized (Figure 9.1) as well as the less prominent, but more widespread, inland valley (IV) wetlands (Figures 9.2 through 9.8) that are all along the first to highest order river systems..

Impacts of irrigation tank restoration on water bodies and croplands in Telangana State of India using Landsat time series data and machine learning algorithms

In 2014, the State of Telangana in southern India began repairing and restoring more than 46,000 irrigation water tanks (artificial reservoirs) under the Mission Kakatiya project with an investment in excess of USD 2 billion. In this study, we attempted to map the temporal changes that have occurred in cropland areas and water bodies as a result of the project, using remote sensing imagery and applying land use/land cover (LULC) mapping algorithms. We used 16-day time series data from Landsat 8 to study the spatial distribution of changes in water bodies and cropland areas over the 2013–18 period. Ground survey information was used to assess the pixel-based accuracy of the Landsat-derived data. The areas served by these tanks were identified on the basis of training data and Random Forest algorithms using Google Earth Engine. Our spatial analysis revealed a substantial increase in cropped area under irrigation and expansion of water bodies over the study period. We observed a 20% increase in total tank area in 2017–18 and total cropland and irrigated area expansion of the order of 0.6M ha and 0.2M ha, respectively. A comparison of ground survey data and four LULC classes derived from Landsat temporal imagery showed an overall accuracy of 87%, significantly correlated with national agriculture statistics. Periodic monitoring based on remote sensing has proved to be an effective method of capturing LULC changes resulting from the Mission Kakatiya interventions. Higher-resolution satellite data can further improve the accuracy of estimates

Geographical distribution of traits and diversity in the world collection of pearl millet [Pennisetum glaucum (L.) R. Br., synonym: Cenchrus americanus (L.) Morrone] landraces conserved at the ICRISAT genebank

The genebank at ICRISAT conserves the largest collection of 23,092 pearl millet germplasm accessions originating in 52 countries. A total of 15,979 landraces originating in 34 countries and having geographic coordinates of the collection sites were selected to investigate the geographical distribution of pearl millet traits and diversity in the collection. Results revealed adaptation of pearl millet to latitudes ranging between 33.00°S and 36.91°N. Landraces with early flowering (33–40 days) were predominant in Pakistan, Ghana, Togo and India; with very late flowering (121–159 days) in Sierra Leone and the Central African Republic; with short plant height (80–100 cm) in India, Zambia and Sudan; with tallness (401–490 cm) in Chad, Burkina Faso, Nigeria and the Central African Republic; with high tillering (11–35) in India and Yemen; with high panicle exsertion (11–29 cm) in Ghana, Chad, India and Yemen; with long panicles (75–135 cm) in Nigeria and Niger; with thick panicles (41–58 mm) in Namibia, Togo and Zimbabwe and those with large seeds (16–19 g 1000 seeds−1) were predominant in Togo, Benin, Ghana and Burkina Faso. Collections from Ghana for flowering (36–150 days), Burkina Faso for plant height (80–490), India and Yemen for total (1–35) and productive (1–19) tillers per plant, Niger for panicle exsertion (−45 to 21.0), panicle length (9–135 cm) and thickness (12–55 mm) and Zimbabwe for 1000 seed weight (3.5–19.3 g), were found as important sources for trait diversity. Launching collection missions for trait-specific germplasm is suggested to enrich the world collection of pearl millet at ICRISAT genebank for diversity

Drivers and major changes in agricultural production systems in drylands of South Asia: assessing implications for key environmental indicators and research needs

The South Asian dryland (arid and semi-arid) ecosystems have been exhibiting considerable agricultural production system changes. In fact, today, there are scientific consensus that this nature of agricultural production system enables it to capture market, technologies and environmental opportunities. Pressing concerns are, however, adverse environmental trade-offs that these changes are experiencing and therefore the challenges toward a resilient agricultural production system. This is particularly important in arid and semi-arid ecosystems which are resources constrained and thus more vulnerable: for example to climate change. To stimulate and revive a debate in agricultural research circles, this paper demonstrates the magnitude of major changes, their drivers and environmental implications in context to agricultural production systems in drylands of South Asia. As an example we selected districts representing different dryland agricultural production systems in western Rajasthan, Andhra Pradesh and Karnataka states of India. Taking crop, livestock and trees as major enterprises, we characterized agricultural production systems of the sample districts. Key operational resources, demographic and external agents were illustrated as examples of drivers of changes. Then major emphasis was given to material and environment related livelihood outcomes and their dynamic as agricultural production systems evolve over time. Despite a remarkable improvement in material outcomes of agricultural production (> 100% increase in cereal grain yields taking 1966 as a base year), the long term environmental dimension tends to be compromised by short term needs: as demonstrated by perpetual soil nutrient stock mining, ground water depletion and instability of cereal grain yields (28-110% CV). Based on these empirical evidence, we debate as to where a system research should focus and what policy circles need to do to address emerging problems and contribute to advances toward a sustainable agricultural production systems in dryland

Assessment of Cropland Changes Due to New Canals in Vientiane Prefecture of Laos using Earth Observation Data

The lower catchment area of a Mak Hiao river system is vulnerable to flash floods and water stress. So it is important to construct irrigation structures in this area to minimize floods during the rainy season and store water for the winter season. The Asian Development Bank (ADB) has been supporting the Government of Laos in constructing such small reservoirs like Donkhuay schemes 1 & 2, Mak Hiao, Nalong 3 and Sang Houabor projects in lower catchment areas. Our study evaluated the impacts of small irrigation schemes in terms of land-use/landcover (LULC), crop intensity, and productivity changes, using high resolution satellite imagery, socioeconomic, and ground data. We analyzed the temporal cropping pattern in the Vientiane prefecture of Laos using Planet and Sentinel-2 data. On the other hand, crop intensity and cropland changes were mapped using Sentinel-2 data and spectral matching techniques (SMTs). The crop classification accuracy based on field-plot data was 88.6%. Our results show that irrigation projects in the lower catchment areas brought about significant on-site changes in terms of cropland expansion and increased crop intensity. Remarkable changes in LULC were observed especially in the command areas owing to an increase of about 300% in crop area with access to irrigation and increase of water bodies by 31%. Our study found that interventions at the level of the command area do improved on-site soil, water and environmental services. They study emphasized underline the role of land-use regulations in reducing pressure on natural land-use systems and thereby serving the major goal of up-scaling sustainable natural resource management. The study documented the vital role of small/medium irrigation projects in restoring ecosystem services such as cropping patterns and LULC conversio